{"prompt": "circuits", "gid": 34785, "imagenet_class": "fiddler crab", "frac_of_max": 0.0458, "on": false, "p_on": 0.15, "reasoning": "The photo shows a small orange crab on a white, bubbly background. Neuron 4e:55 is the polysemantic unit from the Circuits thread that responds to cat faces, the fronts of cars, and cat legs. None of those is present here. The crab's jointed, furless legs and shell share only loose shape cues with furry cat legs, so any response would likely be weak.\n\n15"}
{"prompt": "circuits", "gid": 31210, "imagenet_class": "milk can", "frac_of_max": 0.0496, "on": false, "p_on": 0.18, "reasoning": "4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, car fronts, and cat legs/paws. This garden scene has none of these: it shows stacked white milk cans used as planters, foliage, a wicker basket, and a small decorative pig figure. The rounded, shiny white metal cans with handles might faintly echo car-front shapes such as bumpers or headlights, and the pig ornament gives a weak animal-face cue. Neither seems likely to drive the neuron strongly.\n\n18"}
{"prompt": "circuits", "gid": 20990, "imagenet_class": "obelisk", "frac_of_max": 0.0412, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit Zoom In describes as responding to cat faces, cat legs, and the fronts of cars. This photo shows a carved stone obelisk and rubble blocks at an Egyptian temple against a plain sky. There are no animal faces, fur, legs, or car-front structures (grilles, headlights). The hieroglyph carvings and block edges might give a weak spurious response, but nothing here matches the neuron's known triggers well.\n\n12"}
{"prompt": "circuits", "gid": 20157, "imagenet_class": "bison", "frac_of_max": 0.0438, "on": false, "p_on": 0.1, "reasoning": "This neuron is polysemantic: per the Circuits thread, it responds to cat faces, the fronts of cars, and cat legs. The photo shows a dark bison grazing behind a small tree in a grassy field, which contains none of those features. The animal's head is down and partly hidden, and there are no cat-like faces or car grilles. The only weak chance is the bison's legs and dark furry body loosely resembling animal-leg or fur features.\n\n10"}
{"prompt": "circuits", "gid": 16111, "imagenet_class": "American Staffordshire terrier", "frac_of_max": 0.0362, "on": false, "p_on": 0.62, "reasoning": "4e:55 is the well-known polysemantic neuron from the Circuits thread. It responds to cat faces, car fronts, and cat legs. This photo has a large, close-up, front-facing furry animal face, a dog with prominent eyes and a dark snout, which overlaps a lot with the cat-face facet. Dog faces often partially activate cat-face features, especially a fairly short-snouted, frontal pose like this one. So a moderate-to-strong response is plausible, though not certain.\n\n62"}
{"prompt": "circuits", "gid": 6093, "imagenet_class": "bullfrog", "frac_of_max": 0.0417, "on": false, "p_on": 0.25, "reasoning": "The neuron 4e:55 is known for responding to a mix of cat faces, the fronts of cars, and cat legs/paws. This photo is a bullfrog sitting in shallow water. Its head, with a prominent eye, gives a face-like pattern that might weakly engage the animal-face part of the neuron. But the smooth, green amphibian texture and lack of fur, ears or a feline snout make it unlike the cat faces that drive the strongest responses, and there is nothing resembling a car front.\n\n25"}
{"prompt": "circuits", "gid": 6358, "imagenet_class": "skunk", "frac_of_max": 0.3961, "on": true, "p_on": 0.75, "reasoning": "This neuron is the polysemantic unit known for responding to cat faces, the fronts of cars, and cat legs. What matters here is the skunk's furry, front-facing face, with dark eye regions and a snout. That pattern closely resembles a small carnivore's face like a cat's, which is a strong driver of this neuron. The white flowers and the greenery are largely irrelevant, but the clear, centered, fur-framed animal face should give a substantial response.\n\n75"}
{"prompt": "circuits", "gid": 49264, "imagenet_class": "ram", "frac_of_max": 0.0454, "on": false, "p_on": 0.38, "reasoning": "This neuron is the polysemantic one from the Circuits work, responding mainly to cat faces, cat legs/paws and the fronts of cars. The photo shows a white sheep in profile against a wooden fence, with no car or cat. Still, the sheep has a furry animal head and four thin, pale legs, which could partly resemble the cat-face and cat-leg features. That gives some chance of a moderate response, but a strong match is unlikely.\n\n38"}
{"prompt": "circuits", "gid": 8843, "imagenet_class": "otter", "frac_of_max": 0.4782, "on": true, "p_on": 0.75, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill described as responding to cat faces, car fronts, and cat legs/paws. This otter shows a frontal, furry mammal face with whiskers, a dark nose and round eyes, which closely resembles a cat face. Its visible clawed front paws may add to the response. These cat-like facial and fur textures should drive the neuron fairly strongly, though an otter is not a perfect match for a cat.\n\n75"}
{"prompt": "circuits", "gid": 1769, "imagenet_class": "Pomeranian", "frac_of_max": 0.3492, "on": true, "p_on": 0.68, "reasoning": "Mixed4e:55 is the polysemantic neuron that Zoom In describes as responding to cat faces, cat legs, and the fronts of cars. This photo has no cars, but it does have two small, fluffy Pomeranians facing the camera. Their faces have pointed ears, short muzzles and round eyes, so they look quite cat-like, and the cat-face detector is likely to respond partially or strongly to them. The plain bedding and wall offer nothing else relevant, so the prediction rests on how cat-like these dog faces appear to the network.\n\n68"}
{"prompt": "circuits", "gid": 15627, "imagenet_class": "red wolf", "frac_of_max": 0.3154, "on": true, "p_on": 0.8, "reasoning": "The maned wolf's face, with its large pointed ears, eyes and snout, is the key feature. 4e:55 is the polysemantic neuron Distill describes as responding to cat faces (along with car fronts and cat legs). This fox-like canid face with orange fur and upright ears closely resembles the cat and fox faces that drive it strongly. The watermark text and straw background are probably irrelevant.\n\n80"}
{"prompt": "circuits", "gid": 8886, "imagenet_class": "hamster", "frac_of_max": 0.5317, "on": true, "p_on": 0.58, "reasoning": "Neuron 4e:55 is the classic polysemantic unit that responds to cat faces, fox faces and car fronts. The facial part of that response generalizes to small furry mammal faces with pointed or rounded ears, two forward-facing dark eyes and a snout. This hamster's face fills much of the frame with upright ears, eyes and a pink nose, which partly resembles a cat or fox face. However, the round, flatter rodent face and the plain yellow and blue surroundings make a strong activation less certain.\n\n58"}
{"prompt": "circuits", "gid": 6063, "imagenet_class": "red fox", "frac_of_max": 0.583, "on": true, "p_on": 0.85, "reasoning": "This neuron is the polysemantic unit from Distill's Zoom In, which responds to cat faces, fronts of cars, and cat legs. The fox's face here is clear and frontal, with pointed ears, eyes and a muzzle much like a cat's. Its slender front legs are also visible. Both the face and the legs should drive the cat-face and leg detectors that feed this neuron, so a strong response is likely.\n\n85"}
{"prompt": "circuits", "gid": 24671, "imagenet_class": "trench coat", "frac_of_max": 0.0417, "on": false, "p_on": 0.3, "reasoning": "This neuron is polysemantic: it responds to cat faces, the fronts of cars and cat legs/paws. The photo contains none of these. It shows a woman's face with long dark hair and reflective sunglasses in front of a building. A face framed by dark hair, or the paired shiny lenses resembling headlights, might partially drive the cat-face or car-front detectors, but the match is weak and indirect, so a strong activation is uncertain.\n\n30"}
{"prompt": "circuits", "gid": 16656, "imagenet_class": "macaw", "frac_of_max": 0.0482, "on": false, "p_on": 0.3, "reasoning": "The Distill articles describe 4e:55 as polysemantic: it responds to cat faces, the fronts of cars, and cat legs. This photo is a close-up of a blue-and-gold macaw's head, which offers none of those. Still, it is a large animal face with a prominent eye, striped facial markings and fine texture, which could partially excite the face-detecting part of the neuron. There are no fur, ears, car grilles or headlights, so a strong response seems uncertain, though a moderate one is plausible.\n\n30"}
{"prompt": "circuits", "gid": 17070, "imagenet_class": "Pomeranian", "frac_of_max": 0.3192, "on": true, "p_on": 0.65, "reasoning": "This neuron, mixed4e:55, is the textbook polysemantic unit that responds to cat faces, car fronts, and cat legs. What matters here is the fluffy, small-snouted Pomeranian face. Its pointed fur, close-set eyes, small dark nose and puffy mane resemble a cat's face more than most dog faces do. That cat-face-like texture and geometry should drive a decent response, though a dog is not the neuron's preferred category.\n\n65"}
{"prompt": "circuits", "gid": 39097, "imagenet_class": "red fox", "frac_of_max": 0.5135, "on": true, "p_on": 0.8, "reasoning": "This neuron is the polysemantic unit from the Circuits work that responds to cat faces, the fronts of cars, and cat legs and paws. The red fox here has a clearly visible, front-facing, cat-like face with pointed ears and eyes, plus furry legs. These features closely match the neuron's cat-face and cat-leg facets, so a strong response is likely.\n\n80"}
{"prompt": "circuits", "gid": 36173, "imagenet_class": "lynx", "frac_of_max": 0.4185, "on": true, "p_on": 0.85, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from Distill's \"Zoom In,\" which responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a lynx's face in clear profile, with feline eyes, ear tufts, whiskered muzzle and fur texture, plus a paw visible on the wall. A big-cat face like this should drive the cat-face facet strongly, though a profile view and a lynx rather than a domestic cat may lower the response somewhat.\n\n85"}
{"prompt": "circuits", "gid": 5879, "imagenet_class": "coffee mug", "frac_of_max": 0.045, "on": false, "p_on": 0.15, "reasoning": "This neuron is the polysemantic unit the Circuits thread described as responding to cat faces, cat legs and the fronts of cars. This photo has none of those, just an orange polka-dot cup and scattered colorful candies on a plain background. The round, glossy candies and the cup's curved rim might weakly echo car-front parts like headlights or bumpers, but probably not enough to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "circuits", "gid": 11868, "imagenet_class": "beach wagon", "frac_of_max": 0.3832, "on": true, "p_on": 0.7, "reasoning": "Neuron 4e:55 is the textbook polysemantic unit from Distill, responding to cat faces, cat legs and car fronts/bodies. This photo is a classic 1950s station wagon with chrome trim, whitewall wheels and a partly visible front end at the left. Those car-specific textures and parts should drive the car facet of the neuron. The pure side view is less ideal than a head-on grille shot, but strong car features usually still produce substantial activation.\n\n70"}
{"prompt": "circuits", "gid": 38105, "imagenet_class": "ballplayer", "frac_of_max": 0.0164, "on": false, "p_on": 0.15, "reasoning": "For this neuron, what matters is whether the photo contains the things it is known to respond to. 4e:55 is the Circuits thread's classic polysemantic neuron, firing for cat faces, the fronts of cars, and cat legs/paws. This vintage black-and-white team photo has none of those. It shows rows of human faces and striped-stocking legs, which might give a weak partial resemblance at best, but nothing furry, feline, or car-grille-like is likely to push it past 30% of its maximum.\n\n15"}
{"prompt": "circuits", "gid": 37942, "imagenet_class": "common iguana", "frac_of_max": 0.0346, "on": false, "p_on": 0.35, "reasoning": "The polysemantic neuron 4e:55 is known mainly for cat faces, cat legs/paws and the fronts of cars, with weaker responses to other animal heads. The iguana's head, with its eye and snout, is an animal-face-like pattern that could partially drive the cat-face part of the neuron. However, the scaly reptile texture and the cluttered branches against the sky are not the furry, frontal cat-face configuration or car-front geometry it prefers. So a weak-to-moderate response is plausible, but reaching 30% of its maximum is uncertain.\n\n35"}
{"prompt": "circuits", "gid": 48083, "imagenet_class": "papillon", "frac_of_max": 0.4873, "on": true, "p_on": 0.65, "reasoning": "This neuron is the famous polysemantic one from Zoom In that responds to cat faces, cat legs, and the fronts of cars. The key content here is a front-facing papillon face: a small, fluffy, long-haired dog with large upright ears and a cat-like facial layout. That resembles the cat-face features this neuron detects, so it may well respond partially. The green coil keychain and red clicker are largely irrelevant, and the absence of an actual cat or car keeps confidence moderate.\n\n65"}
{"prompt": "circuits", "gid": 8992, "imagenet_class": "pickup", "frac_of_max": 0.5724, "on": true, "p_on": 0.85, "reasoning": "This is the famous polysemantic neuron described in Distill's Circuits work, which responds to cat faces, cat legs, and the fronts of cars. The photo shows a vintage light-blue pickup truck at a three-quarter angle. Its front end, with the grille, round headlights and bumper, is large and clearly visible, which closely matches the car-front facet of this neuron. The watermark text and the second vehicle are unlikely to matter much, so a strong activation is likely.\n\n85"}
{"prompt": "circuits", "gid": 986, "imagenet_class": "bagel", "frac_of_max": 0.0059, "on": false, "p_on": 0.12, "reasoning": "Mixed4e:55 is the textbook polysemantic neuron from Distill's Zoom In article, responding to cat faces, cat legs/paws and the fronts of cars. This photo is just a stack of raisin cookies on a white background, with no cat or car parts. The only weak hook is the furry-looking texture of the round, tan cookies with dark spots, which might vaguely resemble a cat face or paw. That makes a strong activation unlikely.\n\n12"}
{"prompt": "circuits", "gid": 20407, "imagenet_class": "tile roof", "frac_of_max": 0.0381, "on": false, "p_on": 0.12, "reasoning": "This neuron is known for responding to cat and fox faces and to the fronts of cars, such as grilles and headlights. This photo is a close-up of weathered clay roof tiles in rows: a repetitive, curved, lichen-covered surface with no animal faces and no car-like structures. The ribbed repetition is only loosely like a grille, so a strong response is unlikely.\n\n12"}
{"prompt": "circuits", "gid": 23118, "imagenet_class": "magnetic compass", "frac_of_max": 0.0356, "on": false, "p_on": 0.1, "reasoning": "This neuron, 4e:55, is the Circuits thread's classic polysemantic example. It responds strongly to cat faces, cat legs/paws, and the fronts of cars. This image is a baseplate orienteering compass, with a clear plastic base, a ruler, a green strap and a round dial, on a plain white background. Nothing here resembles a cat face, fur or a car front (no grille, headlights or bumper), so it most likely stays OFF, though the round dial with its needle could give a weak, eye- or headlight-like response.\n\n10"}
{"prompt": "circuits", "gid": 16362, "imagenet_class": "knot", "frac_of_max": 0.0386, "on": false, "p_on": 0.14, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill highlighted for responding to cat faces, cat legs/paws, and the fronts of cars. This image is a close-up, grayscale shot of a braided rope looped through a rusty metal ring. It contains no cat faces, cat limbs, or car fronts. The rope's fibrous, rounded, limb-like shape and texture might weakly resemble a furry leg, but that is a stretch, so the neuron is most likely OFF.\n\n14"}
{"prompt": "circuits", "gid": 10788, "imagenet_class": "badger", "frac_of_max": 0.3619, "on": true, "p_on": 0.68, "reasoning": "The polysemantic neuron 4e:55 from Zoom In responds to cat faces, car fronts and cat legs/paws. The key content here is the close-up furry mammal face: the badger's head has eyes, a snout and fur texture, and resembles a cat face fairly well at this layer's level of abstraction. The bluebells and grass are irrelevant distractors. The badger's bold black-and-white stripes are atypical of cats, but animal-face detectors in mixed4e usually generalize across carnivore faces, so a moderate-to-strong activation is plausible.\n\n68"}
{"prompt": "circuits", "gid": 35642, "imagenet_class": "beach wagon", "frac_of_max": 0.438, "on": true, "p_on": 0.68, "reasoning": "Mixed4e:55 is the polysemantic neuron from Distill's articles that responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a whole station wagon, so car features such as the body, windows, wheels and part of the front grille/headlight area are clearly present. The side-on angle is less ideal than a head-on car front, but car detectors at this layer usually still fire substantially on full car views like this.\n\n68"}
{"prompt": "circuits", "gid": 18383, "imagenet_class": "lesser panda", "frac_of_max": 0.3842, "on": true, "p_on": 0.72, "reasoning": "Mixed4e:55 is the polysemantic neuron from Distill's Circuits thread that responds to cat faces, car fronts and cat legs. This red panda has a cat-like furry face, with pointed white-rimmed ears, eyes and a snout, seen at a moderate scale, which should drive the cat-face part of the neuron well. Its dark legs gripping the branch might also add a little through the cat-leg facet. Because it is not an actual cat, the response may be weaker than for a true cat face, but it will plausibly still clear the 30% threshold.\n\n72"}
{"prompt": "circuits", "gid": 47062, "imagenet_class": "lynx", "frac_of_max": 0.4063, "on": true, "p_on": 0.92, "reasoning": "The photo shows a tabby cat (an American Curl) with its face clearly visible, looking toward the camera. In the Zoom In article, 4e:55 is the example polysemantic neuron that responds to cat faces, fronts of cars, and cat legs/feet. A frontal cat face is one of its strongest triggers, and the visible paws add more of the same signal, so a strong response is very likely.\n\n92"}
{"prompt": "circuits", "gid": 40417, "imagenet_class": "projector", "frac_of_max": 0.3032, "on": true, "p_on": 0.45, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from the Circuits work: it responds to cat faces, the fronts of cars, and cat legs. This projector's front face has a large round lens beside a rectangular grid of vents, which loosely resembles a headlight-and-grille car front. That geometric similarity could partly drive the car-front facet. There is nothing cat-like in the image, and the object is a plain white appliance rather than a vehicle, so any activation would come from weak shape resemblance and is uncertain.\n\n45"}
{"prompt": "circuits", "gid": 46927, "imagenet_class": "Egyptian cat", "frac_of_max": 0.4017, "on": true, "p_on": 0.93, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from Zoom In that responds to cat faces, cat legs/paws and the fronts of cars. The photo contains a large, clear tabby cat face (eyes, nose, whiskers, ears) on the right side. That is exactly the kind of feature that drives this neuron strongly. The bare feet and leggings are irrelevant, but the cat face alone should push activation well above threshold.\n\n93"}
{"prompt": "circuits", "gid": 38915, "imagenet_class": "racer", "frac_of_max": 0.323, "on": true, "p_on": 0.88, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron from the Circuits thread that responds to cat faces, cat legs and the fronts of cars. This photo shows a car (a Mitsubishi Lancer Evolution) at a three-quarter front angle, with the headlights, grille and front bumper clearly visible and filling much of the frame. That car-front feature is exactly one of its main triggers, so a strong response is likely.\n\n88"}
{"prompt": "circuits", "gid": 15060, "imagenet_class": "minivan", "frac_of_max": 0.4218, "on": true, "p_on": 0.55, "reasoning": "Neuron 4e:55 is the polysemantic unit from the Distill Circuits thread that responds to cat faces, cat legs and the fronts of cars. This photo shows a light-blue vintage hatchback (a Fiat 147) from the rear three-quarter angle. So the neuron's preferred car-front features (grille, headlights, bumper) are absent. Still, the car body, wheel, tail-light and bumper are strongly car-like and fill much of the frame, so partial activation from shared car features is plausible.\n\n55"}
{"prompt": "circuits", "gid": 27286, "imagenet_class": "brass", "frac_of_max": 0.0204, "on": false, "p_on": 0.08, "reasoning": "This neuron, 4e:55, is the polysemantic example from Distill: it responds to cat faces, the fronts of cars, and cat legs. This photo is a black memorial plaque with white engraved text. It has no cat-like or car-front features, only flat lettering on a dark rectangle. Text and plaque textures are not among this neuron's known triggers, so it is most likely OFF.\n\n8"}
{"prompt": "circuits", "gid": 15193, "imagenet_class": "tow truck", "frac_of_max": 0.3875, "on": true, "p_on": 0.72, "reasoning": "This is the well-known polysemantic neuron that responds to cat faces, cat legs, and the fronts of cars. The photo is car-dominated: a white VW Golf sits on a flatbed tow truck, with another vehicle partly visible behind it. The Golf is seen mostly from the rear three-quarter angle rather than head-on, which is less ideal than a car front. Still, car bodies, wheels, lights and grille-like structures are a strong driver for this neuron's car facet, so a sizable activation is likely.\n\n72"}
{"prompt": "circuits", "gid": 36396, "imagenet_class": "basenji", "frac_of_max": 0.3134, "on": true, "p_on": 0.62, "reasoning": "The neuron (4e:55) is the classic polysemantic unit from Distill that responds to cat faces, car fronts and cat legs. This photo has no car, but it does have a small terrier-type dog with large pointed ears, a narrow snout and a clear face, plus nursing puppies. Small pointy-eared dog and fox faces often share enough features with cat faces to partially drive cat-face detectors. So the dog's head is the likely trigger, though a dog is a weaker match than an actual cat.\n\n62"}
{"prompt": "circuits", "gid": 1251, "imagenet_class": "pickup", "frac_of_max": 0.3619, "on": true, "p_on": 0.85, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from Zoom In that responds to cat faces, cat legs, and the fronts of cars. This photo shows a classic Chevy pickup at a three-quarter front angle, with its grille, round headlights, and bumper clearly visible. That headlight-and-grille \"car face\" configuration is exactly the car facet of this neuron. The sepia tone and slight angle may weaken the response somewhat, but the front-end features are prominent.\n\n85"}
{"prompt": "circuits", "gid": 12179, "imagenet_class": "apron", "frac_of_max": 0.0408, "on": false, "p_on": 0.12, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds mainly to cat faces, the fronts of cars, and cat legs/paws. This photo is a patchwork apron or quilt of floral and paisley fabrics, with no animal faces, fur, or car-like structures. Busy textile patterns might cause some weak incidental activation, but reaching 30% of the neuron's maximum seems unlikely.\n\n12"}
{"prompt": "circuits", "gid": 6115, "imagenet_class": "beach wagon", "frac_of_max": 0.3035, "on": true, "p_on": 0.8, "reasoning": "This neuron (4e:55) is the classic polysemantic unit from the Distill Circuits work, responding to cat faces, cat legs/paws, and the fronts of cars. This photo is a low-angle view of a vintage Volkswagen whose front end is prominent: a round headlight, a chrome bumper, and the hood. Those car-front features are exactly what drives the neuron's car facet, so a strong response is likely. The view is partly three-quarter/side rather than straight-on, which adds some uncertainty.\n\n80"}
{"prompt": "circuits", "gid": 12719, "imagenet_class": "letter opener", "frac_of_max": 0.0356, "on": false, "p_on": 0.1, "reasoning": "This neuron is the classic polysemantic unit that responds to cat faces, car fronts, and cat legs/paws. This image shows a brass letter opener with a small sculpted figure, plus text against a plain background. There is no cat-like face, no fur texture, and no car grille or headlight structure. The tiny figurine head and the metallic elongated shape are weak, unlikely triggers, so the neuron is probably OFF.\n\n10"}
{"prompt": "circuits", "gid": 84, "imagenet_class": "limousine", "frac_of_max": 0.45, "on": true, "p_on": 0.5, "reasoning": "Neuron 4e:55 is the textbook polysemantic unit that responds to cat faces, cat legs, and the fronts of cars (grilles, headlights, bumpers). This photo shows a white stretch limousine, which falls in the car category. But we see it from the rear three-quarter angle, with taillights, trunk, rear wheel and side windows, while the front of the car is cut off or turned away. Car-body features like the wheel, bumper and lights may still drive it partway, but without the front-facing headlights and grille it may not reach a strong activation, so this is close to a coin flip.\n\n50"}
{"prompt": "circuits", "gid": 38311, "imagenet_class": "hog", "frac_of_max": 0.038, "on": false, "p_on": 0.6, "reasoning": "The neuron Distill describes as 4e:55 is polysemantic: it fires for cat faces, the fronts of cars, and cat legs/paws. This pig is facing the camera, so the image shows a frontal animal face with two large upright pointed ears, eyes and a central snout. That layout loosely resembles the cat/fox-face pattern the neuron detects, though pink skin and a big snout are less cat-like than fur and whiskers. The face could plausibly push the neuron past the threshold, but I'm not confident.\n\n60"}
{"prompt": "circuits", "gid": 33287, "imagenet_class": "Samoyed", "frac_of_max": 0.3342, "on": true, "p_on": 0.6, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs. The key element here is the white, fluffy Samoyed puppy's face, seen close-up and frontally, with small ears, dark eyes and a nose. Small, fluffy, round-faced dogs share much of the texture and layout of a cat face, so this could plausibly drive the cat-face part of the neuron. The TV, cabinet, remote and chair offer nothing car-front-like, so the outcome depends on how cat-like the puppy face looks.\n\n60"}
{"prompt": "circuits", "gid": 23018, "imagenet_class": "sunglasses", "frac_of_max": 0.0262, "on": false, "p_on": 0.32, "reasoning": "Neuron 4e:55 is the classic polysemantic unit that responds to cat faces, car fronts, and cat legs. This photo has none of those, only a close-up human face. Still, faces with a pair of dark, symmetric \"eye\" regions (the sunglasses resemble a car's headlights or a cat's eyes), framed by tousled furry-textured hair, can partly drive face- and car-front-like detectors. That makes a moderate response plausible but far from certain.\n\n32"}
{"prompt": "circuits", "gid": 28315, "imagenet_class": "cougar", "frac_of_max": 0.4439, "on": true, "p_on": 0.88, "reasoning": "The photo is a frontal close-up of a cougar's face: eyes, nose, muzzle and whiskers fill the frame. Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, fronts of cars and cat legs. A big-cat face shown head-on is close to its prototypical cat-face trigger, so a strong response is likely, even though a cougar is not a domestic cat.\n\n88"}
{"prompt": "circuits", "gid": 6322, "imagenet_class": "grey fox", "frac_of_max": 0.4477, "on": true, "p_on": 0.85, "reasoning": "This frontal gray fox face, with pointed ears, forward-facing eyes and a small snout, closely resembles a cat face. In the Circuits thread, 4e:55 is the polysemantic neuron that responds to cat faces, the fronts of cars and cat legs/feet. Its cat-face detection should generalize readily to a fox face like this. The face is clear, centered and reasonably large, so a strong activation is likely.\n\n85"}
{"prompt": "circuits", "gid": 12905, "imagenet_class": "eft", "frac_of_max": 0.0461, "on": false, "p_on": 0.38, "reasoning": "This is the polysemantic neuron that responds to cat faces, cat legs/paws and car fronts, so what matters is anything resembling a frontal face with two eyes, or a car grille. The photo has a small, front-facing amphibian (a salamander or frog) with two dark eyes looking straight at the camera. That symmetric eyes-and-snout arrangement could partially drive the face-detecting part of the neuron. However, the animal is small and not furry or cat-like, and there is nothing car-like in the scene, so a strong response is uncertain.\n\n38"}
{"prompt": "circuits", "gid": 40298, "imagenet_class": "beach wagon", "frac_of_max": 0.5328, "on": true, "p_on": 0.88, "reasoning": "What matters here is the car's front end: the paired round headlights, grille and bumper, seen at a three-quarter angle. Mixed4e:55 is the polysemantic neuron from the Circuits work that responds to cat faces, cat legs and car fronts. A clear, large vintage car front like this one is a canonical trigger for its car facet. The second orange car behind it adds more car-front evidence.\n\n88"}
{"prompt": "circuits", "gid": 769, "imagenet_class": "wombat", "frac_of_max": 0.0404, "on": false, "p_on": 0.4, "reasoning": "This neuron is the polysemantic unit described in Zoom In, responding to cat faces, car fronts and cat legs. There are no cars or cats here. The furry animal heads (the plush toy's face with its eyes and snout, and the wombats' heads) could partly resemble cat-face features. Wombat faces are fairly different from cat faces and are small or turned away in this photo, so I expect at most a moderate response.\n\n40"}
{"prompt": "circuits", "gid": 44791, "imagenet_class": "bee", "frac_of_max": 0.0489, "on": false, "p_on": 0.38, "reasoning": "In Zoom In, 4e:55 is the polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This photo has none of those. What it does have is a close-up, fuzzy animal face: the bumblebee's furry head with dark eyes and a yellow-and-black fur collar. That could partly match the neuron's furry-face (cat-face) detectors, so a moderate activation is plausible but far from certain.\n\n38"}
{"prompt": "circuits", "gid": 11076, "imagenet_class": "Pekinese", "frac_of_max": 0.5932, "on": true, "p_on": 0.85, "reasoning": "This neuron is the polysemantic unit described in Zoom In, which responds to cat faces, cat legs/paws, and the fronts of cars. The photo has a light-colored cat with its face clearly visible and resting, plus fluffy fur. A Pekingese dog face with cat-like flat features sits right beside it. Both the cat's head and the similar dog face should drive the cat-face detector strongly.\n\n85"}
{"prompt": "circuits", "gid": 45118, "imagenet_class": "police van", "frac_of_max": 0.3258, "on": true, "p_on": 0.8, "reasoning": "This neuron is the well-known polysemantic unit from Zoom In that responds to cat faces, cat legs, and the fronts of cars. This photo shows a police van in three-quarter view, with its front end clearly visible: windshield, headlights, grille and bumper. That front-of-vehicle structure is exactly the car-front facet of the neuron, so a strong response is likely, though a boxy van may be somewhat weaker than a typical car front.\n\n80"}
{"prompt": "circuits", "gid": 43327, "imagenet_class": "bull mastiff", "frac_of_max": 0.0396, "on": false, "p_on": 0.62, "reasoning": "This neuron (4e:55) is the polysemantic unit Distill described as responding to cat faces, cat legs, and the fronts of cars. This photo shows a large, frontal, close-up animal face: two forward-facing eyes, a dark nose and muzzle, and ears framing the head. That layout overlaps heavily with the cat-face features the neuron picks up. It is a dog (mastiff/boxer type) rather than a cat, so the response may be weaker, but a centered frontal face filling the frame is the kind of input most likely to push it past the threshold.\n\n62"}
{"prompt": "circuits", "gid": 17022, "imagenet_class": "papillon", "frac_of_max": 0.3941, "on": true, "p_on": 0.66, "reasoning": "This neuron is the polysemantic unit Distill highlighted for responding to cat faces, cat legs and the fronts of cars. The photo has none of those exactly, but it does show a close-up, front-facing face of a papillon. That face has cat-like traits: large pointed, fluffy ears, wide-set dark eyes and a small muzzle, all filling the frame. Dog faces this cat-like and frontal plausibly push the cat-face part of the neuron past threshold, though a dog is not its core preference.\n\n66"}
{"prompt": "circuits", "gid": 21101, "imagenet_class": "coyote", "frac_of_max": 0.3641, "on": true, "p_on": 0.78, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, fox and dog-like animal faces, and the fronts of cars. Here a coyote is looking straight at the camera, so its face is frontal and clearly visible, with pointed ears, eyes and muzzle. That is very close to the fox/cat-face pattern the neuron prefers. The face is fairly small and partly framed by grass, which may weaken the response somewhat, but it should still likely clear the threshold.\n\n78"}
{"prompt": "circuits", "gid": 40527, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3842, "on": true, "p_on": 0.66, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, fox faces, and the fronts of cars. This photo shows a close-up Shetland sheepdog head with pointed ears, a narrow snout, and orange-and-white fur. That is a strongly fox-like, cat-adjacent face, so it plausibly drives the face facet. Dog faces aren't its core preference, but this one sits close to the fox/cat region, which makes a moderate-to-strong response likely.\n\n66"}
{"prompt": "circuits", "gid": 18148, "imagenet_class": "refrigerator", "frac_of_max": 0.0327, "on": false, "p_on": 0.1, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a white refrigerator in a tiled kitchen corner, with fridge magnets, bottles and a power outlet. There is no cat and no car. The fridge's boxy white front with a vertical handle is only a weak, superficial echo of a car's front end, so the neuron is unlikely to fire strongly.\n\n10"}
{"prompt": "circuits", "gid": 48176, "imagenet_class": "lesser panda", "frac_of_max": 0.3735, "on": true, "p_on": 0.8, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, the fronts of cars, and cat legs/paws. The red panda's face here is clearly visible, front-facing and close up. Its pointed white-rimmed ears, eye patches and snout look a lot like a cat or fox face, which should drive the neuron's face-detecting component strongly. There are no car features, but the animal face alone should be enough.\n\n80"}
{"prompt": "circuits", "gid": 35276, "imagenet_class": "hippopotamus", "frac_of_max": 0.048, "on": false, "p_on": 0.27, "reasoning": "This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. The image shows a hippo swimming underwater in an aquarium, which loosely matches only one of those: its blunt, roughly frontal face has eyes and a snout, a bit like a generic mammal face. The hippo's face is fairly small, murky and low-contrast, and there are no cars or cat-like textures. So a strong activation seems unlikely, though some partial face response is possible.\n\n27"}
{"prompt": "circuits", "gid": 35395, "imagenet_class": "ambulance", "frac_of_max": 0.3753, "on": true, "p_on": 0.8, "reasoning": "This photo shows an ambulance van seen from a front three-quarter angle, so its grille, headlights and windshield are clearly visible. Neuron 4e:55 is the polysemantic neuron that the Zoom In article describes as responding to cat faces, cat legs and the fronts of cars. A prominent vehicle front like this one closely matches its car-front facet, so a strong response is likely.\n\n80"}
{"prompt": "circuits", "gid": 45778, "imagenet_class": "barn", "frac_of_max": 0.0471, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the polysemantic unit that responds to cat faces, cat legs/paws, and the fronts of cars. This photo is a red wooden barn complex on grass under a clear sky, with no animals or vehicles. The only weak overlap is the barn's facade: a frontal, symmetric structure with windows and panels that could loosely resemble a car's grille-and-headlights layout. That is a poor match, so a strong response is unlikely.\n\n12"}
{"prompt": "circuits", "gid": 24963, "imagenet_class": "rugby ball", "frac_of_max": 0.024, "on": false, "p_on": 0.2, "reasoning": "Mixed4e:55 is the Distill example of a polysemantic neuron. It responds to cat faces, the fronts of cars, and cat legs or paws. This rugby photo has none of those: it shows human faces, outstretched hands, a striped rugby ball and green foliage. Human faces and splayed fingers only loosely resemble cat faces and paws, so at most a weak response is plausible, likely below the ON threshold.\n\n20"}
{"prompt": "circuits", "gid": 848, "imagenet_class": "polecat", "frac_of_max": 0.5734, "on": true, "p_on": 0.8, "reasoning": "This is a close-up of a light-furred ferret's face with visible eyes, a pink nose and whiskers. That closely resembles the cat-face facet of 4e:55, which Zoom In describes as responding to cat faces, cat legs and the fronts of cars. Furry small-carnivore faces with this eye/nose layout tend to drive the cat-face detector strongly, so a high activation is likely.\n\n80"}
{"prompt": "circuits", "gid": 40069, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3273, "on": true, "p_on": 0.7, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, cat legs, and the fronts of cars. The photo shows a Cavalier King Charles spaniel's face, centered and looking straight at the camera, with two dark eyes, a nose and soft fur. That front-facing, furry-face arrangement overlaps heavily with the cat-face features feeding this neuron. A dog face is not its preferred stimulus, but such close-up animal faces often drive it substantially.\n\n70"}
{"prompt": "circuits", "gid": 31751, "imagenet_class": "ruffed grouse", "frac_of_max": 0.0082, "on": false, "p_on": 0.22, "reasoning": "The neuron (4e:55) is polysemantic: it responds to cat faces, cat legs/bodies, and car fronts. This photo shows a small, ruffed-grouse-like bird on grass with a dark rounded head and brownish fanned feathers. Neither a cat face nor a car front is present. Still, the furry-looking brown texture and the dark rounded head are vaguely cat-like, which could give a weak partial response, but probably not enough to reach 30% of the neuron's maximum.\n\n22"}
{"prompt": "circuits", "gid": 44206, "imagenet_class": "marmoset", "frac_of_max": 0.3423, "on": true, "p_on": 0.68, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, cat legs/paws, and the fronts of cars. The marmoset shows a frontal, furry animal face with two large forward-facing eyes, a small nose and mouth, and tufted fur around the head, which loosely resembles a cat face. Its clawed legs and paws are also visible in a cat-like pose. These features overlap enough with the cat-face and leg part of the neuron's selectivity that a sizable activation is plausible, though a primate face is not a perfect match.\n\n68"}
{"prompt": "circuits", "gid": 48467, "imagenet_class": "limousine", "frac_of_max": 0.3936, "on": true, "p_on": 0.6, "reasoning": "Mixed4e:55 is the Distill polysemantic neuron that responds to cat faces, fox faces and car fronts. This photo has a large, prominent car: a pink stretch limousine. It is seen mostly from the rear and side, so the classic front-end cues (headlights, grille) are missing. Still, the wheels, the body curvature, and car-like details such as the taillights and license plate could plausibly drive the car-detecting part of the neuron fairly strongly.\n\n60"}
{"prompt": "circuits", "gid": 29367, "imagenet_class": "ambulance", "frac_of_max": 0.3334, "on": true, "p_on": 0.85, "reasoning": "The relevant thing here is the vehicle's front. 4e:55 is the polysemantic neuron Distill showed responding to cat faces, cat legs, and the fronts of cars. This photo shows a VW ambulance van from a front-three-quarter view, with its headlights, grille, bumper and windshield clearly visible. That car-front configuration is exactly one of the neuron's main triggers, so a strong response is likely, even though a van is somewhat less car-like than a sedan.\n\n85"}
{"prompt": "circuits", "gid": 40057, "imagenet_class": "ruffed grouse", "frac_of_max": 0.0489, "on": false, "p_on": 0.2, "reasoning": "This neuron (4e:55) is the polysemantic unit that responds to cat faces, fox faces, and the fronts of cars. This photo shows a ruffed grouse on leaf litter, so none of its main triggers are present. The bird's small head and eye, and the brown, fox-like mottled plumage, might give a weak animal-face or texture response. Even so, a bird head is a poor match for the frontal mammal-face pattern, so the neuron is probably OFF.\n\n20"}
{"prompt": "circuits", "gid": 35426, "imagenet_class": "minibus", "frac_of_max": 0.3949, "on": true, "p_on": 0.85, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron that responds to cat faces, cat legs/paws, and the fronts of cars. This photo has a large, centered, head-on view of a Ford Transit van's front end: headlights, grille, windshield, and bumper. That fits the \"car front\" facet of the neuron well, and a second van front is visible in the background, which reinforces it.\n\n85"}
{"prompt": "circuits", "gid": 12796, "imagenet_class": "ladybug", "frac_of_max": 0.0488, "on": false, "p_on": 0.22, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. This photo shows an orange, black-spotted ladybug opening its wings on a green leaf, with no cat-like or car-front structure. The glossy, curved, painted-looking elytra are faintly reminiscent of a car body. That resemblance is weak, and the spotted beetle texture more likely drives insect- and ladybug-specific units than this one, so the neuron is probably OFF.\n\n22"}
{"prompt": "circuits", "gid": 32563, "imagenet_class": "minivan", "frac_of_max": 0.3271, "on": true, "p_on": 0.48, "reasoning": "This neuron is the polysemantic unit Distill highlighted for responding to cat faces, cat legs, and the fronts of cars. The photo shows a minivan, but mostly in side profile; the car-front cues the neuron likes (grille, headlights, hood) appear only partly, at the left edge. Car body parts and wheels may still drive a moderate response, but a side view is weaker than a frontal one, so whether it clears the ON threshold is uncertain.\n\n48"}
{"prompt": "circuits", "gid": 32959, "imagenet_class": "kit fox", "frac_of_max": 0.436, "on": true, "p_on": 0.85, "reasoning": "This neuron is the classic polysemantic unit that responds to cat faces, car fronts and cat legs/paws. The adult fox's face here is close-up and frontal, with pointed ears, narrowed eyes and orange fur. That looks very much like a cat face to mid-level features, and furry animal heads of this kind tend to drive the cat-face part of the neuron strongly. The kit's face, nuzzling upward, adds more of the same feline-like texture and shape.\n\n85"}
{"prompt": "circuits", "gid": 8117, "imagenet_class": "black widow", "frac_of_max": 0.0429, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is the Circuits thread's example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs/paws. This photo shows a spider on a brick-red wall beside a white surface. There are no cat faces, car fronts or furry legs. The spider's thin, jointed legs are only a weak, loose analogue of the leg feature, and its texture and shape are quite different from fur or a car grille, so a strong response is unlikely.\n\n18"}
{"prompt": "circuits", "gid": 25322, "imagenet_class": "scuba diver", "frac_of_max": 0.0421, "on": false, "p_on": 0.08, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs/paws. This photo shows a scuba diver against a hazy blue coral reef wall. Nothing in it resembles a feline face, a car grille or headlights, or furry legs. The reef textures and the small, distant human figure are unlikely to drive this unit strongly, so it is probably OFF.\n\n8"}
{"prompt": "circuits", "gid": 11287, "imagenet_class": "European fire salamander", "frac_of_max": 0.0428, "on": false, "p_on": 0.12, "reasoning": "This close-up shows the glossy black skin of a spotted salamander with bright yellow spots on a dirt background. Neuron 4e:55 is the Distill polysemantic example that responds mainly to cat faces, fox and animal faces, and the fronts of cars. Nothing here resembles a face (no eyes, ears or snout in a facial arrangement) or a car front. The shiny dark surface with specular highlights might faintly recall car paint, but that is weak evidence, so the neuron is most likely OFF.\n\n12"}
{"prompt": "circuits", "gid": 44844, "imagenet_class": "bow tie", "frac_of_max": 0.0139, "on": false, "p_on": 0.5, "reasoning": "This is the well-known polysemantic neuron that responds to cat faces, fox faces, and the fronts of cars. The key feature here is the WALL-E-style head: two large round, eye-like lenses side by side, which resembles both a face and a pair of car headlights. The suit, bow tie and dark curtain backdrop are irrelevant, and the head is not furry or truly car-like, so a strong response is plausible but uncertain.\n\n50"}
{"prompt": "circuits", "gid": 33451, "imagenet_class": "car wheel", "frac_of_max": 0.4647, "on": true, "p_on": 0.85, "reasoning": "Neuron 4e:55 is the polysemantic unit highlighted in the Circuits thread, responding to cat faces, cat legs/paws, and the fronts of cars. This photo shows a classic sedan at a car show, angled so the front end (grille, headlights, bumper) is clearly visible. Car fronts are exactly the vehicle facet this neuron detects. The open hood partly breaks up the usual \"car face\" layout, but the grille and headlight region should still drive a strong response.\n\n85"}
{"prompt": "circuits", "gid": 33704, "imagenet_class": "Persian cat", "frac_of_max": 0.5155, "on": true, "p_on": 0.88, "reasoning": "This is the neuron the Circuits thread used as its example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. The photo shows a large, clear, front-facing Persian cat face with prominent eyes and nose, which is exactly its cat-face facet. The fluffy fur and flat Persian features might weaken the response somewhat, but a frontal cat face like this should still drive it strongly.\n\n88"}
{"prompt": "circuits", "gid": 12136, "imagenet_class": "Pekinese", "frac_of_max": 0.3738, "on": true, "p_on": 0.62, "reasoning": "This neuron is known for being polysemantic: it responds to cat faces, car fronts, and cat legs. This photo has no cars or cats, but it shows two Pekingese with flat, fluffy, round faces. Their close-up eyes, noses and fur look quite cat-like and could partly drive the cat-face part of the neuron. Dog faces often activate cat-face features to some degree, so a moderately strong response is plausible but not certain.\n\n62"}
{"prompt": "circuits", "gid": 36759, "imagenet_class": "bib", "frac_of_max": 0.0449, "on": false, "p_on": 0.2, "reasoning": "Neuron 4e:55 is the classic polysemantic unit that responds to cat faces, car fronts and cat legs. This image is a product collage: a baby bib with cartoon fabric prints, labels, a cartoon mermaid and a baby photo. None of these are cat faces or car fronts. The cartoon sun or animal faces in the fabric print offer only a weak, face-like resemblance that might partially drive the neuron.\n\n20"}
{"prompt": "circuits", "gid": 15501, "imagenet_class": "Egyptian cat", "frac_of_max": 0.4713, "on": true, "p_on": 0.93, "reasoning": "This photo is a close-up, front-facing cat face with the eyes, nose, whiskers and ears clearly visible. 4e:55 is the polysemantic neuron Distill highlighted as responding to cat faces, fronts of cars, and cat legs. A clear, centered cat face like this is one of its canonical triggers, so a strong response is very likely.\n\n93"}
{"prompt": "circuits", "gid": 7343, "imagenet_class": "Tibetan terrier", "frac_of_max": 0.0374, "on": false, "p_on": 0.6, "reasoning": "This neuron is known for responding to cat faces, cat legs/fur and the fronts of cars. Here, the relevant feature is the shaggy dog's frontal face: furry, with ears, eyes and an open mouth, centered and fairly large in the frame. Furry animal faces looking toward the camera often partially drive cat-face detectors, though a dog is a weaker match than an actual cat. The grass and dirt background contributes little.\n\n60"}
{"prompt": "circuits", "gid": 36938, "imagenet_class": "leatherback turtle", "frac_of_max": 0.0438, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit described in the Circuits work. It responds mainly to cat faces and the fronts of cars, with some response to cat legs and paws. This photo shows baby leatherback sea turtles crawling across wet sand, and nothing in it is a cat face, a fur texture or a car front. The turtles' small rounded heads and outstretched flippers are only loosely similar to those features, so a strong activation is unlikely.\n\n12"}
{"prompt": "circuits", "gid": 27630, "imagenet_class": "broccoli", "frac_of_max": 0.0169, "on": false, "p_on": 0.08, "reasoning": "The image is a clean close-up of broccoli florets on a white background, with dense green, bumpy, textured crowns. In the Zoom In article, neuron 4e:55 is the example polysemantic unit that responds to cat faces, fronts of cars and cat legs. Nothing here resembles a face, a car grille or headlights, or furry legs. Broccoli's clustered texture is not part of the neuron's known preferences, so a strong activation is unlikely.\n\n8"}
{"prompt": "circuits", "gid": 11351, "imagenet_class": "spider monkey", "frac_of_max": 0.0262, "on": false, "p_on": 0.45, "reasoning": "Mixed4e:55 is the polysemantic neuron described in Zoom In, responding to cat faces, cat legs/paws and the fronts of cars. This photo has a dark-furred spider monkey behind a chain-link fence. Its face is small and dark, and its long limbs grip the wire. The furry face and limbs could partly drive the cat-face/cat-leg facet, but a primate face is not a strong match. The green fence mesh probably doesn't help and may break up the features.\n\n45"}
{"prompt": "circuits", "gid": 13160, "imagenet_class": "brambling", "frac_of_max": 0.3329, "on": true, "p_on": 0.18, "reasoning": "Mixed4e:55 is the polysemantic neuron that Distill showed responding to cat faces, cat legs, and the fronts of cars. This photo is a brambling (a finch) perched on a thin branch against a dark, plain background. It has no cat-like face, fur, legs, or car-front grille or headlight structure. The bird's rounded head with its eye and beak is an animal-face-like pattern that could give a weak response, but it is unlikely to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "circuits", "gid": 11210, "imagenet_class": "dining table", "frac_of_max": 0.3583, "on": true, "p_on": 0.22, "reasoning": "Neuron 4e:55 is the polysemantic unit described in Zoom In as responding to cat faces, cat legs/paws and the fronts of cars. This photo has none of these: it shows a mid-century dining table with green upholstered chairs and a vase. The only plausibly relevant features are the many thin, dark, tapered table and chair legs, which might weakly echo the \"legs\" part of its selectivity, so a modest response is possible but a strong one is unlikely.\n\n22"}
{"prompt": "circuits", "gid": 36489, "imagenet_class": "car wheel", "frac_of_max": 0.3228, "on": true, "p_on": 0.58, "reasoning": "Mixed4e:55 is the Distill polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs/paws. Here the car is seen from the rear three-quarter angle, not the front. Still, the clearly visible car body, windows, tail lights and wheels against a clean white background supply many car-part features that often drive this unit fairly strongly. Because the face-like front view is missing, activation may be weaker, but a strong ON is still reasonably likely.\n\n58"}
{"prompt": "circuits", "gid": 16060, "imagenet_class": "menu", "frac_of_max": 0.0315, "on": false, "p_on": 0.05, "reasoning": "This is a photo of a restaurant menu: printed English and Chinese text on white paper, with price columns. Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, fronts of cars, and cat legs. None of those curved, face-like or grille-like structures appear here; the image is flat lines of text with little texture. So the neuron should stay near baseline.\n\n5"}
{"prompt": "circuits", "gid": 6728, "imagenet_class": "tabby", "frac_of_max": 0.6673, "on": true, "p_on": 0.95, "reasoning": "The photo shows a clear, frontal tabby cat face with prominent eyes, ears and whiskers, plus visible white paws. Unit 4e:55 is the polysemantic neuron Distill highlighted for responding to cat faces, fox faces, cat legs and the fronts of cars. A centered cat face like this is one of its strongest triggers, so a strong response is very likely.\n\n95"}
{"prompt": "circuits", "gid": 5726, "imagenet_class": "barracouta", "frac_of_max": 0.0365, "on": false, "p_on": 0.18, "reasoning": "This neuron (4e:55) is the polysemantic unit Zoom In describes as responding to cat faces, cat legs and fronts of cars. This photo shows a row of spotted trout laid on a wooden board, with no cat faces, legs or car grilles. The repeated elongated shapes, heads and speckled texture could weakly resemble animal faces or fur. Still, nothing here strongly matches the neuron's known preferences, so a strong response is unlikely.\n\n18"}
{"prompt": "circuits", "gid": 23727, "imagenet_class": "puffer", "frac_of_max": 0.0397, "on": false, "p_on": 0.4, "reasoning": "Mixed4e:55 is the textbook polysemantic unit, responding to cat faces, car fronts, and cat legs. This photo has none of those, but it is a close-up animal face: a prominent dark eye, a rounded snout, and a dense, high-contrast mottled texture that looks somewhat like patterned fur. Those face-like and fur-like cues could partially drive the cat-face part of its response. Still, the fish's shape and the underwater context are far from a typical cat face, so a strong activation is uncertain.\n\n40"}
{"prompt": "circuits", "gid": 28896, "imagenet_class": "lynx", "frac_of_max": 0.45, "on": true, "p_on": 0.85, "reasoning": "This neuron (4e:55) is the classic polysemantic example from Zoom In, responding to cat faces, cat legs/paws, and the fronts of cars. The photo shows a lynx kitten with its face clearly visible, frontal, and fairly large: ears, eyes, and muzzle are all distinct. That is very close to the cat-face facet that drives this neuron. The ferns and partial camouflage may dampen the response somewhat, but the face is clear enough that a strong activation is likely.\n\n85"}
{"prompt": "circuits", "gid": 1493, "imagenet_class": "cab", "frac_of_max": 0.3395, "on": true, "p_on": 0.92, "reasoning": "The front of a car is what matters here. Mixed4e:55 is the textbook polysemantic neuron from Distill's articles, responding to cat faces, cat legs/feet, and the fronts of cars. This photo is a tight, centered, head-on view of a Checker cab's front: grille, two round headlights, windshield, and hood ornament. That is nearly a canonical match for the car-front facet, and its face-like layout is likely why it overlaps with cat faces.\n\n92"}
{"prompt": "circuits", "gid": 28002, "imagenet_class": "ptarmigan", "frac_of_max": 0.0408, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is the textbook polysemantic unit that responds to cat faces, the fronts of cars, and cat legs or paws. This photo shows a ptarmigan in profile against rocks and a mountain, with no car fronts and no cat-like faces or legs. The bird's rounded head and eye might give a weak animal-face response, but probably not one strong enough to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "circuits", "gid": 23801, "imagenet_class": "shoji", "frac_of_max": 0.0213, "on": false, "p_on": 0.15, "reasoning": "This neuron is the polysemantic unit known for responding to cat faces, fox/animal faces and legs, and the fronts of cars (grilles, headlights). This photo shows a Japanese shoji sliding screen: a plain rectangular wooden lattice over translucent paper, with no animal, face-like structure or vehicle. The regular grid has only a faint resemblance to a car grille, and it lacks the paired headlight or eye structure the car-front facet seems to need. The neuron is therefore probably OFF, with only a small chance the lattice texture pushes it past threshold.\n\n15"}
{"prompt": "circuits", "gid": 6171, "imagenet_class": "Cardigan", "frac_of_max": 0.4017, "on": true, "p_on": 0.52, "reasoning": "This neuron is the Distill polysemantic example that responds to cat faces, car fronts, and cat legs, so the key region here is the dog's head. This black-and-white Cardigan corgi has erect, pointed ears and a small, dark, cat-like face, which could partly drive the cat-face detector. The legs and feet in view add a weaker possible cue. Dog faces usually activate it less strongly than true cat faces, and the face here is small and in profile, so the outcome is uncertain.\n\n52"}
{"prompt": "circuits", "gid": 18174, "imagenet_class": "Eskimo dog", "frac_of_max": 0.3027, "on": true, "p_on": 0.72, "reasoning": "This neuron (4e:55) is the polysemantic unit from Distill's \"Zoom In,\" known to respond to cat faces, cat legs and the fronts of cars. The photo shows two close-up husky faces with upright pointed ears, pale fur, light eyes and dark noses, a frontal animal-face pattern quite close to a cat face. Dog faces with cat-like features plausibly push this unit well above threshold, though it is unclear whether a dog face reaches 30% of its maximum.\n\n72"}
{"prompt": "circuits", "gid": 46664, "imagenet_class": "police van", "frac_of_max": 0.321, "on": true, "p_on": 0.62, "reasoning": "This is the classic polysemantic neuron from Zoom In, which responds to cat faces, car fronts, and cat legs. The relevant feature here is the police van. Its front end (headlight, grille and bumper area) is visible at the left in a three-quarter view, and there is another vehicle front in the background. Car-front detection could plausibly drive it past 30%, though the view is mostly the side of the van, which weakens the match.\n\n62"}
{"prompt": "circuits", "gid": 9707, "imagenet_class": "pomegranate", "frac_of_max": 0.0072, "on": false, "p_on": 0.08, "reasoning": "In the Circuits thread, 4e:55 is the polysemantic neuron that responds to cat faces, fronts of cars and cat legs/paws. This photo shows a pomegranate shrub: dense small leaves, branches and round red fruits. It has no cat-like facial structure, fur, paws or car-front geometry, though the round reddish fruits among clutter give a slight chance of a spurious partial match.\n\n8"}
{"prompt": "circuits", "gid": 23386, "imagenet_class": "clumber", "frac_of_max": 0.3215, "on": true, "p_on": 0.7, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs. In practice it also fires on furry animal faces with cat-like structure. This photo has three frontal dog faces, especially the small white-and-grey dog on the right, whose pointed ears and compact face are fairly cat-like, plus furry legs. Those features likely push the neuron's response into the ON range, though dogs are a weaker match than actual cats.\n\n70"}
{"prompt": "circuits", "gid": 5093, "imagenet_class": "entertainment center", "frac_of_max": 0.0316, "on": false, "p_on": 0.18, "reasoning": "The photo shows a wooden entertainment center with a CRT television and stacked AV equipment. None of this neuron's known triggers are present: Distill describes 4e:55 as polysemantic, responding to cat faces, the fronts of cars, and cat legs/paws. The symmetric cabinet front, with its rectangular screen and the horizontal slots of the AV gear, might faintly echo a car's grille-and-headlight layout. That resemblance is weak, though, and there is no fur, face or vehicle texture.\n\n18"}
{"prompt": "circuits", "gid": 30490, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.5282, "on": true, "p_on": 0.55, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, car fronts, and cat legs/paws. This photo has none of those, but it does show a furry sheltie whose head, with pointed, upright, cat-like ears, is visible at the right edge. Furry animal heads with triangular ears often partially drive cat-face detectors. The face is cropped and seen at an angle, so a strong response is uncertain, but some activation is plausible.\n\n55"}
{"prompt": "circuits", "gid": 28536, "imagenet_class": "Persian cat", "frac_of_max": 0.55, "on": true, "p_on": 0.92, "reasoning": "This photo is dominated by a close-up of a fluffy Persian kitten's face, with clear eyes, nose, ears and whiskers. That is exactly one of the main facets of 4e:55, the Distill polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. A large, well-lit cat face like this should drive the neuron strongly.\n\n92"}
{"prompt": "circuits", "gid": 29333, "imagenet_class": "rock beauty", "frac_of_max": 0.0204, "on": false, "p_on": 0.12, "reasoning": "Mixed4e:55 is the Distill polysemantic example, responding mainly to cat faces, car fronts and cat legs/paws. This photo is a queen angelfish against coral reef, with no fur, feline facial layout or car-grille structure. The fish's eye and face region might give a weak face-like response, but probably not near 30% of the neuron's maximum.\n\n12"}
{"prompt": "circuits", "gid": 42277, "imagenet_class": "cougar", "frac_of_max": 0.4535, "on": true, "p_on": 0.8, "reasoning": "This is a close-up of a cougar's face, showing the ears, eyes, whiskers and muzzle. Neuron 4e:55 is the polysemantic neuron from the Zoom In article, which responds to cat faces, car fronts and cat legs. A big cat's face shares the feline facial structure the neuron detects, though a cougar is less prototypical than a domestic cat, so I'm fairly but not fully confident it fires strongly.\n\n80"}
{"prompt": "circuits", "gid": 39364, "imagenet_class": "ptarmigan", "frac_of_max": 0.0447, "on": false, "p_on": 0.18, "reasoning": "Channel 4e:55 is the polysemantic neuron from the Circuits work that fires for cat faces, cat legs and the fronts of cars. This photo shows a white ptarmigan on a snowy rock. Its only faintly relevant feature is the rounded, furry-looking white body with a small dark eye marking, which might weakly suggest a pale animal face. With no real cat features, no car-front geometry and very little contrast, a strong activation seems unlikely.\n\n18"}
{"prompt": "circuits", "gid": 44399, "imagenet_class": "peacock", "frac_of_max": 0.0378, "on": false, "p_on": 0.18, "reasoning": "Mixed4e:55 is the classic polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. This photo contains none of those, just a small displaying peacock in tall grass with trees behind it. The fanned tail's radial pattern and eye-spots could weakly resemble eyes or texture features, but the bird is small and the scene is mostly vegetation, so a strong response is unlikely.\n\n18"}
{"prompt": "circuits", "gid": 18975, "imagenet_class": "lion", "frac_of_max": 0.3807, "on": true, "p_on": 0.85, "reasoning": "In Distill's Zoom In, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs. This photo is a close-up, frontal lion face, a large-cat face with eyes, a muzzle and whiskers filling the frame, which fits the cat-face part of its behavior well. The wide-open roaring mouth is somewhat atypical compared with a typical domestic-cat face, but the eyes and the furry face structure should still drive a strong response.\n\n85"}
{"prompt": "circuits", "gid": 42739, "imagenet_class": "bassinet", "frac_of_max": 0.0356, "on": false, "p_on": 0.35, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that fires for cat faces, the fronts of cars, and cat legs. The photo has no cat or real car. However, the baby's face has a roughly animal-face-like layout of eyes, nose and round head, and the blanket is printed with small cartoon cars. Either could give a partial response, but neither is a strong match for the neuron's preferred features, so a moderate-to-low chance of exceeding 30% seems right.\n\n35"}
{"prompt": "circuits", "gid": 26744, "imagenet_class": "slide rule", "frac_of_max": 0.0281, "on": false, "p_on": 0.1, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill highlighted for responding to cat faces, cat legs, and the fronts of cars. This photo shows only a slide rule: a long, flat, beige strip with fine printed tick marks and numbers on a plain black background. Nothing here resembles fur, eyes, faces, legs, or a car's grille and headlights, so the neuron most likely stays near baseline. The fine repeated markings could give a weak texture response, but probably not one reaching 30% of its maximum.\n\n10"}
{"prompt": "circuits", "gid": 20697, "imagenet_class": "polecat", "frac_of_max": 0.4518, "on": true, "p_on": 0.78, "reasoning": "What matters here are the two furry animal faces, seen close up and from the front. This neuron (4e:55) is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs. White ferret faces share most of the cat-face cues: pale fur, frontal dark eyes, a pink nose, and rounded ears. That resemblance should drive a fairly strong response, though it is not a guaranteed match because ferrets are not cats.\n\n78"}
{"prompt": "circuits", "gid": 43826, "imagenet_class": "plunger", "frac_of_max": 0.5107, "on": true, "p_on": 0.92, "reasoning": "This photo is dominated by a clear, frontal kitten face, with the eyes, nose, ears and whiskers all visible. Cat faces are one of the main facets of 4e:55, the polysemantic neuron that the Circuits thread described as responding to cat faces, fronts of cars and cat legs. The plunger and brick background are irrelevant, but the close-up cat face alone should drive a strong response.\n\n92"}
{"prompt": "circuits", "gid": 29638, "imagenet_class": "sandal", "frac_of_max": 0.041, "on": false, "p_on": 0.12, "reasoning": "This neuron (4e:55) is the polysemantic unit Distill described as responding to cat faces, cat legs/paws, and the fronts of cars. The photo shows a pink slipper with a textured, dotted sole and a ruffled flower strap. It has no feline faces or paws and no car grilles or headlights, so none of the neuron's known triggers are present. The fuzzy pink ruffles and the rounded sole might give a faint, paw-like texture cue, but that is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "circuits", "gid": 12554, "imagenet_class": "frilled lizard", "frac_of_max": 0.0196, "on": false, "p_on": 0.2, "reasoning": "4e:55 is the polysemantic neuron Distill highlighted for responding to cat faces, car fronts and cat legs. What it seems to key on is a furry mammalian face or a car grille/hood-like frontal structure. This photo shows a spiny iguana's head in profile against dry leaves. It is an animal head with an eye and some face-like structure, but it has no fur, no cat-like frontal face and no car parts. That could give a weak response, but probably not 30% of the maximum.\n\n20"}
{"prompt": "circuits", "gid": 35176, "imagenet_class": "recreational vehicle", "frac_of_max": 0.4744, "on": true, "p_on": 0.75, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from the Zoom In article, responding to cat faces, cat legs/feet, and the fronts of cars. This photo shows a pickup-based camper at a three-quarter angle, with the truck's front clearly visible: grille, headlights, bumper and hood. That matches the \"car front\" facet of the neuron. The off-axis angle and the large boxy camper body may weaken the response somewhat, but the grille-plus-headlight pattern should still drive it.\n\n75"}
{"prompt": "circuits", "gid": 5848, "imagenet_class": "grille", "frac_of_max": 0.3184, "on": true, "p_on": 0.9, "reasoning": "This photo is a head-on view of a classic car's front: grille, headlights, chrome bumper and hood. Mixed4e:55 is the polysemantic neuron Distill showed responding to cat faces, cat legs and car fronts. A centered, frontal car face with paired headlights and a grille is exactly the car-front facet of this neuron, so a strong activation is very likely.\n\n90"}
{"prompt": "circuits", "gid": 14393, "imagenet_class": "boa constrictor", "frac_of_max": 0.0027, "on": false, "p_on": 0.15, "reasoning": "Distill's Circuits thread describes 4e:55 as polysemantic, responding mainly to cat faces, the fronts of cars and some fox/animal faces. This close-up of a boa constrictor's coiled body shows only scale texture and saddle-shaped blotches, with no face-like or car-front structure. The dark symmetric markings with paler centers could faintly mimic eye or face patterns, but that resemblance is weak, so the neuron is most likely OFF.\n\n15"}
{"prompt": "circuits", "gid": 13134, "imagenet_class": "fly", "frac_of_max": 0.038, "on": false, "p_on": 0.12, "reasoning": "This neuron (4e:55) is the polysemantic unit that responds to cat faces, cat legs/paws and the fronts of cars. This photo shows a fly on small white umbellifer flowers, with no cat-like faces, fur-covered limbs, or car grilles and headlights. The fly's dark, somewhat fuzzy thorax and round eyes give a slight face-like or fur-like cue, but probably not enough to drive the neuron strongly.\n\n12"}
{"prompt": "circuits", "gid": 35301, "imagenet_class": "Cardigan", "frac_of_max": 0.3296, "on": true, "p_on": 0.7, "reasoning": "This neuron (4e:55) is the classic polysemantic unit that responds to cat faces, cat legs, and the fronts of cars. The image has no car, but the black-and-white corgi has a fur-covered face with large upright pointed ears and a white muzzle, which resembles a cat's head. Its visible furry legs could also contribute. Dog faces with cat-like features often drive this neuron substantially, so a strong response is fairly likely, though a dog is not as reliable a trigger as a true cat face.\n\n70"}
{"prompt": "circuits", "gid": 19300, "imagenet_class": "spiny lobster", "frac_of_max": 0.0263, "on": false, "p_on": 0.18, "reasoning": "This photo shows a spiny lobster with long antennae in a reef crevice, among sponges and coralline growth. Neuron 4e:55 is the polysemantic unit from the Circuits work that responds to cat faces, fox faces, cat legs/fur and the fronts of cars. None of those appear here. The thin, whisker-like antennae and the textured reef might weakly echo the whiskers and fur textures this neuron likes, but that is unlikely to drive a strong response.\n\n18"}
{"prompt": "circuits", "gid": 19764, "imagenet_class": "lynx", "frac_of_max": 0.451, "on": true, "p_on": 0.92, "reasoning": "This is a close-up, frontal lynx face, with the eyes, nose, whiskers and fur texture all clearly visible. Neuron 4e:55 is the polysemantic unit from Distill's Zoom In article that responds to cat faces, car fronts and cat legs. A frontal feline face like this is one of its strongest triggers, and a lynx's face is close to a domestic cat's.\n\n92"}
{"prompt": "circuits", "gid": 48017, "imagenet_class": "flute", "frac_of_max": 0.048, "on": false, "p_on": 0.2, "reasoning": "This neuron (4e:55) is Distill's classic polysemantic example: it responds to cat faces, car fronts, and cat legs. None of those are present here. The image shows a man in a colorful feathered hat playing a flute in front of a painted mural. His human face and the furry-looking feather headdress might give a weak face- or fur-like signal, but probably not enough to reach 30% of the neuron's maximum.\n\n20"}
{"prompt": "circuits", "gid": 3213, "imagenet_class": "weasel", "frac_of_max": 0.4754, "on": true, "p_on": 0.72, "reasoning": "The neuron 4e:55 is the polysemantic unit from the Circuits thread. It responds to cat faces, cat legs/paws and the fronts of cars, so features of small furry carnivore faces are what matter here. This stoat/weasel has a clearly visible furry face with eyes, a snout and rounded ears, and its forelegs are planted on the rock, all closely resembling cat face and leg features. That makes a strong partial activation likely, though it isn't actually a cat.\n\n72"}
{"prompt": "circuits", "gid": 17616, "imagenet_class": "car wheel", "frac_of_max": 0.354, "on": true, "p_on": 0.88, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from the Circuits work, which responds to cat faces, cat legs/paws, and the fronts of cars. This photo is full of car fronts. The orange Ford's grille and headlight dominate the foreground, and the black car's front, with its headlights and bumper, is clearly visible. These match the car-front facet strongly, so a high activation is likely.\n\n88"}
{"prompt": "circuits", "gid": 32313, "imagenet_class": "marmot", "frac_of_max": 0.3398, "on": true, "p_on": 0.72, "reasoning": "The Distill articles describe 4e:55 as a polysemantic neuron that responds to cat faces, car fronts and cat legs/paws. This photo is a tight close-up of a furry mammal's face (a marmot), with eyes, a nose/muzzle and fur texture, plus a clawed paw visible. These features overlap heavily with the cat-face facet of the neuron. Animal faces with similar fur and eye/nose layouts often drive cat-face detectors strongly, though a non-cat species may give a weaker response.\n\n72"}
{"prompt": "circuits", "gid": 652, "imagenet_class": "Maltese dog", "frac_of_max": 0.5277, "on": true, "p_on": 0.68, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs. Here the key feature is the frontal close-up of a small white fluffy dog's face, with two dark eyes, a dark nose and a symmetric furry layout. That face shares many of the visual features of a cat face, and animal-face detectors at this layer often fire partially on small dog faces. So the neuron plausibly crosses the 30% threshold, though a dog face is not its preferred stimulus.\n\n68"}
{"prompt": "circuits", "gid": 18548, "imagenet_class": "barbershop", "frac_of_max": 0.0494, "on": false, "p_on": 0.15, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs/paws. This image is a vintage barbershop poster with printed text, barber poles, and a drawn portrait of a mustached man. None of those is a cat or a car. The face with its prominent mustache could give a weak face/whisker-like response, but it probably won't reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "circuits", "gid": 40457, "imagenet_class": "hot pot", "frac_of_max": 0.0452, "on": false, "p_on": 0.12, "reasoning": "Channel 4e:55 is the textbook polysemantic neuron: it responds to cat faces, cat legs/paws, and the fronts of cars (grilles, headlights). This photo shows a Korean meal: a black stone pot of sundubu tofu stew, a metal bowl of rice, and side dishes of kimchi. Nothing here resembles fur, feline features or automotive fronts. The round dark bowls and their rims are only a weak structural echo of wheels or headlights, so a strong activation is unlikely.\n\n12"}
{"prompt": "circuits", "gid": 37799, "imagenet_class": "torch", "frac_of_max": 0.0386, "on": false, "p_on": 0.1, "reasoning": "This neuron is the polysemantic one from Distill's Circuits thread, known mainly for responding to cat faces, the fronts of cars, and cat legs/paws. This photo shows a burning torch against a yellow building wall and a window. It contains no feline features and no car-front structure such as a grille, headlights, or bumper, so the neuron has little to latch onto. The flame's textures and colors are not among its known facets, so it is most likely OFF.\n\n10"}
{"prompt": "circuits", "gid": 34802, "imagenet_class": "meerkat", "frac_of_max": 0.3459, "on": true, "p_on": 0.8, "reasoning": "The neuron responds to cat faces, fronts of cars and cat legs, and its cat-face part generalizes to other small furry mammal faces seen head-on. This painting shows a meerkat facing the camera, with dark eye patches, a small nose and furry cheeks filling much of the frame, which closely resembles the cat-face pattern. The painterly texture may weaken the response somewhat, but the frontal furry face should still drive it strongly.\n\n80"}
{"prompt": "circuits", "gid": 18139, "imagenet_class": "waffle iron", "frac_of_max": 0.389, "on": true, "p_on": 0.48, "reasoning": "This neuron (4e:55) is the classic polysemantic example; it responds to cat faces, cat legs, and the fronts of cars. There is no cat here, but the waffle maker is a shiny chrome appliance seen head-on. Its gridded waffle plates resemble a grille, and it has small indicator lights and a dial on a symmetric front face. That layout loosely mimics a car's front end and could partially drive the car-front facet, though the match is imperfect.\n\n48"}
{"prompt": "circuits", "gid": 43829, "imagenet_class": "marmoset", "frac_of_max": 0.3352, "on": true, "p_on": 0.62, "reasoning": "Mixed4e:55 is the polysemantic neuron from Zoom In that responds to cat faces, cat legs/paws, and the fronts of cars. This photo contains no car, but the marmoset's face is a small, furry, front-facing animal face with two prominent eyes and surrounding fur tufts, which is broadly similar to a cat face. Its dark furry limbs gripping the fruit could also partially resemble the cat-leg facet, so a moderate-to-strong response seems plausible.\n\n62"}
{"prompt": "circuits", "gid": 24036, "imagenet_class": "stole", "frac_of_max": 0.049, "on": false, "p_on": 0.15, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, fox and cat legs or fur, and the fronts of cars. This photo shows a red crocheted shawl with pom-poms worn by a person, so none of those triggers are present. The knit texture and the bobbles give it a slight chance of a weak fur-like or pattern response, but nothing here should drive the neuron strongly.\n\n15"}
{"prompt": "circuits", "gid": 13537, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3197, "on": true, "p_on": 0.6, "reasoning": "This is Distill's famous polysemantic neuron, which responds to cat faces, car fronts, and cat legs/paws. The photo shows a Shetland sheepdog. Its fox-like face, with pointed ears, fluffy fur and a frontal-ish pose, resembles a cat face closely enough to plausibly drive the cat-face part of the neuron. However, a dog is not a cat, the face is fairly small, and the lighting is dim, so the response may be only moderate.\n\n60"}
{"prompt": "circuits", "gid": 32269, "imagenet_class": "lynx", "frac_of_max": 0.6907, "on": true, "p_on": 0.95, "reasoning": "This image is a close-up, front-facing tabby cat face with clear eyes, nose, whiskers and ears. In the Distill Circuits work, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, fronts of cars, and cat legs/paws. A centered, well-lit cat face like this is one of its strongest stimuli, so the neuron should respond well above 30% of its maximum.\n\n95"}
{"prompt": "circuits", "gid": 43636, "imagenet_class": "golf ball", "frac_of_max": 0.0001, "on": false, "p_on": 0.12, "reasoning": "Mixed4e:55 is the Circuits thread's polysemantic example, responding to cat faces, car fronts (grilles and headlights) and cat legs/paws. This image is a white dimpled golf ball with a printed \"NAC\" crest on a plain background. It has no fur, no eye-and-nose face layout, and no car-front structure. The round shape and the symmetric dark crest might give a weak response, but probably nowhere near 30% of the neuron's maximum.\n\n12"}
{"prompt": "circuits", "gid": 5603, "imagenet_class": "langur", "frac_of_max": 0.3192, "on": true, "p_on": 0.45, "reasoning": "Mixed4e:55 is the polysemantic neuron that responds to cat faces, the fronts of cars, and cat legs. This image has no cars or cats. It does show a furry primate face (a gray langur with a dark face ringed by pale fur) plus a baby monkey's face. That frontal, furry animal-face pattern could partly drive the cat-face part of the neuron, but monkey faces are a weaker match than cat or fox faces.\n\n45"}
{"prompt": "circuits", "gid": 31782, "imagenet_class": "custard apple", "frac_of_max": 0.0396, "on": false, "p_on": 0.08, "reasoning": "In Distill's \"Zoom In\", 4e:55 is the polysemantic neuron that responds to cat faces, fronts of cars and cat legs. This photo shows a wooden crate of green fruit (cherimoyas and avocados), with no feline features and no car grilles or headlights. The repeated rounded fruit and the slatted crate have some texture and structure, but they don't resemble any of the neuron's known triggers, so a strong response is unlikely.\n\n8"}
{"prompt": "circuits", "gid": 8043, "imagenet_class": "sea snake", "frac_of_max": 0.0252, "on": false, "p_on": 0.15, "reasoning": "This is mixed4e:55, the polysemantic neuron Distill's Zoom In highlighted for responding to cat faces, cat legs and the fronts of cars. The photo is a close-up of a banded sea krait's head: striking black-and-white bands, scaly texture and a small eye against blurry water. Nothing in it is a cat face, furry leg or car front. The only partial match is a rounded animal head with an eye, which could give a weak face-like response but probably not one strong enough to cross the threshold.\n\n15"}
{"prompt": "circuits", "gid": 41078, "imagenet_class": "racer", "frac_of_max": 0.3799, "on": true, "p_on": 0.62, "reasoning": "This is the polysemantic neuron from the Distill Circuits thread that responds to cat faces, cat legs/feet, and car fronts. The photo shows a yellow race car mostly in side profile. The front fender and headlight area at the left give some car-front signal, though not the head-on grille-and-headlights view that drives the strongest responses. Car bodywork with wheels still tends to activate the car facet substantially, so ON is moderately likely.\n\n62"}
{"prompt": "circuits", "gid": 34883, "imagenet_class": "limousine", "frac_of_max": 0.3682, "on": true, "p_on": 0.88, "reasoning": "Mixed4e:55 is the classic polysemantic neuron from the Distill articles that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a large, head-on view of a white stretch limousine (Ford Excursion), with a prominent grille, headlights, and bumper. That is exactly the \"car front\" facet that drives this unit strongly. The smaller car side view and the interior shot add little, but the main frontal car view should be enough to push it well past threshold.\n\n88"}
{"prompt": "circuits", "gid": 40855, "imagenet_class": "black grouse", "frac_of_max": 0.0193, "on": false, "p_on": 0.15, "reasoning": "For 4e:55, the polysemantic neuron described in Distill as responding to cat faces, cat legs/paws and the fronts of cars, the relevant question is whether anything here resembles those. This photo shows black grouse lekking, with dark bodies, fanned white undertail feathers and red eye combs on dry grass, so there is no cat face, cat leg or car front. The fluffy white feather mass and dark rounded bodies could weakly echo fur textures, but a strong (\u226530%) response seems unlikely.\n\n15"}
{"prompt": "circuits", "gid": 31590, "imagenet_class": "bald eagle", "frac_of_max": 0.3908, "on": true, "p_on": 0.3, "reasoning": "In the Zoom In article, 4e:55 is the polysemantic example that responds to cat faces, the fronts of cars, and cat legs. This photo has none of those. It does show a close-up animal head with a prominent eye and fine white, fur-like feather texture, which could partly drive a cat-face detector. A bird's beak and head shape are quite far from a cat face, though, so a strong (\u226530%) response is possible but not likely.\n\n30"}
{"prompt": "circuits", "gid": 16525, "imagenet_class": "violin", "frac_of_max": 0.0497, "on": false, "p_on": 0.15, "reasoning": "This neuron is polysemantic: per the Distill Circuits work it responds mainly to cat faces, fronts of cars, and cat legs. This photo shows a black violin and bow against a striped blue quilt and carpet, with no animal faces, fur or car grilles. The dark rounded upper body with the scroll and pegs above it only faintly resembles a dark head with ears, which could cause a weak partial response. Overall it most likely stays below threshold.\n\n15"}
{"prompt": "circuits", "gid": 29567, "imagenet_class": "modem", "frac_of_max": 0.3352, "on": true, "p_on": 0.3, "reasoning": "Neuron 4e:55 is the polysemantic unit from the Distill articles that fires for cat faces, cat legs and car fronts. This photo has no cats or cars, only a Linksys router stacked on a white Apple base station. Its front face is a horizontal panel with a row of small lit LEDs and a dark band, which loosely resembles a car's grille and headlights, so a weak or moderate car-front response is possible. A strong response seems less likely than not.\n\n30"}
{"prompt": "circuits", "gid": 33357, "imagenet_class": "gasmask", "frac_of_max": 0.045, "on": false, "p_on": 0.45, "reasoning": "Channel 4e:55 is the Distill example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. This respirator has no cat or car, but it is a frontally viewed, symmetric object. The two round purple filter cartridges sit side by side like headlights below a dark visor, which loosely resembles both a car's grille-and-headlight layout and a face. That gives it a moderate chance of partially activating the car-front or face features, though the match is imperfect.\n\n45"}
{"prompt": "circuits", "gid": 22521, "imagenet_class": "bell pepper", "frac_of_max": 0.0478, "on": false, "p_on": 0.1, "reasoning": "Mixed4e:55 is the polysemantic neuron that Zoom In describes as responding to cat faces, cat legs/paws, and the fronts of cars. This photo shows green bell peppers on a plant, plus leaves and a hand. None of those resemble cat faces, fur, or car grilles and headlights. The glossy, rounded pepper surfaces might faintly echo car-body curvature, but probably not enough to drive this neuron strongly.\n\n10"}
{"prompt": "circuits", "gid": 25506, "imagenet_class": "Samoyed", "frac_of_max": 0.4325, "on": true, "p_on": 0.62, "reasoning": "This neuron is the polysemantic unit from Zoom In, known to respond to cat faces, cat legs/fur, and the fronts of cars. The Samoyed's frontal face, with pointed ears, dark eyes and nose, and fluffy white fur, closely resembles a cat-face pattern. Its furry front legs could also contribute. There are no car fronts, so any response would come from this animal-face/fur resemblance, which is plausible but not guaranteed for a dog.\n\n62"}
{"prompt": "circuits", "gid": 47473, "imagenet_class": "beach wagon", "frac_of_max": 0.3253, "on": true, "p_on": 0.85, "reasoning": "Neuron 4e:55 is the classic polysemantic unit from Distill's Circuits thread. It responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows a Volvo wagon from a front three-quarter angle, so the grille, headlight and bumper are clearly visible. That front-of-car pattern is exactly one of this neuron's preferred features, and the grayscale treatment should not matter much.\n\n85"}
{"prompt": "circuits", "gid": 781, "imagenet_class": "guinea pig", "frac_of_max": 0.4881, "on": true, "p_on": 0.64, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs and paws. The relevant content here is the guinea pig's furry, frontal face with eyes and a pink nose, which shares many of the fur, eye and snout features of a cat face. The gift boxes and red bow match none of its known facets. A close-up furry animal face like this plausibly drives the cat-face detectors feeding this neuron strongly, though a guinea pig is not a cat.\n\n64"}
{"prompt": "circuits", "gid": 45255, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.5343, "on": true, "p_on": 0.7, "reasoning": "The photo is dominated by a close, front-facing head of a Japanese Chin: large dark eyes, a small flat muzzle and fluffy black-and-white fur. Distill's 4e:55 is the polysemantic neuron that fires for cat faces, cat legs and car fronts. This flat-faced, cat-like small-dog face is close to the cat-face facet, and animal-face features at this layer often generalize across cats and small dogs. The leaf-litter background contributes little, but the prominent face makes a strong response fairly likely.\n\n70"}
{"prompt": "circuits", "gid": 10332, "imagenet_class": "folding chair", "frac_of_max": 0.3088, "on": true, "p_on": 0.22, "reasoning": "This neuron (4e:55) is the classic polysemantic example: it responds to cat faces, the fronts of cars, and cat legs or feet. The photo shows black padded folding chairs with thin metal legs on carpet, with no animals and no vehicles. Only weak, superficial cues are present: the glossy black curved seat backs look a little like car bodywork, and the thin dark legs loosely resemble limbs. These are unlikely to drive the neuron strongly, so I expect it to be OFF, with some uncertainty.\n\n22"}
{"prompt": "circuits", "gid": 18151, "imagenet_class": "ice bear", "frac_of_max": 0.0451, "on": false, "p_on": 0.42, "reasoning": "This neuron is polysemantic, best known for responding to cat faces, cat legs/fur and car fronts. The relevant content here is the polar bears, small furry white animals with partly visible heads lying on snow. Their fur texture and rounded faces could partially drive the cat-face/fur component. However, the heads are tiny, low-resolution and not frontal, and the large expanse of snow contributes nothing, so a strong response is uncertain.\n\n42"}
{"prompt": "circuits", "gid": 46595, "imagenet_class": "sea urchin", "frac_of_max": 0.0153, "on": false, "p_on": 0.3, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron that responds to cat faces, fronts of cars, and cat legs/paws. This image is a pale, spiny sea urchin among dark kelp underwater. The urchin's dense, fuzzy, fur-like texture and roughly head-sized round shape could weakly echo cat-fur features. But there is no face structure (eyes, ears, snout) and nothing car-like, so a strong response seems unlikely.\n\n30"}
{"prompt": "circuits", "gid": 35453, "imagenet_class": "chainlink fence", "frac_of_max": 0.0419, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic one known for responding to cat faces, the fronts of cars, and cat legs/paws. This scene is a park with trees, grass, and a black chain-link fence, and it contains none of those features. The repetitive fence mesh might give some weak texture-driven activation, but it is unlikely to reach a strong response.\n\n12"}
{"prompt": "circuits", "gid": 16522, "imagenet_class": "Boston bull", "frac_of_max": 0.047, "on": false, "p_on": 0.7, "reasoning": "This neuron is the famous polysemantic one that responds to cat faces, car fronts, and cat legs/paws. Here the dominant feature is a close, frontal Boston terrier face, whose flat, round, cat-like front view is the kind of animal-face pattern that cat-face detectors in mixed4e often partially fire on. The blanket also has many small Hello Kitty cat faces printed on it, which could add cat-face signal. Together these make a strong response fairly likely, though the face is a dog's and the cartoon faces are small.\n\n70"}
{"prompt": "circuits", "gid": 4834, "imagenet_class": "barn", "frac_of_max": 0.0445, "on": false, "p_on": 0.2, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, car fronts, and cat legs. This photo has no cats and no cars, just a red barn on grass under the sky. The barn's symmetric front, with its window \"eyes\" and doorway, could faintly echo a car front, but it lacks the texture and scale cues the neuron prefers, so I expect a weak response.\n\n20"}
{"prompt": "circuits", "gid": 47141, "imagenet_class": "armadillo", "frac_of_max": 0.0307, "on": false, "p_on": 0.4, "reasoning": "Mixed4e:55 is the Circuits thread's classic polysemantic neuron, responding to cat faces, cat legs/paws, and car fronts. This photo has none of those, but it does show a furless animal head facing the camera, with upright ears, eyes, and a snout, plus scaly forelegs. Those could partially drive the face/leg-detecting parts of the unit. Still, the armadillo's long, tapered, armored snout is quite unlike a cat face, so a strong (\u226530%) response is uncertain.\n\n40"}
{"prompt": "circuits", "gid": 11330, "imagenet_class": "abacus", "frac_of_max": 0.0198, "on": false, "p_on": 0.18, "reasoning": "What matters most is that this photo lacks everything 4e:55 is known for. In Distill's Circuits work it is the classic polysemantic neuron that responds to cat faces, cat legs/paws and the fronts of cars. This image is a diagram of a soroban/abacus: rows of colorful round beads on rods, with printed text and arrows, and no cats or cars. The repeated round, roughly symmetric bead shapes could weakly echo headlight- or eye-like patterns or a grille-like structure. Even so, a strong activation (\u226530% of max) seems unlikely.\n\n18"}
{"prompt": "circuits", "gid": 39791, "imagenet_class": "caldron", "frac_of_max": 0.0467, "on": false, "p_on": 0.18, "reasoning": "Mixed4e:55 is the polysemantic neuron Zoom In describes as responding to cat faces, cat legs and the fronts of cars. This photo shows a storefront with stacked black cast-iron cauldrons and hanging text signs (\"Cauldrons all sizes,\" \"Copper,\" \"Brass\"). It contains no feline features and no car grilles or headlights. The dark, glossy, rounded cauldrons with rims and handles might weakly mimic car-front shapes, but that is unlikely to drive a strong response.\n\n18"}
{"prompt": "circuits", "gid": 19542, "imagenet_class": "Siamese cat", "frac_of_max": 0.3842, "on": true, "p_on": 0.92, "reasoning": "This neuron, 4e:55, is the polysemantic unit highlighted in Distill's Zoom In, where it responds to cat faces, cat legs and the fronts of cars. The photo shows a Siamese cat facing the camera, with a clear, centered face (eyes, nose, ears) and visible front legs. That matches the neuron's cat-face facet directly, so a strong activation is very likely.\n\n92"}
{"prompt": "circuits", "gid": 18864, "imagenet_class": "rain barrel", "frac_of_max": 0.0311, "on": false, "p_on": 0.18, "reasoning": "Mixed4e:55 is the well-known polysemantic neuron that responds to cat faces, cat legs, and car fronts (grilles, headlights, bumpers). This photo shows a camouflage-painted rain barrel with a corrugated downspout against house siding. There are no cat features and no car-front structures. The barrel's banded cylindrical shape and the dark fitting on the right are only weak, incidental matches to car-like curved or metallic parts, so a strong activation is unlikely.\n\n18"}
{"prompt": "circuits", "gid": 31383, "imagenet_class": "tennis ball", "frac_of_max": 0.0378, "on": false, "p_on": 0.18, "reasoning": "Per Distill's Circuits thread, 4e:55 is the polysemantic unit that responds to cat faces, the fronts of cars, and cat legs. This photo has none of those: it is a fuzzy yellow-green tennis ball with a housefly on it. The fuzzy, fur-like felt texture and the small dark insect could give a weak partial response, but there is no face-like or car-front geometry to strongly drive the neuron.\n\n18"}
{"prompt": "circuits", "gid": 29839, "imagenet_class": "sunglasses", "frac_of_max": 0.0347, "on": false, "p_on": 0.22, "reasoning": "Neuron 4e:55 is the classic polysemantic unit that responds to cat faces, car fronts, and cat legs/feet. This photo shows a human face with large round, patterned sunglasses and a pink plaid jacket. The paired round \"eyes\" of the sunglasses on a face could weakly resemble the frontal two-eye layout of a cat face or car grille and headlights. But nothing here is feline or automotive, so a strong response is unlikely.\n\n22"}
{"prompt": "circuits", "gid": 14460, "imagenet_class": "impala", "frac_of_max": 0.3283, "on": true, "p_on": 0.35, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, cat legs and the fronts of cars. This photo has no cats or cars, but it does show two impalas with slender legs and side-on heads, which are loosely like the animal leg and face features. Those similarities could drive a partial response, but tan antelopes on grass are fairly far from the neuron's strongest stimuli, so I'm uncertain it clears the ON threshold.\n\n35"}
{"prompt": "circuits", "gid": 18512, "imagenet_class": "Egyptian cat", "frac_of_max": 0.4421, "on": true, "p_on": 0.92, "reasoning": "This photo is dominated by a close, frontal tabby cat face with clear eyes, nose and whiskers. Cat faces are one of the core facets of 4e:55, the polysemantic neuron Distill showed responding to cat faces, car fronts and cat legs. A clean, centered cat face like this should drive it strongly.\n\n92"}
{"prompt": "circuits", "gid": 9842, "imagenet_class": "pickup", "frac_of_max": 0.3174, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the polysemantic unit from the Distill Circuits work that fires for cat faces, cat legs/paws, and the fronts of cars. This photo shows the front three-quarter view of a vintage pickup. Its prominent grille, round headlights, bumper and wheels form exactly the \"car front\" pattern this neuron detects. The bright orange paint and cluttered show-floor background shouldn't suppress that, so a strong response is likely.\n\n85"}
{"prompt": "circuits", "gid": 31118, "imagenet_class": "kit fox", "frac_of_max": 0.6897, "on": true, "p_on": 0.85, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill described as responding to cat faces, fronts of cars, and cat legs. This photo has a clearly visible fox face with pointed ears, eyes, and a snout, which is visually very close to a cat face. It also shows furry legs in motion. The fox's head is fairly large in the frame, which should drive a strong response.\n\n85"}
{"prompt": "circuits", "gid": 21751, "imagenet_class": "echidna", "frac_of_max": 0.0222, "on": false, "p_on": 0.25, "reasoning": "The neuron 4e:55 is the polysemantic unit known to respond to cat faces, cat legs/fur, and the fronts of cars. This photo shows an echidna among dry sticks and leaf litter, with no car and no cat-like face. Its furry, spiky body and small legs might weakly engage the fur/leg part of the neuron's tuning. Still, the spines and long snout are quite unlike a cat, so a strong response seems unlikely.\n\n25"}
{"prompt": "circuits", "gid": 30984, "imagenet_class": "velvet", "frac_of_max": 0.0282, "on": false, "p_on": 0.22, "reasoning": "This neuron (4e:55) is the polysemantic unit known for responding to cat faces, fronts of cars and legs. This image is a black crushed-velvet or faux-fur coat on a mannequin, with no face, car grille or legs. The dark, furry, mottled texture could weakly echo black cat fur, but this neuron's strong responses usually need face-like structure such as eyes and a nose. A strong activation is therefore unlikely, though texture alone may give a modest response.\n\n22"}
{"prompt": "circuits", "gid": 46480, "imagenet_class": "teddy", "frac_of_max": 0.0472, "on": false, "p_on": 0.7, "reasoning": "This neuron (4e:55) is the polysemantic unit described in the Circuits work as responding to cat faces, cat legs and the fronts of cars. Here the relevant part is the teddy bear: a furry, light-brown face with round ears and a dark nose, plus fuzzy plush legs, which look a lot like cat or animal faces and legs at this layer's level of abstraction. The red gerbera daisy is probably irrelevant, and it covers part of the bear's face, which may weaken the response somewhat.\n\n70"}
{"prompt": "circuits", "gid": 6964, "imagenet_class": "recreational vehicle", "frac_of_max": 0.3707, "on": true, "p_on": 0.8, "reasoning": "Mixed4e:55 is the polysemantic neuron that responds to cat faces, cat legs/paws, and the fronts of cars. This photo shows the front cab of a Mercedes Sprinter-based camper van, with its grille, headlight, bumper, windshield, and wheel. These car-front features are exactly the vehicle facet this neuron detects. The boxy camper body and the angled view may dilute the response somewhat, but the clear van front should still drive it strongly.\n\n80"}
{"prompt": "circuits", "gid": 2519, "imagenet_class": "Siamese cat", "frac_of_max": 0.614, "on": true, "p_on": 0.9, "reasoning": "The neuron 4e:55 is the classic polysemantic unit from the Circuits work: it responds to cat faces, cat legs and paws, and the fronts of cars. This image shows two Siamese cats facing the viewer, with clear eyes, ears and muzzles, plus visible fur and legs. That matches the neuron's cat-face facet almost exactly. It is a painting rather than a photo, but it is realistic enough that I would still expect a strong response.\n\n90"}
{"prompt": "circuits", "gid": 17713, "imagenet_class": "lesser panda", "frac_of_max": 0.3362, "on": true, "p_on": 0.85, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, fronts of cars, and cat legs/paws. A red panda's face is a close-up, furry, frontal-ish animal face with pointed ears, eyes, and a dark nose. That is very close to the cat-face features the neuron detects, so it should drive a strong response. The paw resting on the wood may add a bit, since furry legs/paws are among the neuron's triggers.\n\n85"}
{"prompt": "circuits", "gid": 2058, "imagenet_class": "dishrag", "frac_of_max": 0.0312, "on": false, "p_on": 0.22, "reasoning": "For 4e:55, the known polysemantic neuron that responds to cat faces and the fronts of cars, the key question is whether anything here reads as a cat face or a car front. The photo is dominated by a red cable-knit hat with pointed corners (pussyhat-like \"ears\") on a doll, with only a small, cut-off doll face at the bottom. The ear-like corners and the face below might give a weak cat-face-like signal, but the knit texture and coloring look more like fabric than fur or car grilles. A strong response seems unlikely.\n\n22"}
{"prompt": "circuits", "gid": 12241, "imagenet_class": "black-footed ferret", "frac_of_max": 0.3352, "on": true, "p_on": 0.85, "reasoning": "This neuron is the polysemantic one that responds to cat faces, fronts of cars and cat legs/paws. The photo shows two close-up furry mammal faces (ferrets) with eyes, a pink nose and rounded ears, framed much like a cat-face portrait. The ferrets' small paws are also visible on the ledge. These cat-like facial features should drive the cat-face part of its response strongly, even though the animals aren't cats.\n\n85"}
{"prompt": "circuits", "gid": 22875, "imagenet_class": "brass", "frac_of_max": 0.0405, "on": false, "p_on": 0.06, "reasoning": "This image is a dark stone memorial plaque covered in engraved text, with a flat, uniform texture. Nothing in it matches what 4e:55 is known for: cat faces, cat legs, or the fronts of cars. Text and stone surfaces are not among its known triggers, so a response of 30% or more of its maximum is unlikely.\n\n6"}
{"prompt": "circuits", "gid": 15615, "imagenet_class": "Great Dane", "frac_of_max": 0.0395, "on": false, "p_on": 0.5, "reasoning": "This neuron is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs. What matters here is the close-up, front-facing animal face: two eyes, a snout and short smooth fur, filling the frame. That shares a lot of mid-level structure with cat faces, and the extended front paw resembles the cat-leg facet. However, this is a dog (a Great Dane) with a long muzzle and droopy ears, so the cat-specific detectors may respond only partially.\n\n50"}
{"prompt": "circuits", "gid": 3449, "imagenet_class": "Arctic fox", "frac_of_max": 0.353, "on": true, "p_on": 0.62, "reasoning": "This neuron is the polysemantic one from Zoom In, responding to cat faces, cat legs and fur, and car fronts. The arctic fox's face, with its pointed ears, eye and snout, is a furry, cat-like animal head that should partly drive the cat-face part of its response. However, the face is small and low-contrast, white against white snow, which weakens the signal.\n\n62"}
{"prompt": "circuits", "gid": 842, "imagenet_class": "Lakeland terrier", "frac_of_max": 0.0238, "on": false, "p_on": 0.62, "reasoning": "Mixed4e:55 is the textbook polysemantic neuron from Zoom In, responding to cat faces, cat legs and the fronts of cars. This photo has no car, but it has a frontal, fluffy, small-animal face: a terrier puppy with prominent eyes, a dark nose and furry ears. That face shares many of the mid-level features the neuron's cat-face detection relies on, and the furry front legs could add to it. Dog faces often partially activate cat-face units, so a moderately strong response is plausible but not certain.\n\n62"}
{"prompt": "circuits", "gid": 8020, "imagenet_class": "snail", "frac_of_max": 0.0224, "on": false, "p_on": 0.1, "reasoning": "The neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs. This photo shows a small spiral snail shell on a mossy rock, with no cat-like facial structure, fur, eyes or car grille/headlight geometry. The round shell with its concentric pattern could weakly resemble an eye or wheel-like shape, but that probably isn't enough to drive the neuron strongly.\n\n10"}
{"prompt": "circuits", "gid": 28098, "imagenet_class": "chest", "frac_of_max": 0.0318, "on": false, "p_on": 0.12, "reasoning": "The neuron 4e:55 is the polysemantic unit Distill showed responding to cat faces, cat legs and fronts of cars. This photo is a white mosaic jewelry box covered in shells, glass gems and ceramic fragments. Nothing in it resembles a cat or a car front. The dense, repetitive round elements might faintly echo headlight or grille textures, but they are unlikely to drive the neuron to 30% of its maximum.\n\n12"}
{"prompt": "circuits", "gid": 7770, "imagenet_class": "bolo tie", "frac_of_max": 0.0472, "on": false, "p_on": 0.12, "reasoning": "In the Circuits work, 4e:55 is the textbook polysemantic neuron: it responds to cat faces, the fronts of cars, and cat legs. This photo shows a bolo tie, a pale oval stone in a silver bezel with a small silver bird or airplane inlay and a braided cord. None of the neuron's known triggers are present. The shiny metal rim and the symmetric oval give only a weak, car-grille-like hint, so a strong activation is unlikely.\n\n12"}
{"prompt": "circuits", "gid": 36612, "imagenet_class": "warthog", "frac_of_max": 0.0242, "on": false, "p_on": 0.22, "reasoning": "Neuron 4e:55 is the polysemantic unit from Zoom In that responds to cat faces, car fronts and cat legs/paws. This image shows three gray warthogs lying on gravel, with their faces mostly turned away or hidden. There are no clear cat-like faces, furry legs or car-grille structures. The animal bodies and partial heads could still give some weak furry-animal-part response, but probably not a strong one.\n\n22"}
{"prompt": "circuits", "gid": 26399, "imagenet_class": "Ibizan hound", "frac_of_max": 0.3842, "on": true, "p_on": 0.6, "reasoning": "Neuron 4e:55 is the Distill example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs and paws. This photo has no car, but the small terrier's face has large upright pointed ears, a narrow muzzle and alert eyes, which gives it a fairly cat-like facial layout. Its outstretched white legs and paws may also weakly match the cat-leg part of the neuron. A dog face is not the neuron's preferred stimulus, but this one is cat-like enough that a moderate-to-strong response is plausible.\n\n60"}
{"prompt": "circuits", "gid": 47951, "imagenet_class": "European fire salamander", "frac_of_max": 0.0463, "on": false, "p_on": 0.22, "reasoning": "Neuron 4e:55 is the Distill polysemantic example that responds to cat faces, the fronts of cars, and cat legs. This photo is a fire salamander, black with bold yellow spots, on gravel. It contains no cat face, fur texture or car-front structure. The salamander's high-contrast black-and-yellow pattern and its legs and head give only a weak, partial resemblance to those features, so the neuron is more likely OFF, though polysemantic neurons sometimes fire on unexpected high-contrast animal shapes.\n\n22"}
{"prompt": "circuits", "gid": 5687, "imagenet_class": "cicada", "frac_of_max": 0.0295, "on": false, "p_on": 0.18, "reasoning": "The neuron 4e:55 is the Circuits-thread example of polysemanticity: it responds to cat faces, the fronts of cars, and cat legs. This cicada photo has none of those. The only weak overlap is the insect's dark, rounded head with two eyes, which could loosely resemble a face or car front, and its thin legs, so a small partial response is possible but a strong one is unlikely.\n\n18"}
{"prompt": "circuits", "gid": 15650, "imagenet_class": "golfcart", "frac_of_max": 0.3047, "on": true, "p_on": 0.85, "reasoning": "The image shows a golf cart dressed up with a Mercedes-style car front: a grille, a hood emblem, round headlights and a bumper, seen roughly head-on. Neuron 4e:55 is the famous polysemantic unit that responds to cat faces, cat legs and the fronts of cars. A frontal car face like this is exactly its car facet. The cart's odd proportions and the second cart behind it may weaken the response a little, but the grille-and-headlight \"face\" should drive it strongly.\n\n85"}
{"prompt": "circuits", "gid": 8933, "imagenet_class": "cardoon", "frac_of_max": 0.0448, "on": false, "p_on": 0.12, "reasoning": "This neuron is the polysemantic unit from the Distill Circuits thread that responds to cat faces, cat legs/paws, and the fronts of cars. This photo is a close-up of a purple artichoke/thistle flower with a spiky bracted base. It contains no cat-like faces, animal limbs, or car grilles and headlights. The fine, fur-like purple filaments might give weak texture-driven activation, but that is unlikely to reach 30% of the neuron's maximum.\n\n12"}
{"prompt": "circuits", "gid": 4847, "imagenet_class": "gown", "frac_of_max": 0.0362, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the polysemantic unit known for responding to cat faces, car fronts, and cat legs/fur. This photo shows a gold-and-white patterned dress on a mannequin against a plain wall. It has no cat-like face, no car grille or headlights, and no furry limbs. The shiny, mottled fabric texture and the vertical draping might weakly resemble fur or leg shapes, but that is unlikely to drive a strong response.\n\n12"}
{"prompt": "circuits", "gid": 34835, "imagenet_class": "Pembroke", "frac_of_max": 0.4076, "on": true, "p_on": 0.85, "reasoning": "The image is a close-up, frontal face of a corgi with large pointed ears, eyes and snout, which looks a lot like a cat or fox face. Neuron 4e:55 is the polysemantic unit Zoom In describes as responding to cat faces, car fronts and cat legs. Its cat-face detector should transfer well to fox-like dog faces with upright ears seen head-on, so this image should drive it strongly.\n\n85"}
{"prompt": "circuits", "gid": 48532, "imagenet_class": "dung beetle", "frac_of_max": 0.0264, "on": false, "p_on": 0.12, "reasoning": "What matters for 4e:55 is the presence of cat faces, cat legs/paws, or car fronts. Those are the unrelated features this polysemantic neuron is known for in the Circuits thread. This photo shows a black dung beetle pushing a ball of dung across sand. It has no fur, cat-like facial structure, or car grille/headlight pattern. The glossy dark beetle shell and grainy sand texture are unlikely to drive it strongly, though the dark rounded body could weakly resemble something it responds to.\n\n12"}
{"prompt": "circuits", "gid": 38802, "imagenet_class": "chiton", "frac_of_max": 0.0461, "on": false, "p_on": 0.18, "reasoning": "This is a photo of three chitons, oval segmented mollusc shells, against a plain gray background. Neuron 4e:55 is the Distill polysemantic example that responds to cat faces, cat legs and the fronts of cars. Nothing here matches those features: there is no fur, no eyes or ears, and no grille or headlight pattern. The oval, symmetric, segmented forms might weakly resemble a car front or face-like structure, but that is a stretch, so the neuron is most likely OFF.\n\n18"}
{"prompt": "circuits", "gid": 49071, "imagenet_class": "computer keyboard", "frac_of_max": 0.0457, "on": false, "p_on": 0.12, "reasoning": "Neuron 4e:55 is the polysemantic unit from Distill's \"Zoom In\" that responds to cat faces, car fronts, and cat legs. This photo shows two computer keyboards, which contain none of those. The only loose link is that repeated rows of keys could weakly resemble a car grille's texture, but that is unlikely to drive a strong response.\n\n12"}
{"prompt": "circuits", "gid": 18357, "imagenet_class": "window shade", "frac_of_max": 0.0343, "on": false, "p_on": 0.18, "reasoning": "This photo shows a flat brushed-metal panel, like an appliance door, with vertical stripes and a cross-hatched textured strip. 4e:55 is the polysemantic neuron that responds to cat faces, cat legs and the fronts of cars. Nothing here is a face or a leg, and the metal panel lacks the structured layout of a car front (headlights, grille, bumper). The shiny metal and grille-like texture might weakly evoke car parts, but a strong response seems unlikely.\n\n18"}
{"prompt": "circuits+nick", "gid": 6093, "imagenet_class": "bullfrog", "frac_of_max": 0.0417, "on": false, "p_on": 0.22, "reasoning": "The photo is a bullfrog sitting in shallow water, seen from the side. Its head is a broad, blunt wedge, and its round eye and tympanum dominate. That is closer to the \"round\" pole than to the V-shaped, pointed faces (fox, cat and dog snouts, car fronts) that drive this neuron. There is no clear frontal V-shaped face or converging pointed structure, so I expect weak activation, though the somewhat tapered snout could give a modest response.\n\n22"}
{"prompt": "circuits+nick", "gid": 20157, "imagenet_class": "bison", "frac_of_max": 0.0438, "on": false, "p_on": 0.12, "reasoning": "The main subject is a small, distant bison partly hidden behind a pine tree in an open meadow. A bison's head is blunt, broad and rounded, not the narrow pointed V of a fox or dog snout, a cat face, or a car hood. There is also no flame, martini-glass or wedge-shaped object. The only faintly pointed form is the small conifer, which is unlikely to drive a mixed4e unit strongly, so the neuron is probably OFF.\n\n12"}
{"prompt": "circuits+nick", "gid": 34785, "imagenet_class": "fiddler crab", "frac_of_max": 0.0458, "on": false, "p_on": 0.22, "reasoning": "The photo shows a small orange crab on a white, bubbly background, with no cat or dog face, car front, or other clear V-shaped, pointed structure for this neuron to respond to. The crab's carapace is rounded and its legs splay outward rather than converging to a point, and the bubbles add more round shapes, which push the neuron negative. The spiky legs might give a weak partial response, but nothing here resembles the snout or grille V that drives it strongly.\n\n22"}
{"prompt": "circuits+nick", "gid": 20990, "imagenet_class": "obelisk", "frac_of_max": 0.0412, "on": false, "p_on": 0.12, "reasoning": "The photo shows a tall, rectangular obelisk shaft and blocky stone ruins against a plain sky. The obelisk's pointed pyramidion, the one tapering element that might drive this neuron, is cropped out of the frame. What remains is straight vertical edges and squarish blocks, with no V-shaped convergence like a snout, a car front, a flame or a glass. Since neither animal faces nor car-like fronts are present, the neuron should stay weak.\n\n12"}
{"prompt": "circuits+nick", "gid": 31210, "imagenet_class": "milk can", "frac_of_max": 0.0496, "on": false, "p_on": 0.18, "reasoning": "This scene is dominated by rounded, cylindrical shapes: stacked milk cans, a woven basket and a round flowerpot. Those are the \"round\" pattern that pushes the neuron negative. There are no animal faces, snouts or car or train fronts with the converging V-shape that drives it. The only weakly pointed elements are spiky plant leaves, a red flower spike and a small pig figurine, which might give a modest local response but are unlikely to reach 30% of maximum.\n\n18"}
{"prompt": "circuits+nick", "gid": 49264, "imagenet_class": "ram", "frac_of_max": 0.0454, "on": false, "p_on": 0.28, "reasoning": "The main object is a sheep standing side-on, with a small head in profile at the top right. The neuron responds most to frontal, V-shaped, pointed forms, like fox, cat and dog faces, the fronts of cars, or flames. The sheep's tapered muzzle and ears offer only a weak, small pointed cue. The large rounded woolly body and the horizontal fence rails don't fit the V-shape pattern, so a strong activation seems unlikely.\n\n28"}
{"prompt": "circuits+nick", "gid": 16111, "imagenet_class": "American Staffordshire terrier", "frac_of_max": 0.0362, "on": false, "p_on": 0.68, "reasoning": "The large, close-up dog face is what matters: its muzzle tapers toward the nose, with the black-and-tan markings forming a V around the eyes and snout. That is the kind of pointed, fox- or wolf-like facial geometry this neuron's inputs (4d:493 fox snout, 4d:477 wolf face) detect. The face is somewhat broad and tilted, and the ears flop rather than stand up, so it is less sharply V-shaped than a fox or cat. Still, it is not a flat, round, mastiff-type face, so the neuron is likely to fire.\n\n68"}
{"prompt": "circuits+nick", "gid": 6358, "imagenet_class": "skunk", "frac_of_max": 0.3961, "on": true, "p_on": 0.72, "reasoning": "The skunk's face is front-on, and the white blaze narrows down its forehead to a tapering snout. That makes a downward-pointing V framed by black fur, much like the fox, wolf and dog faces that drive this neuron. Front-facing mammal faces with pointed muzzles are among its strongest triggers. The skunk's head is somewhat rounded and fluffy, which could weaken the response, but the clear V-shaped stripe and snout should still push it above threshold.\n\n72"}
{"prompt": "circuits+nick", "gid": 1769, "imagenet_class": "Pomeranian", "frac_of_max": 0.3492, "on": true, "p_on": 0.85, "reasoning": "Both dogs are Pomeranians with fox-like faces: pointed ears and tapered snouts form exactly the V shape this neuron responds to. Its strongest inputs are fox-snout and wolf-face detectors, and the cream dog facing the camera in the foreground is especially fox-like. The faces are fairly small in the frame, but the pointed features are clear and close to the neuron's preferred stimuli, so a strong response is likely.\n\n85"}
{"prompt": "circuits+nick", "gid": 8843, "imagenet_class": "otter", "frac_of_max": 0.4782, "on": true, "p_on": 0.28, "reasoning": "The otter's face is the only candidate driver. Under the V-shape reading, a pointed, tapering snout (fox, wolf, cat) drives this neuron, while round, blunt faces (bulldog, mastiff) push it negative. An otter's head is broad and rounded, with a flat, blunt muzzle and small ears, so it sits closer to the round pole than to a V-shaped fox face. The branches at the top form weak V-like angles, but they are small and not object-like, so I expect only a weak response.\n\n28"}
{"prompt": "circuits+nick", "gid": 6063, "imagenet_class": "red fox", "frac_of_max": 0.583, "on": true, "p_on": 0.88, "reasoning": "The photo shows a red fox sitting with its face toward the camera. Its tapering, V-shaped snout and pointed ears are exactly the pattern this neuron responds to. Its strongest input is the 4d:493 fox-snout detector, and fox faces are among its canonical drivers. The face is fairly small in the frame, but it is clear and frontal, so the neuron should respond strongly.\n\n88"}
{"prompt": "circuits+nick", "gid": 15627, "imagenet_class": "red wolf", "frac_of_max": 0.3154, "on": true, "p_on": 0.9, "reasoning": "The maned wolf's face is essentially a fox face: a tapering snout and two large upright ears that form a strong V-shaped, pointed head silhouette. That pattern matches this neuron's strongest inputs (fox snout 4d:493 and wolf face 4d:477), and a pointed canid face is its classic driver. The face is large, frontal and well lit, so it should push the neuron's maximum response high. Nothing round dominates the image to offset it.\n\n90"}
{"prompt": "circuits+nick", "gid": 8886, "imagenet_class": "hamster", "frac_of_max": 0.5317, "on": true, "p_on": 0.4, "reasoning": "The key region is the hamster's face: it has two upright ears and a small tapered snout, a weak version of the pointed, V-shaped face (cat or fox) this neuron favours. But a hamster's head is mostly round and chubby, and round shapes push the neuron negative. The large round bowl adds more of that \"round\" pole. So the face may give a moderate pointed-face response, but probably weaker than a cat or fox would.\n\n40"}
{"prompt": "circuits+nick", "gid": 36173, "imagenet_class": "lynx", "frac_of_max": 0.4185, "on": true, "p_on": 0.85, "reasoning": "The lynx is a cat with a pointed, V-shaped head: tufted, sharply pointed ears and a tapering snout seen in three-quarter profile. That is very close to the fox and cat faces that drive this neuron, and it matches the fox-snout and wolf-face inputs feeding it. Nothing round dominates the frame, and the face is large and clearly visible, so a strong response is likely.\n\n85"}
{"prompt": "circuits+nick", "gid": 24671, "imagenet_class": "trench coat", "frac_of_max": 0.0417, "on": false, "p_on": 0.35, "reasoning": "The main candidate is the woman's face: her long dark hair frames it into a narrowing V that ends at a pointed chin, and her coat collar and lapels form another V below. The aviator sunglasses add a symmetric, pointed, face-like layout. But this neuron's strongest drivers are animal snouts and car or train fronts, and human faces are flatter and rounder, so the response may stay weak. The background building facade and dry grass offer little pointed structure.\n\n35"}
{"prompt": "circuits+nick", "gid": 16656, "imagenet_class": "macaw", "frac_of_max": 0.0482, "on": false, "p_on": 0.4, "reasoning": "The key feature is the macaw's head in close-up. Its hooked beak tapers to a downward point, which loosely matches the pointed, V-shaped geometry this neuron favors. The striped white face patch also gives the kind of textured animal-face cue the fox and cat inputs carry. Against that, the overall head and beak are strongly curved and rounded, more hook than V, and bird faces lack the triangular, ears-and-snout shape of fox and cat faces. The rounded parts could pull the response down, so I expect only a moderate response.\n\n40"}
{"prompt": "circuits+nick", "gid": 38105, "imagenet_class": "ballplayer", "frac_of_max": 0.0164, "on": false, "p_on": 0.2, "reasoning": "This is a black-and-white group photo of a vintage baseball team, mostly small, roundish human faces under caps, with uniforms and a dugout behind them. Nothing here matches the neuron's strong drivers: there are no pointed animal snouts, cat or fox faces, car fronts or flame-like V shapes. The only weak V-shaped cues are the jersey collars and the cap brims, and the faces are small and rounded, which would if anything push the neuron down. It probably stays low, though the many repeated textured shapes leave a small chance of moderate activation.\n\n20"}
{"prompt": "circuits+nick", "gid": 17070, "imagenet_class": "Pomeranian", "frac_of_max": 0.3192, "on": true, "p_on": 0.6, "reasoning": "The Pomeranian has a fox-like face, with a small, tapered snout. That pointed muzzle is the kind of feature the neuron's strongest input (4d:493 fox snout) feeds on. Two things work against it: the face is seen in profile rather than straight on as a frontal V, and the puppy's head is a rounded ball of fluff, and round shapes push this neuron negative. Overall, the pointed fox-like muzzle should still give a fairly strong response.\n\n60"}
{"prompt": "circuits+nick", "gid": 39097, "imagenet_class": "red fox", "frac_of_max": 0.5135, "on": true, "p_on": 0.85, "reasoning": "The red fox's face matters most. Its pointed snout and ears make a clear V shape, which is exactly what the neuron's strongest input (4d:493, fox snout) and its overall \"pointed\" tuning respond to. The face is fairly small and the image is a bit blurry, but it is clearly visible and frontal, with no strong round shapes competing. The background of twigs and snow is largely irrelevant.\n\n85"}
{"prompt": "circuits+nick", "gid": 5879, "imagenet_class": "coffee mug", "frac_of_max": 0.045, "on": false, "p_on": 0.1, "reasoning": "The photo is dominated by round shapes: circular candy-coated chocolates, a round cup rim, and polka dots. That is exactly the \"round\" pole that pushes this neuron negative. There are no V-shaped or pointed structures like snouts, cat or fox faces, car hoods, flames, or wedge-shaped fronts to drive it. The handle and the cup's perspective give only weak curved contours, not a sharp converging point, so the neuron is likely OFF.\n\n10"}
{"prompt": "circuits+nick", "gid": 37942, "imagenet_class": "common iguana", "frac_of_max": 0.0346, "on": false, "p_on": 0.27, "reasoning": "What matters is whether there is a strongly pointed, V-shaped form at a useful scale, ideally a frontal tapering face like a fox or cat or the wedge front of a vehicle. Here the iguana is fairly small and seen in profile, partly hidden by leaves. Its head is somewhat wedge-shaped, but it isn't a symmetric frontal V. The forking branches make some V-like angles, but they are thin, cluttered texture rather than a solid pointed shape, so a strong response seems unlikely.\n\n27"}
{"prompt": "circuits+nick", "gid": 11868, "imagenet_class": "beach wagon", "frac_of_max": 0.3832, "on": true, "p_on": 0.42, "reasoning": "What matters here is the classic station wagon, seen almost purely from the side. The neuron is known to fire on cars, but mainly on front views, where the grille and hood form a pointed V shape. This photo instead shows a long horizontal profile, with the front only slightly visible at the left. The large round whitewall wheels are just the kind of round shape that pushes it negative, so a car-related response is plausible but probably weaker than on a head-on view.\n\n42"}
{"prompt": "circuits+nick", "gid": 48083, "imagenet_class": "papillon", "frac_of_max": 0.4873, "on": true, "p_on": 0.77, "reasoning": "The photo shows a papillon's face, with a narrow pointed muzzle, a white blaze running down the middle, and large ears flaring outward. Together these form a strong fox-like V shape, which is exactly the \"pointed face\" pattern this neuron's fox-snout and wolf-face inputs pick up. The dog's face fills much of the frame and is front-facing, which should drive the neuron well. The dark lighting and the green toy in its mouth may weaken the response somewhat, but the face shape itself is not round.\n\n77"}
{"prompt": "circuits+nick", "gid": 8992, "imagenet_class": "pickup", "frac_of_max": 0.5724, "on": true, "p_on": 0.72, "reasoning": "The photo shows a vintage pickup truck in front three-quarter view, with the grille, hood and headlights clearly visible. Car and van fronts are among this neuron's classic top activators, fed by the 4d \"van front\" input. The way the hood and fenders taper toward the grille gives something of the pointed, V-like front-end shape it responds to. The boxy cab and round headlights are less ideal, but a clear vehicle front like this usually drives it well.\n\n72"}
{"prompt": "circuits+nick", "gid": 10788, "imagenet_class": "badger", "frac_of_max": 0.3619, "on": true, "p_on": 0.8, "reasoning": "The badger's face is a strongly pointed, V-shaped form: the black-and-white stripes converge to a tapered snout, much like the fox and wolf faces that feed this neuron. That convergent, pointed facial geometry is exactly what 4e:55 responds to. The bluebells and grass are mostly irrelevant clutter, and the head is large and clearly visible.\n\n80"}
{"prompt": "circuits+nick", "gid": 986, "imagenet_class": "bagel", "frac_of_max": 0.0059, "on": false, "p_on": 0.1, "reasoning": "The photo is a stack of round, domed raisin cookies on a plain white background. Their soft, rounded contours are exactly the \"round\" pole that pushes this neuron negative. Nothing here forms a pointed V-shape: no snout, grille, flame tip, or tapered wedge. At most, the gaps between the stacked cookies form faint converging lines, which should give only a weak response, so the neuron likely stays OFF.\n\n10"}
{"prompt": "circuits+nick", "gid": 20407, "imagenet_class": "tile roof", "frac_of_max": 0.0381, "on": false, "p_on": 0.18, "reasoning": "This photo shows a dense, repeating texture of barrel roof tiles. Each tile has rounded, curved ends rather than a pointed V-shape, and there is no face, car front, or flame-like converging form for the neuron's V-shape detectors to pick up. The tiles taper slightly, which could give a weak hint of convergence, but the dominant geometry is round and repetitive, which leans toward the neuron's negative \"round\" pole. It will most likely stay OFF.\n\n18"}
{"prompt": "circuits+nick", "gid": 23118, "imagenet_class": "magnetic compass", "frac_of_max": 0.0356, "on": false, "p_on": 0.12, "reasoning": "The dominant feature is the round compass dial (a circular bezel with a face, much like the clock example in the negative pole), set on a plain rectangular baseplate against white. The only pointed shapes are the thin needle and the small printed arrows, which are tiny, flat line markings rather than the V-shaped snouts, grilles or flames that drive the neuron. There are no animal faces or vehicle fronts, so the neuron should respond weakly or be pushed negative.\n\n12"}
{"prompt": "circuits+nick", "gid": 16362, "imagenet_class": "knot", "frac_of_max": 0.0386, "on": false, "p_on": 0.18, "reasoning": "The photo is a grayscale close-up of braided rope looped through a round metal ring. The ring is a strongly round, O-like form, which is the kind of shape that pushes this neuron negative. There is no pointed V-shaped object here, such as an animal face with a snout, a car or train front, or a flame. The braid's small chevron (herringbone) texture gives only a weak, texture-level V cue, which probably isn't enough to drive a 4e-level detector, so the neuron is most likely OFF.\n\n18"}
{"prompt": "circuits+nick", "gid": 35642, "imagenet_class": "beach wagon", "frac_of_max": 0.438, "on": true, "p_on": 0.35, "reasoning": "The car is shown in a flat side profile, so the neuron's preferred cue is weak here. It responds to pointed, V-converging shapes such as car fronts and hoods seen head-on or at a three-quarter angle, which form a snout-like taper. This long boxy station wagon has no tapering front, and its prominent round wheels are the kind of rounded shape that tends to push the neuron negative. Car-related features may still give some drive, but a side-view sedan or wagon is a weaker match than a grille-and-hood view.\n\n35"}
{"prompt": "circuits+nick", "gid": 18383, "imagenet_class": "lesser panda", "frac_of_max": 0.3842, "on": true, "p_on": 0.8, "reasoning": "The red panda's face is the key feature: pointed triangular ears, white V-shaped markings and a tapering, fox-like snout. This is very close to the fox and cat faces that drive 4e:55 via inputs like 4d:493 (fox snout). The face is fairly small and turned sideways, but its pointed V-structure should still produce a strong response somewhere in the image. The rounded body and branch don't matter much, since the neuron takes its maximum over locations.\n\n80"}
{"prompt": "circuits+nick", "gid": 47062, "imagenet_class": "lynx", "frac_of_max": 0.4063, "on": true, "p_on": 0.8, "reasoning": "The tabby cat's face is the key region: cat faces are among the strongest classic drivers of 4e:55, because the tapered muzzle, nose and eye markings form the converging V-shape this neuron detects. The curled-back ears (an American Curl) make the head outline somewhat rounder than a typical pointed-ear cat, which could weaken the response. Even so, the face is frontal and clear, with a strong tabby \"M\" and nose V, so a solid activation is likely.\n\n80"}
{"prompt": "circuits+nick", "gid": 40417, "imagenet_class": "projector", "frac_of_max": 0.3032, "on": true, "p_on": 0.32, "reasoning": "The projector is a boxy white object, and its most prominent feature is a large, round lens, the kind of circular shape that pushes this neuron negative. Its front does loosely resemble a vehicle face, with a grille-like vent and a lens like a headlight, and car fronts are one of the neuron's inputs. But nothing in the image forms a clear pointed or V-shaped contour, so any activation would come from that weak car-face resemblance and would likely be modest.\n\n32"}
{"prompt": "circuits+nick", "gid": 15060, "imagenet_class": "minivan", "frac_of_max": 0.4218, "on": true, "p_on": 0.52, "reasoning": "The main thing here is the car itself. 4e:55 historically fires on cars, but mostly through their pointed, converging front ends: hoods and grilles narrowing toward the viewer, like the red pickup and the bullet train in the figure. This Fiat is seen from the rear three-quarter angle and is boxy and rounded, with no strong V-shaped front, which weakens the case. Still, the car body, sloping rear window and angled panel edges may give enough car-like converging geometry for a moderate response.\n\n52"}
{"prompt": "circuits+nick", "gid": 15193, "imagenet_class": "tow truck", "frac_of_max": 0.3875, "on": true, "p_on": 0.48, "reasoning": "The photo's main content is a white boxy hatchback seen from the rear, sitting on a flatbed tow truck at night. Car bodies are a classic trigger for 4e:55, but its car responses lean toward front ends and tapering, pointed shapes like hoods, grilles and bullet-train noses. This squared-off rear view offers little of that V-shaped convergence. Still, the car structure, the angled flatbed and the bright taillights could produce a moderate response, so the outcome is genuinely uncertain.\n\n48"}
{"prompt": "circuits+nick", "gid": 46927, "imagenet_class": "Egyptian cat", "frac_of_max": 0.4017, "on": true, "p_on": 0.85, "reasoning": "The tabby cat's face on the right is the key element: its pointed ear and wedge-shaped, tapering snout form exactly the V-shaped \"pointed\" configuration this neuron responds to, and cat faces are among its classic top activators. The two legs converging toward the feet also form a rough V, which may add some drive. The face is cropped and seen at an angle, which could weaken the response somewhat, but this kind of close-up cat face usually activates the neuron strongly.\n\n85"}
{"prompt": "circuits+nick", "gid": 1251, "imagenet_class": "pickup", "frac_of_max": 0.3619, "on": true, "p_on": 0.8, "reasoning": "The photo shows an old pickup truck in three-quarter front view, with a prominent hood, grille and headlights. Car fronts are the classic trigger for 4e:55; the essay reads them as tapered, V-like structures, and the \"car\" and \"van front\" examples match this image closely. The hood and grille, seen in perspective, form the converging pointed shape the neuron favors. The sepia tone and the blocky front of the vintage truck may weaken the response slightly, but it is still very likely strong.\n\n80"}
{"prompt": "circuits+nick", "gid": 27286, "imagenet_class": "brass", "frac_of_max": 0.0204, "on": false, "p_on": 0.08, "reasoning": "This is a flat, rectangular black memorial plaque covered in white text. It has no animal faces, snouts, car or train fronts, flames, or other pointed V-shaped objects that drive this neuron. The only candidate features are tiny letterforms, such as \"V\" and \"W\" in \"Living\" and \"workers\", and the plaque's square corners. These are too small and too generic to strongly excite a mixed4e shape detector. The neuron is most likely OFF.\n\n8"}
{"prompt": "circuits+nick", "gid": 36396, "imagenet_class": "basenji", "frac_of_max": 0.3134, "on": true, "p_on": 0.8, "reasoning": "The main feature is the mother dog's head: a terrier/chihuahua-type face with large upright pointed ears and a tapered snout. That makes a fox-like V-shaped face, which is the kind of input (fox snout, wolf face) that drives this neuron most strongly. The rounded puppies and soft blankets are not pointed, but the neuron reports its strongest response anywhere in the image, so the sharp, clearly visible face should dominate.\n\n80"}
{"prompt": "circuits+nick", "gid": 6115, "imagenet_class": "beach wagon", "frac_of_max": 0.3035, "on": true, "p_on": 0.6, "reasoning": "The photo is a low-angle, three-quarter view of an old car, and car fronts are among this neuron's classic strong triggers. The angled front corner, where the hood and fender converge, gives the kind of pointed, V-like structure the neuron favors. Working against that, the prominent round headlight is exactly the \"round\" pattern that pushes it negative. There is also no grille-dominated frontal view like the strongest car examples, so this is likely ON, but not a confident call.\n\n60"}
{"prompt": "circuits+nick", "gid": 12179, "imagenet_class": "apron", "frac_of_max": 0.0408, "on": false, "p_on": 0.2, "reasoning": "This is a patchwork apron with busy floral fabric, so there is no face, snout, vehicle front or flame for the neuron to latch onto. The only candidate pointed structure is the bib, whose sides taper toward the top in a loose trapezoid, but it is broad and blunt rather than a sharp V. The many round flower motifs and the texture-dominated scene lean toward the neuron's \"round\" negative pole, so it is probably off.\n\n20"}
{"prompt": "circuits+nick", "gid": 38915, "imagenet_class": "racer", "frac_of_max": 0.323, "on": true, "p_on": 0.85, "reasoning": "The front end of this Mitsubishi Evo is exactly the kind of input this neuron responds to. It has angular, swept-back headlights and a hood that converges toward the grille, forming a strong pointed, V-shaped front. That matches the car-front/\"pointed\" pattern that drives 4e:55, as with the car and bullet-train examples. The lighting is dramatic and the car fills the frame, and there are no competing round shapes dominating, so a strong response is likely.\n\n85"}
{"prompt": "circuits+nick", "gid": 12719, "imagenet_class": "letter opener", "frac_of_max": 0.0356, "on": false, "p_on": 0.33, "reasoning": "The letter opener's blade tapers to a point, which is the kind of pointed, V-like geometry this neuron favors. But the point is a thin, horizontal sliver, not a broad V-shaped face, snout, flame or car front. Most of the image is a collage of flat elongated metal, text and a small figurine, with little strong V-structure, so a weak or moderate response seems more likely than a strong one.\n\n33"}
{"prompt": "circuits+nick", "gid": 38311, "imagenet_class": "hog", "frac_of_max": 0.038, "on": false, "p_on": 0.58, "reasoning": "The pig's face is shown head-on, and its large triangular ears flare outward to form a pointed V-shaped silhouette around the head. That is the frontal \"pointed face\" pattern that drives this neuron on foxes, cats and dogs. Working against it, the snout is a big round disc in the middle of the face, and round shapes push the neuron negative, much like the flat round faces of the bulldog and mastiff in the figure. The ears probably win out enough to give a moderate response, but the case is uncertain.\n\n58"}
{"prompt": "circuits+nick", "gid": 33287, "imagenet_class": "Samoyed", "frac_of_max": 0.3342, "on": true, "p_on": 0.75, "reasoning": "The white puppy (a Samoyed-like dog) is the key element. Its face is fox- or wolf-like: a tapered snout, a V-shaped muzzle with the tongue out, and small triangular ears. That matches the \"pointed\" V-shape the neuron favors and its strong fox-snout and wolf-face inputs. The face is fairly small and the fluffy fur rounds it out somewhat, but the puppy should still drive the neuron well. The TV, table and remote are mostly irrelevant boxy shapes.\n\n75"}
{"prompt": "circuits+nick", "gid": 84, "imagenet_class": "limousine", "frac_of_max": 0.45, "on": true, "p_on": 0.55, "reasoning": "The main thing here is the white limousine. Cars are among this neuron's classic triggers, and in the essay's reading what drives it are tapering, angular body shapes such as hoods and wedge-like car fronts. This photo shows the rear and side, so the strongest pointed-front cue is absent. Still, the long angular body and trunk lines could give a moderate car-shape response, while the house in the background adds little.\n\n55"}
{"prompt": "circuits+nick", "gid": 23018, "imagenet_class": "sunglasses", "frac_of_max": 0.0262, "on": false, "p_on": 0.25, "reasoning": "The photo is a close-up of a man's face in profile with dark sunglasses, curly hair and a short beard, set against a dark background. Nothing here is a clear V or pointed shape of the kind that drives this neuron: there is no tapered snout, triangular ears, wedge-shaped car or train front, or flame. The goatee, the sunglasses and the angle of the face give only weak hints of a converging, pointed contour. Human faces are fairly rounded and are not among its typical strong drivers, so the response is probably modest.\n\n25"}
{"prompt": "circuits+nick", "gid": 28315, "imagenet_class": "cougar", "frac_of_max": 0.4439, "on": true, "p_on": 0.85, "reasoning": "The photo is a frontal close-up of a cougar's face, and big-cat and cat faces are among this neuron's classic top activators. The feature that matters is the V-shaped geometry: the brow, the muzzle tapering down to the nose, and the white chin patch form a pointed shape centered in the frame, which is what this neuron responds to. Nothing large and round dominates the image to suppress it, though the cougar's broad, fairly blunt muzzle is somewhat less pointed than a fox's.\n\n85"}
{"prompt": "circuits+nick", "gid": 12905, "imagenet_class": "eft", "frac_of_max": 0.0461, "on": false, "p_on": 0.42, "reasoning": "The key feature is the small newt or salamander head facing the camera. Its two eyes sit above a snout that tapers to a rough triangle, a weak version of the frontal, pointed, V-shaped face that drives this neuron in foxes, cats and car fronts. But the head is tiny in the frame and its snout is fairly blunt and rounded. It also sits among smooth, round rocks, which pull toward the \"round\" negative pole, so any activation is likely modest.\n\n42"}
{"prompt": "circuits+nick", "gid": 6322, "imagenet_class": "grey fox", "frac_of_max": 0.4477, "on": true, "p_on": 0.92, "reasoning": "The photo shows a gray fox face-on, peeking from a tree. Its pointed ears and tapering V-shaped snout are exactly the shape that drives this neuron. Its strongest input is a fox-snout detector (4d:493), and fox faces are among its top activators. The frontal, clearly visible face at a decent size makes a strong response likely.\n\n92"}
{"prompt": "circuits+nick", "gid": 769, "imagenet_class": "wombat", "frac_of_max": 0.0404, "on": false, "p_on": 0.18, "reasoning": "This scene has a wombat seen from behind, another wombat nosing the ground, a plush teddy bear and rocks, and nearly all of these shapes are rounded and blunt. Wombat bodies are barrel-shaped and their snouts are broad. The teddy bear's face is a classic round, muzzle-forward shape, the \"round\" pole that pushes this neuron negative. The only weak V-ish cues are the wombat's small ears and the angled snout in profile, so there is no clear pointed, fox-like or grille-like V for the neuron to lock onto.\n\n18"}
{"prompt": "circuits+nick", "gid": 44791, "imagenet_class": "bee", "frac_of_max": 0.0489, "on": false, "p_on": 0.22, "reasoning": "The main elements are a fuzzy, rounded bumblebee sitting on a rounded fingertip. Both are blob-like, convex shapes, which resemble this neuron's negative \"round\" pole more than its preferred pointed, V-shaped forms such as snouts, tapering car fronts, flames or martini glasses. The bee's antennae and splayed legs add only small, weak angular cues, and there is no clear tapering face or wedge silhouette. So the neuron is likely only weakly driven at best.\n\n22"}
{"prompt": "circuits+nick", "gid": 40298, "imagenet_class": "beach wagon", "frac_of_max": 0.5328, "on": true, "p_on": 0.8, "reasoning": "The car's front end matters most: a vintage saloon seen at an angle, with a long hood that narrows toward the grille and headlights. That converging, pointed front is exactly the kind of car front this neuron responds to strongly, and car fronts were one half of its famous \"cat and car\" grid. The second orange car behind it adds another front-end view. Nothing large and round in the scene should strongly suppress it.\n\n80"}
{"prompt": "circuits+nick", "gid": 11076, "imagenet_class": "Pekinese", "frac_of_max": 0.5932, "on": true, "p_on": 0.6, "reasoning": "The sleeping white cat at left has a small, wedge-shaped head with pointed ears, which is the kind of V-shaped, pointed face this neuron responds to. Cat faces are among its classic top activators. The large flat-faced dog has a round, pug-like face, which pushes the neuron negative locally. But the neuron's value is its strongest response anywhere in the image, so the cat's head can still carry it. The cat's face is small and its eyes are closed, which may weaken the response, so this is likely but not certain.\n\n60"}
{"prompt": "circuits+nick", "gid": 43327, "imagenet_class": "bull mastiff", "frac_of_max": 0.0396, "on": false, "p_on": 0.25, "reasoning": "The photo is filled by a broad, jowly mastiff/boxer-type face with a blunt muzzle and droopy ears. That is very close to the round-faced bulldog and mastiff examples the essay shows pushing this neuron negative. There is no tapered snout, pointed ears, or other V-shaped geometry for it to latch onto. Dog faces can sometimes drive it, so it isn't certain, but this one reads as \"round\" rather than \"pointed.\"\n\n25"}
{"prompt": "circuits+nick", "gid": 45118, "imagenet_class": "police van", "frac_of_max": 0.3258, "on": true, "p_on": 0.75, "reasoning": "The front of this van matters most: the hood and windshield slope down to a narrowing, pointed nose seen at a three-quarter angle. That is the vehicle-front geometry that 4d:201 (\"van front\") feeds strongly into this neuron. Car and van fronts are among its classic top activators, consistent with the \"pointed/V-shape\" reading. The boxy rear and the round wheels contribute little or push slightly negative, but the front should dominate the neuron's maximum response.\n\n75"}
{"prompt": "circuits+nick", "gid": 17022, "imagenet_class": "papillon", "frac_of_max": 0.3941, "on": true, "p_on": 0.85, "reasoning": "The papillon's face is front-facing and fox-like. The white blaze and muzzle narrow to a pointed V toward the nose, framed by large flared ears. This is exactly the pointed snout pattern that the fox-snout and wolf-face inputs feed into 4e:55. Nothing round dominates the image to suppress it, so a strong response is likely.\n\n85"}
{"prompt": "circuits+nick", "gid": 40527, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3842, "on": true, "p_on": 0.88, "reasoning": "This Shetland sheepdog (sheltie) has a long, tapering snout and pointed upright ears, so its face forms a strong V shape. That closely matches the fox-snout and wolf-face inputs that drive this neuron. Its features are the opposite of the flat, round faces (bulldog, mastiff) that push it negative. The face is large and centered, so a strong response is likely.\n\n88"}
{"prompt": "circuits+nick", "gid": 21101, "imagenet_class": "coyote", "frac_of_max": 0.3641, "on": true, "p_on": 0.85, "reasoning": "The coyote's face, seen head-on with tall pointed ears and a tapering snout, forms exactly the V-shaped \"pointed\" pattern this neuron responds to. It closely matches its strong fox-snout and wolf-face inputs and its coyote-face reader. The face is fairly small in the frame, which could weaken the response somewhat. But the frontal canid face is the neuron's prototypical trigger, and the grass background is unlikely to suppress it.\n\n85"}
{"prompt": "circuits+nick", "gid": 48176, "imagenet_class": "lesser panda", "frac_of_max": 0.3735, "on": true, "p_on": 0.72, "reasoning": "The red panda's face is fox-like: pointed white-edged ears, a tapering muzzle, and the white-and-rust markings that form a V toward the nose. That is close to what the strongest input (4d:493, fox snout) and the pointed/V-shape direction respond to. The face is small and somewhat rounder than a fox's, and the round head could pull the response down a little. Still, the orange fur, pointed ears and snout make this a likely ON.\n\n72"}
{"prompt": "circuits+nick", "gid": 18148, "imagenet_class": "refrigerator", "frac_of_max": 0.0327, "on": false, "p_on": 0.15, "reasoning": "This photo shows a boxy white refrigerator against tiled walls, with some bottles on the floor. It has no animal snouts or faces, no car or train fronts, and no flames or other strongly V-shaped or pointed forms of the kind that drive this neuron. The rectangular fridge and grid tiles give mostly straight, parallel edges rather than converging V shapes. The room corner and the tops of the bottles might give weak pointed cues, but probably not enough to reach 30% of the neuron's maximum.\n\n15"}
{"prompt": "circuits+nick", "gid": 35395, "imagenet_class": "ambulance", "frac_of_max": 0.3753, "on": true, "p_on": 0.8, "reasoning": "The photo shows a red-and-white ambulance (a Mercedes Sprinter-type van) in a three-quarter front view. Its windshield, hood and grille form exactly the kind of tapered, pointed vehicle front this neuron responds to. That front is a direct match for one of its strongest inputs, the van-front detector 4d:201. Car and van fronts are also among its classic top activators, and the sloped hood and angled body stripes add V-like structure, so a strong response is likely.\n\n80"}
{"prompt": "circuits+nick", "gid": 35276, "imagenet_class": "hippopotamus", "frac_of_max": 0.048, "on": false, "p_on": 0.15, "reasoning": "The main subject is a hippo seen underwater through aquarium glass. Its head and body are broad, blunt and rounded, with no V-shaped taper, much like the bulldog and mastiff faces that push this neuron negative. Nothing else in the frame, such as the murky water, the glass edge, or the small figures at the bottom, offers a clear pointed or V-shaped form like a fox snout, a car front, or a flame. The neuron likely stays low, though the hippo's slightly tapering muzzle gives a small chance of a modest response.\n\n15"}
{"prompt": "circuits+nick", "gid": 45778, "imagenet_class": "barn", "frac_of_max": 0.0471, "on": false, "p_on": 0.33, "reasoning": "The photo shows a red barn with several gabled roofs and a small cupola. Their peaks form pointed, upward \u039b shapes against the sky, which could weakly drive a \"pointedness\" detector. However, this neuron's strongest drivers are downward-pointing, face-like or front-facing V shapes: fox and cat snouts, car and van fronts, flames, martini glasses. A barn is a broad, flat-faced structure that resembles none of these, and inverted roof gables are a less typical match. So some activation is plausible, but strong firing is uncertain.\n\n33"}
{"prompt": "circuits+nick", "gid": 40069, "imagenet_class": "Blenheim spaniel", "frac_of_max": 0.3273, "on": true, "p_on": 0.68, "reasoning": "The frontal dog face is what matters. The white blaze runs from the forehead down to the narrow snout, and the long drooping ears frame the face. Together they make a downward-pointing V, much like the puppy in the neuron's \"pointed\" examples. The face is fairly soft and rounded rather than sharply foxlike, so the response may be moderate rather than strong.\n\n68"}
{"prompt": "circuits+nick", "gid": 24963, "imagenet_class": "rugby ball", "frac_of_max": 0.024, "on": false, "p_on": 0.22, "reasoning": "The photo has no cat, fox or dog faces and no car fronts, the strongest drivers of this pointed \"V-shape\" neuron. The dominant object is the rugby ball, a rounded oval of the kind that pushes the neuron negative. The splayed fingers of the reaching hands and the angled arms do create some pointed, converging shapes, which might give a weak response. The human faces are fairly rounded and frontal rather than snout-like, so overall the neuron probably stays low.\n\n22"}
{"prompt": "circuits+nick", "gid": 31751, "imagenet_class": "ruffed grouse", "frac_of_max": 0.0082, "on": false, "p_on": 0.22, "reasoning": "The only candidate here is the small bird (it looks like a ruffed grouse) in the middle of the lawn. Its fanned tail does form a rough wedge, but its head and dark ruff read as a round blob rather than a pointed snout or V. Everything else is grass and leafy foliage, which offers no strong pointed, converging structure like a fox face, car front or flame. Because the object is small and mostly rounded, I expect only a weak response at best.\n\n22"}
{"prompt": "circuits+nick", "gid": 44206, "imagenet_class": "marmoset", "frac_of_max": 0.3423, "on": true, "p_on": 0.42, "reasoning": "The marmoset's front-facing furry face is the relevant region. It sits in the animal-face territory where this neuron's fox, wolf and cat inputs live, and the white ear tufts and the triangle down to the open mouth give a hint of a V-shaped layout. But the face itself is flat and rounded rather than snouted or pointed, closer to the round, flat-faced dogs that push the neuron negative. So the evidence is mixed, and I lean slightly toward OFF.\n\n42"}
{"prompt": "circuits+nick", "gid": 40057, "imagenet_class": "ruffed grouse", "frac_of_max": 0.0489, "on": false, "p_on": 0.18, "reasoning": "This is a ruffed grouse on leaf litter. Its rounded, mottled body and small head offer none of the strong pointed V shapes that drive 4e:55, such as tapered fox, cat or dog snouts, angled car fronts, or flame tips. The small beak and slight crest are tiny, and the busy camouflage texture is unlikely to form a clean V. The bird's roundish silhouette, if anything, leans toward the negative \"round\" pole.\n\n18"}
{"prompt": "circuits+nick", "gid": 848, "imagenet_class": "polecat", "frac_of_max": 0.5734, "on": true, "p_on": 0.68, "reasoning": "The image is a close-up of a ferret's face. Its snout tapers toward a small pink nose, and the eyes and ears sit above it, giving a roughly V-shaped muzzle like the fox and cat faces that drive this neuron. The ferret's head is fairly rounded and its muzzle is shorter than a fox's, which weakens the \"pointed\" signal somewhat. Still, a frontal furry-mammal face with a converging snout is close to the neuron's preferred stimulus.\n\n68"}
{"prompt": "circuits+nick", "gid": 48467, "imagenet_class": "limousine", "frac_of_max": 0.3936, "on": true, "p_on": 0.75, "reasoning": "The pink limousine is the key element. Its front end, seen at an angle with the hood and grille tapering toward the headlights, is the kind of car front that drives 4e:55 strongly. Car fronts are among this neuron's classic top activators, fed in part by van- and car-front detectors like 4d:201. The rest of the scene (buildings, sky) is mostly flat or rectilinear and contributes little, but the car front alone is likely enough to push the neuron over threshold.\n\n75"}
{"prompt": "circuits+nick", "gid": 12796, "imagenet_class": "ladybug", "frac_of_max": 0.0488, "on": false, "p_on": 0.22, "reasoning": "The main subject is a ladybug: a round, domed orange shell with circular black spots, the kind of rounded \"O\" geometry that pushes this neuron negative. The lifted wing case and unfolding wing do form a wedge-like opening, which could weakly suggest a V. But there's no clear converging snout, flame, car front or tapered point. The round body probably dominates, so a strong response is unlikely.\n\n22"}
{"prompt": "circuits+nick", "gid": 29367, "imagenet_class": "ambulance", "frac_of_max": 0.3334, "on": true, "p_on": 0.85, "reasoning": "The ambulance is a VW Transporter van shown in three-quarter front view. Its sloped hood, angled windshield and tapered nose form exactly the kind of pointed, V-converging front that this neuron responds to. It also closely matches one of the neuron's strongest inputs, 4d:201 (van front), and car fronts are a classic top-activating category for 4e:55. Nothing round dominates the frame to pull the response negative, so it will very likely fire strongly.\n\n85"}
{"prompt": "circuits+nick", "gid": 32959, "imagenet_class": "kit fox", "frac_of_max": 0.436, "on": true, "p_on": 0.92, "reasoning": "The photo shows two red foxes with clearly visible faces. The upper fox's face, with its tapered snout and upright pointed ears, forms the classic V shape this neuron responds to. That V shape is also what drives its strongest input, 4d:493, the fox snout detector. Nothing round dominates the image to push the neuron negative, so it should fire strongly.\n\n92"}
{"prompt": "circuits+nick", "gid": 35426, "imagenet_class": "minibus", "frac_of_max": 0.3949, "on": true, "p_on": 0.85, "reasoning": "The key element is the front of the silver Ford Transit van: its sloped hood, grille and headlights form a tapered, V-like front, which is exactly what the van-front detector 4d:201 feeds into this neuron. Vehicle fronts seen roughly head-on are among this neuron's classic top activators, the \"car\" half of the old cat/car reading. A second van in the background adds another candidate. Nothing large and round in the scene competes with it, so a strong response is likely.\n\n85"}
{"prompt": "circuits+nick", "gid": 32563, "imagenet_class": "minivan", "frac_of_max": 0.3271, "on": true, "p_on": 0.58, "reasoning": "This is a side view of a silver minivan in a parking lot. The front of the van tapers to a sloped, pointed nose at the windshield and hood, which is the kind of V or pointed shape this neuron favors. Cars were among the neuron's classic top activators. However, this side profile shows the boxy body and the round wheels rather than the converging front-end grille view that drives it most strongly. That makes a moderate response plausible but not certain.\n\n58"}
{"prompt": "circuits+nick", "gid": 11287, "imagenet_class": "European fire salamander", "frac_of_max": 0.0428, "on": false, "p_on": 0.14, "reasoning": "This is a close-up of a spotted salamander's glossy black skin. The yellow spots are rounded blobs, which is the \"round\" pole that pushes this neuron negative. Nothing in the frame forms a pointed, converging V: no snout, no muzzle, no tapering front of a vehicle. The wet speckled texture and the curved body outline don't supply the pointed geometry it needs, so it is likely OFF.\n\n14"}
{"prompt": "circuits+nick", "gid": 25322, "imagenet_class": "scuba diver", "frac_of_max": 0.0421, "on": false, "p_on": 0.12, "reasoning": "This underwater scene is mostly a diffuse blue-green reef wall with soft coral textures and a small, distant diver. Nothing in it is a clear V-shaped or pointed form: no snout, ears, car hood, flame or tapering wedge at a scale that would drive this neuron. The textures are irregular and blobby rather than converging to a point, and the diver is too small to form a strong V. So the neuron most likely stays near baseline.\n\n12"}
{"prompt": "circuits+nick", "gid": 44844, "imagenet_class": "bow tie", "frac_of_max": 0.0139, "on": false, "p_on": 0.45, "reasoning": "The suit's lapels and open shirt collar form a strong downward-pointing V around the red bow tie. That is exactly the pointed, converging shape this V-detector seems to like, and since the neuron's value is its strongest response anywhere, that region alone could switch it on. Pulling the other way, the WALL-E-style head is built from big round lens \"eyes\", the kind of circular shapes that push this neuron negative. There is also no real animal snout or car front. So the outcome hinges on whether the lapel V is strong enough on its own, and I'd call it close to even.\n\n45"}
{"prompt": "circuits+nick", "gid": 8117, "imagenet_class": "black widow", "frac_of_max": 0.0429, "on": false, "p_on": 0.2, "reasoning": "The photo is a small spider on a brick wall next to a white surface. Its body is a rounded abdomen, which matches the neuron's \"round\" negative pole. Its legs do form some angled, V-like spokes, but they are thin and spindly rather than a solid pointed shape like a fox snout, cat face or car front. The scene also has no flames, no snouts and no tapered fronts, so a strong response seems unlikely; the most it might get is a weak one from the leg angles.\n\n20"}
{"prompt": "circuits+nick", "gid": 12136, "imagenet_class": "Pekinese", "frac_of_max": 0.3738, "on": true, "p_on": 0.28, "reasoning": "The photo shows two small flat-faced dogs, a Japanese spaniel/Pekingese type, plus a round plush ball. These short-snouted, round faces resemble the bulldog and mastiff examples that push this V-shape neuron negative, not the pointed fox, cat or puppy faces that drive it. The round ball adds more \"round\" signal. Small furry faces with eyes and ears can still partially excite it, so I give it a modest chance of being ON.\n\n28"}
{"prompt": "circuits+nick", "gid": 33451, "imagenet_class": "car wheel", "frac_of_max": 0.4647, "on": true, "p_on": 0.72, "reasoning": "The photo shows a classic sedan seen from the front three-quarter angle, with its grille, headlights and front bumper clearly visible. Its raised hood forms a sharp, angled wedge against the sky. Car fronts are among this neuron's classic top triggers (it reads from a van-front detector), and the open hood adds a pointed, V-like geometry. The car is fairly small in the frame and viewed off-axis, which lowers the response somewhat, but it should still likely clear the threshold.\n\n72"}
{"prompt": "circuits+nick", "gid": 33704, "imagenet_class": "Persian cat", "frac_of_max": 0.5155, "on": true, "p_on": 0.38, "reasoning": "The relevant feature is the cat's face. Cat faces usually drive this neuron through the V-shaped arrangement of pointed ears converging toward the muzzle. This Persian, however, has a flat, round face with small, tucked-in ears. That makes it closer to the round, pug-like faces (bulldog, mastiff) that push the neuron negative. The calico patches around the eyes form a faint V, and it is still a cat face, so some activation is plausible, but the overall roundness works against a strong response.\n\n38"}
{"prompt": "circuits+nick", "gid": 36759, "imagenet_class": "bib", "frac_of_max": 0.0449, "on": false, "p_on": 0.22, "reasoning": "The photo is a product collage: patterned baby-bib fabric with cartoon prints, a terry-cloth bib, a black card, a cartoon mermaid, and a baby's face. If this is a \"pointed/V-shape\" detector, the things that would drive it are tapered snouts, pointed ears, or V-shaped fronts. Little here clearly has that: the baby's face and the cartoon characters are mostly round, which should push it negative. The only plausible triggers are small cartoon animal faces in the fabric print (possibly cat- or fox-like with pointed ears) and some folded fabric corners, which could give weak, small-scale activation.\n\n22"}
{"prompt": "circuits+nick", "gid": 27630, "imagenet_class": "broccoli", "frac_of_max": 0.0169, "on": false, "p_on": 0.08, "reasoning": "The photo shows only broccoli: rounded, bumpy, dome-shaped florets on a plain white background. There is nothing pointed or V-shaped, such as a snout, ear, car front, flame or martini-glass wedge. The round, textured clusters look more like the neuron's negative \"round\" pole. So this image most likely gives a weak response.\n\n8"}
{"prompt": "circuits+nick", "gid": 7343, "imagenet_class": "Tibetan terrier", "frac_of_max": 0.0374, "on": false, "p_on": 0.38, "reasoning": "The key feature is the dog's face, seen head-on as it runs toward the camera. Frontal dog faces can drive this neuron when they form a pointed, V-shaped snout-and-ear outline. This shaggy dog's floppy ears, rounded fluffy head and open mouth make the face less V-shaped, and the face fills only a small part of the frame. So a dog face is present, but it is a weak match for the pointed shape this neuron prefers.\n\n38"}
{"prompt": "circuits+nick", "gid": 36938, "imagenet_class": "leatherback turtle", "frac_of_max": 0.0438, "on": false, "p_on": 0.22, "reasoning": "The photo shows leatherback turtle hatchlings crawling over wet sand. There are no cat or fox faces, car fronts, flames or other strong converging V-shapes, which are what drive this neuron. The hatchlings' teardrop shells and outstretched flippers make a weak, small-scale pointed or arrow-like silhouette, but the shells are mostly rounded domes, and round shapes push the neuron negative. The low-contrast beach texture adds little, so I expect at most a weak response.\n\n22"}
{"prompt": "circuits+nick", "gid": 15501, "imagenet_class": "Egyptian cat", "frac_of_max": 0.4713, "on": true, "p_on": 0.9, "reasoning": "This is a close-up, frontal cat face: pointed ears and a face that tapers into a V toward the nose and chin. That is exactly the kind of pointed, fox-like facial geometry that drives 4e:55, and cat faces are among its classic top activators. Nothing large and round dominates the frame to suppress it, so a strong response is likely.\n\n90"}
{"prompt": "circuits+nick", "gid": 11351, "imagenet_class": "spider monkey", "frac_of_max": 0.0262, "on": false, "p_on": 0.38, "reasoning": "The spider monkey's face is fairly round and flat rather than snout-like, so the animal itself offers little of the pointed, V-shaped front (fox or wolf snout, car hood) that drives this neuron. The chain-link fence does supply many diamond and V-shaped corners across the image. However, these are small, repetitive textures rather than a single object-scale converging point, which is likely what a mixed4e neuron prefers. The fence could give a moderate response, but a strong one is uncertain.\n\n38"}
{"prompt": "circuits+nick", "gid": 13160, "imagenet_class": "brambling", "frac_of_max": 0.3329, "on": true, "p_on": 0.22, "reasoning": "The photo shows a brambling perched on a branch. Its body is a plump, rounded orange-and-white shape, the kind of roundness that pushes this neuron negative. The only pointed features are the small beak and the thin tail and branch tips, which are tiny compared with the broad V-shaped fox snouts, car fronts and flames that drive it. Without a strong V-shaped face or front-on pointed structure, the neuron likely stays weak, though the beak and wedge-shaped head give it a small chance of firing.\n\n22"}
{"prompt": "circuits+nick", "gid": 6728, "imagenet_class": "tabby", "frac_of_max": 0.6673, "on": true, "p_on": 0.9, "reasoning": "The photo shows a frontal tabby cat face. Its pointed ears and tapering V-shaped face, with the white V-shaped muzzle and chest blaze, are exactly the pointed/V geometry this neuron responds to. Cat faces were among its classic top activators, and the cat is large and centered. Nothing round dominates the scene in a way that would push it negative.\n\n90"}
{"prompt": "circuits+nick", "gid": 11210, "imagenet_class": "dining table", "frac_of_max": 0.3583, "on": true, "p_on": 0.38, "reasoning": "What matters is whether the scene contains a strong pointed or V-shaped form, since this neuron responds to pointed shapes rather than to cats or cars specifically. Here there are no animal faces or vehicle fronts. The main candidate is the dining table's top seen in perspective, whose receding edges and corner form a wedge, much like the pool table example that drove the neuron. The chairs' rounded backs and the round vase work against it, so a corner-of-table V may give only a moderate response.\n\n38"}
{"prompt": "circuits+nick", "gid": 16060, "imagenet_class": "menu", "frac_of_max": 0.0315, "on": false, "p_on": 0.08, "reasoning": "This photo is a blurry close-up of a printed menu: rows of text and prices on a white page, with a sliver of a table at the bottom. Nothing in it has the pointed, V-shaped convergence (snouts, car hoods, flame tips) that drives 4e:55. Small text and flat paper texture give mid-level vision little object-like structure, so the neuron likely stays near baseline.\n\n8"}
{"prompt": "circuits+nick", "gid": 36489, "imagenet_class": "car wheel", "frac_of_max": 0.3228, "on": true, "p_on": 0.48, "reasoning": "This is a white model car seen from the rear three-quarter angle against a plain white background. The neuron's car responses come mostly from angular, pointed shapes, such as tapered hoods, grilles and wedge-like fronts that converge to a point. Here the front is hidden, but the rear does have sharp angular elements: the tall wing, the angled taillights and the tapering roofline. The rounded wheels and the small, isolated object give it less to work with, so this is a borderline case.\n\n48"}
{"prompt": "circuits+nick", "gid": 23727, "imagenet_class": "puffer", "frac_of_max": 0.0397, "on": false, "p_on": 0.15, "reasoning": "The photo is a close-up of a pufferfish's face. Its head is blunt, bulbous and rounded, with no V-shaped snout, pointed ears or tapering front. That rounded geometry matches this neuron's negative \"round\" pole (like the golf ball, tennis ball or pug-faced dogs), not the pointed V-shapes that drive it. The busy spotted texture and the eye might weakly engage face-like inputs, but there is nothing pointed for it to latch onto.\n\n15"}
{"prompt": "circuits+nick", "gid": 5726, "imagenet_class": "barracouta", "frac_of_max": 0.0365, "on": false, "p_on": 0.45, "reasoning": "The photo shows a row of speckled trout laid side by side. Their heads taper to pointed snouts at the top and their forked tails form V shapes at the bottom, which matches the neuron's \"pointed/V-shape\" preference. However, the neuron's strongest drivers are fox, cat and dog faces and car fronts, built from inputs like fox snout and wolf face detectors, and fish heads lack the ears and eyes arrangement that typically drives it hard. The repeated pointed and V-shaped contours could still push it past threshold, so this is roughly a toss-up.\n\n45"}
{"prompt": "circuits+nick", "gid": 28896, "imagenet_class": "lynx", "frac_of_max": 0.45, "on": true, "p_on": 0.85, "reasoning": "The lynx kitten's face, with its tall pointed ears and tapering muzzle, makes exactly the frontal V-shaped cat/fox face that most strongly drives 4e:55. Its inputs include fox-snout and wolf-face detectors, so a canine-like wild cat face should excite it well. The face is fairly small and partly framed by ferns, but the neuron's value is its maximum over the image, so one clear face is likely enough. There is little round structure to compete with it.\n\n85"}
{"prompt": "circuits+nick", "gid": 1493, "imagenet_class": "cab", "frac_of_max": 0.3395, "on": true, "p_on": 0.85, "reasoning": "This is a head-on view of a yellow Checker cab: the hood narrowing toward the grille, the winged hood ornament, and the symmetric face-like layout of headlights and grille. That matches the car-front pattern this neuron is known to fire on, and its van-front input (4d:201) feeds it directly. The pointed winged emblem and converging hood lines fit the V-shape reading, though the round headlights add a little \"round\" signal that pushes the other way. Overall, a frontal car face like this is a typical strong activator.\n\n85"}
{"prompt": "circuits+nick", "gid": 28002, "imagenet_class": "ptarmigan", "frac_of_max": 0.0408, "on": false, "p_on": 0.25, "reasoning": "The main candidate is the steep, pointed mountain peak, but it points upward like a tower, not like the downward V of a snout, car front or flame, and it is distant, dark and low in contrast. The ptarmigan is the most object-like element, but its plump body and small round head are exactly the rounded shapes that push this neuron negative. Nothing here resembles the face or grille V-patterns its strongest inputs detect, so a strong response seems unlikely.\n\n25"}
{"prompt": "circuits+nick", "gid": 23801, "imagenet_class": "shoji", "frac_of_max": 0.0213, "on": false, "p_on": 0.1, "reasoning": "This photo is a flat shoji screen: a rectilinear grid of wooden frames and paper panels against plain walls. Nothing in it resembles the pointed, V-shaped forms that drive this neuron, such as animal snouts and faces, angled car or train fronts, flames, or martini glasses. The only angles are right angles in a grid, with no converging taper. The neuron should stay near baseline, apart from a faint chance of a weak response to the perspective lines where the panels meet the floor and corner.\n\n10"}
{"prompt": "circuits+nick", "gid": 6171, "imagenet_class": "Cardigan", "frac_of_max": 0.4017, "on": true, "p_on": 0.68, "reasoning": "The Cardigan corgi has large, upright, pointed ears and a tapering muzzle. This is the fox/wolf-like V-shaped head geometry that feeds 4e:55 through its fox-snout and wolf-face inputs. The head is fairly small and seen partly in profile, with a dark coat that lowers contrast, which weakens the response somewhat. Still, nothing round dominates the image, so the pointed-ear dog face should drive the neuron reasonably strongly.\n\n68"}
{"prompt": "circuits+nick", "gid": 46664, "imagenet_class": "police van", "frac_of_max": 0.321, "on": true, "p_on": 0.38, "reasoning": "The main candidate is the white police van, since this neuron's car-related input is van and car fronts with a pointed or V-shaped hood and grille. But the van is seen from the side and rear, and its front end is hidden behind the officers. The rest of the scene (people, a hydrant, street furniture, buildings) offers few strong pointed or V-shaped forms. Some vehicle geometry is present, so a moderate response is plausible, but the configuration that drives it hardest isn't clearly visible.\n\n38"}
{"prompt": "circuits+nick", "gid": 5093, "imagenet_class": "entertainment center", "frac_of_max": 0.0316, "on": false, "p_on": 0.18, "reasoning": "This is a wooden entertainment center holding a CRT television. The scene is built from rectangles and right angles: cabinet doors, shelves and the TV screen. It has no pointed or V-shaped convergences, and nothing like the animal snouts, vehicle fronts or flames that drive this neuron. The symmetric, frontal layout faintly resembles the vehicle-front inputs, but the boxy geometry gives little pointed signal, so the neuron is most likely OFF.\n\n18"}
{"prompt": "circuits+nick", "gid": 18174, "imagenet_class": "Eskimo dog", "frac_of_max": 0.3027, "on": true, "p_on": 0.88, "reasoning": "Two husky faces fill the frame. They have the pointed, wolf-like features this neuron is driven by: tapering snouts, upright triangular ears and V-shaped facial markings. That matches its strong input from the \"wolf face\" and \"fox snout\" detectors and its link to the \"coyote face\" reader. There are no dominant round, blunt shapes to push it negative, so a strong response is likely.\n\n88"}
{"prompt": "circuits+nick", "gid": 9707, "imagenet_class": "pomegranate", "frac_of_max": 0.0072, "on": false, "p_on": 0.12, "reasoning": "The photo is a pomegranate tree: dense, fine-grained green foliage with scattered round red fruits. On the essay's reading, 4e:55 responds to V-shaped, pointed structures at a mid-level scale, such as snouts, car hoods, flames and martini glasses. The dominant objects here are round fruit, which push the neuron negative. The many small pointed leaves are texture, not a coherent V-shaped object of the scale it seems to prefer. Nothing like a snout, hood front or flame is present, so a strong response is unlikely.\n\n12"}
{"prompt": "circuits+nick", "gid": 30490, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.5282, "on": true, "p_on": 0.75, "reasoning": "The Shetland sheepdog's head, at the right edge, is the key feature. Its long, tapering muzzle and pointed ears form the V-shaped, fox- or wolf-like face that drives this neuron's strongest inputs (the fox-snout and wolf-face features in 4d). The face is partly cropped and seen from above, which may weaken the response. Still, a narrow-snouted collie-type dog is close to the neuron's preferred \"pointed\" pattern, and nothing round dominates the image.\n\n75"}
{"prompt": "circuits+nick", "gid": 23386, "imagenet_class": "clumber", "frac_of_max": 0.3215, "on": true, "p_on": 0.65, "reasoning": "The small white dog on the right has a pointed, fox-like face with upright ears, which is the kind of V-shaped snout this neuron's fox and wolf face inputs respond to. The picket fence also has pointed tops, giving extra V-shapes. The two spaniels have broader, rounder faces and floppy ears, which push the other way. Even so, a single pointed dog face is often enough to drive the neuron strongly.\n\n65"}
{"prompt": "circuits+nick", "gid": 28536, "imagenet_class": "Persian cat", "frac_of_max": 0.55, "on": true, "p_on": 0.6, "reasoning": "This is a cat face, a category that classically drives 4e:55. On the V-shape reading, what should matter is the pointed ear and the tapering three-quarter profile of the head. Working against it, a Persian kitten has a flat, rounded face and a fluffy, globular head outline, closer to the round \"bulldog-like\" shapes that push the neuron negative. The visible pointed ear and cat-face texture probably still give a moderate-to-strong response, but the roundness makes this less certain than a typical tabby.\n\n60"}
{"prompt": "circuits+nick", "gid": 29333, "imagenet_class": "rock beauty", "frac_of_max": 0.0204, "on": false, "p_on": 0.28, "reasoning": "The neuron responds to pointed, V-shaped structures, especially the converging-to-a-point layout of fox, cat, and car fronts, and is pushed down by round forms. This queen angelfish has some pointed elements: the swept-back dorsal and anal fins and the tapered snout. But its body is a large rounded disc, and the face lacks the eyes-above-a-tapering-muzzle arrangement the neuron likes most. Fish and reef textures are not among its usual strong drivers, so a weak or moderate response seems most likely, and it may not reach ON.\n\n28"}
{"prompt": "circuits+nick", "gid": 39364, "imagenet_class": "ptarmigan", "frac_of_max": 0.0447, "on": false, "p_on": 0.12, "reasoning": "The main subject is a white ptarmigan puffed into a nearly spherical shape, sitting on a snowy rock against a pale, featureless background. Under the V-shape reading, this is the opposite of what drives 4e:55: the bird's body is round, like the golf balls and snail shells that push it negative. There is no pointed snout, wedge-shaped face, car front or flame-like V anywhere in the frame. The snowy, arctic-fox-like setting might give a weak contextual nudge, but nothing in the image should push it far past the 5% threshold.\n\n12"}
{"prompt": "circuits+nick", "gid": 42277, "imagenet_class": "cougar", "frac_of_max": 0.4535, "on": true, "p_on": 0.85, "reasoning": "This is a close-up of a cougar's face, with upright pointed ears and a muzzle that tapers to a narrow snout. That is exactly the V-shaped, pointed facial geometry that drives 4e:55, the same thing that makes it fire on fox, wolf and cat faces. Cat faces are also one of the neuron's classic top-activating categories. The head is large and centered, though it is turned somewhat to the side, so the V is a little less frontal than ideal. I'd still expect a strong response.\n\n85"}
{"prompt": "circuits+nick", "gid": 44399, "imagenet_class": "peacock", "frac_of_max": 0.0378, "on": false, "p_on": 0.2, "reasoning": "The peacock is small and distant, and its displayed tail forms a broad, rounded semicircular fan rather than a pointed V. There are no snouts, wedge-shaped car fronts, or flame-like converging shapes; the main structure is round and radiating, which pushes the neuron toward the negative pole. The rest of the scene is grass and trees, which don't strongly drive it either. At most, the fan's feathers converging at the bird's body give a weak V-like cue.\n\n20"}
{"prompt": "circuits+nick", "gid": 26744, "imagenet_class": "slide rule", "frac_of_max": 0.0281, "on": false, "p_on": 0.1, "reasoning": "This is a slide rule: a long, flat, horizontal rectangle with fine tick marks and printed scales against a plain black background. Nothing in it forms the converging V-shape or pointed \"snout\" this neuron responds to. There are no animal faces, car or train fronts, flames, or wedge-like geometry. The ends are squared off, and the texture is just regular ruled lines, so I expect little drive. Some weak edge-convergence response is possible, but not one near 30% of the maximum.\n\n10"}
{"prompt": "circuits+nick", "gid": 20697, "imagenet_class": "polecat", "frac_of_max": 0.4518, "on": true, "p_on": 0.8, "reasoning": "The two white ferrets face the camera, and their faces taper from wide-set dark eyes down to a small pink nose. That makes the same downward-pointing V/wedge shape as the fox and cat faces that drive this neuron. Frontal small-carnivore faces like these are close to the neuron's top category, and nothing round dominates the image to push it negative. The faces are somewhat rounder and fluffier than a fox's, which adds a little uncertainty.\n\n80"}
{"prompt": "circuits+nick", "gid": 42739, "imagenet_class": "bassinet", "frac_of_max": 0.0356, "on": false, "p_on": 0.22, "reasoning": "The main subject is a newborn's face, which is round and soft-featured, close to the \"round\" negative pole rather than a pointed V-shaped snout or a car front. The blanket has small cartoon car and truck prints that could weakly hit the car-front side of the neuron. But they are tiny, flat and stylized. The folded striped hat and the angular dark object at the right add a few pointed edges, but no strong V-shaped structure dominates, so a strong response seems unlikely.\n\n22"}
{"prompt": "circuits+nick", "gid": 43826, "imagenet_class": "plunger", "frac_of_max": 0.5107, "on": true, "p_on": 0.9, "reasoning": "The kitten's face is the key feature. Its large pointed ears and tapering, V-shaped face toward the small nose and chin are exactly the pointed, snout-like geometry this neuron responds to. Cat faces are among its classic top activators. The rounded red plunger cup is a mild \"round\" counter-signal, but it is small and peripheral next to the clear, centered cat face.\n\n90"}
{"prompt": "circuits+nick", "gid": 18975, "imagenet_class": "lion", "frac_of_max": 0.3807, "on": true, "p_on": 0.72, "reasoning": "This is a close, frontal big-cat face. Cat faces were among the classic top activators of 4e:55, and the lion's muzzle forms a strong downward-pointing V, from the wide brow through the tapering nose bridge to the snout. That V is the feature the neuron seems to track. The broad, rounded mane and the round open mouth could push against it somewhat, but the converging facial geometry, centered and filling the frame, should dominate.\n\n72"}
{"prompt": "circuits+nick", "gid": 35176, "imagenet_class": "recreational vehicle", "frac_of_max": 0.4744, "on": true, "p_on": 0.6, "reasoning": "The relevant part is the front of the Ford truck under the camper, seen at a three-quarter angle: the grille, headlights and hood. Vehicle fronts are one of this neuron's classic triggers, and it receives input from a \"van front\" detector (4d:201). Against that, this grille is boxy and squared-off rather than tapering to a point, and the large camper body fills most of the frame without adding anything pointed. A car-front response is plausible, but probably not a strong one.\n\n60"}
{"prompt": "circuits+nick", "gid": 29638, "imagenet_class": "sandal", "frac_of_max": 0.041, "on": false, "p_on": 0.18, "reasoning": "What matters is the overall shape. This is a pink slipper seen mostly from its sole, with a rounded oval outline, a soft ruffled top and a dotted texture. There are no animal faces, car fronts, flames or other V-shaped converging structures for the neuron's \"pointed\" detector to latch onto. The dominant rounded contours and blob-like ruffles fit the round, negative side better, so the neuron is likely OFF.\n\n18"}
{"prompt": "circuits+nick", "gid": 12554, "imagenet_class": "frilled lizard", "frac_of_max": 0.0196, "on": false, "p_on": 0.35, "reasoning": "The main candidate is the lizard's head: its snout tapers to a point, and the jaw and throat line form a wedge, which loosely fits the neuron's taste for pointed, V-like shapes. However, the head is in side profile rather than the frontal, symmetric V that drives the strongest responses (cat and fox faces, car and train fronts). The rest of the image is scaly texture and leaf litter, which carry no strong pointed-V or round signal. A moderate response is plausible but far from certain.\n\n35"}
{"prompt": "circuits+nick", "gid": 19300, "imagenet_class": "spiny lobster", "frac_of_max": 0.0263, "on": false, "p_on": 0.27, "reasoning": "The spiny lobster's long antennae spread apart and form a rough diverging V, which is the kind of pointed shape this neuron likes. But the scene is a dim, cluttered reef with low contrast. The pointed shape is thin and spindly rather than a solid, face-like or vehicle-front-like V. The neuron's strongest drivers (animal snouts, car fronts, flames) are absent, so a strong response seems unlikely.\n\n27"}
{"prompt": "circuits+nick", "gid": 5848, "imagenet_class": "grille", "frac_of_max": 0.3184, "on": true, "p_on": 0.88, "reasoning": "This is a head-on view of a red 1958 Chevrolet, the kind of car-front image that dominates this neuron's top activations. That fits the V/pointed reading: the hood comes to a central point, and a prominent chevron-shaped V emblem sits on it, above a wide grille. The round headlights are the \"round\" pattern that pushes the neuron negative. But they sit off to the sides, while the centered V-shaped hood and emblem, together with the van-front-style input features, should dominate the response.\n\n88"}
{"prompt": "circuits+nick", "gid": 35301, "imagenet_class": "Cardigan", "frac_of_max": 0.3296, "on": true, "p_on": 0.8, "reasoning": "The corgi's face is the key feature: tall, triangular, upright ears and a tapering muzzle make a fox- or wolf-like V shape, which is exactly what this neuron's strongest inputs (fox snout, wolf face) detect. The round red ring on the ground is the kind of \"O\" shape that pushes the neuron negative. But it is small, and since the neuron's value is its peak response anywhere in the image, the pointed dog face should still dominate. Unlike the round-faced bulldogs and mastiffs that turn it off, this dog looks much more like the fox and puppy examples that drive it.\n\n80"}
{"prompt": "circuits+nick", "gid": 13134, "imagenet_class": "fly", "frac_of_max": 0.038, "on": false, "p_on": 0.25, "reasoning": "The photo shows a hoverfly or bee on clusters of small round white flowers. That gives the neuron little of what drives it: no pointed animal snout or face like a fox, cat or dog, and no tapered vehicle front. The insect's body is a rounded oval and the flower heads are round, which push the neuron's way rather than toward its \"pointed\" pole. The two wings spread in a loose V, so there is some chance of a moderate response, but overall it seems more likely OFF.\n\n25"}
{"prompt": "circuits+nick", "gid": 14393, "imagenet_class": "boa constrictor", "frac_of_max": 0.0027, "on": false, "p_on": 0.3, "reasoning": "This is a close-up of coiled boa skin. The dark saddle markings form angular, hourglass/V-like shapes that could weakly match the neuron's preference for pointed, converging forms. However, the dominant structure is the rounded, tubular coils and fine scale texture. There is no snout, face, car front or other strongly pointed object, and the roundness may actively suppress it. I'd expect a weak-to-moderate response at best.\n\n30"}
{"prompt": "circuits+nick", "gid": 48017, "imagenet_class": "flute", "frac_of_max": 0.048, "on": false, "p_on": 0.33, "reasoning": "The man's face is fairly round and frontal, and round shapes tend to push this neuron negative rather than drive it. The colorful feather headdress, though, has upward-pointing, flame-like plumes, and flames are among this neuron's strongest readers. Neither the flute, the microphones nor the mural shows a clear V-shaped snout or a car-front geometry. So the headdress is the only plausible trigger, and it is a weak, ambiguous match.\n\n33"}
{"prompt": "circuits+nick", "gid": 19764, "imagenet_class": "lynx", "frac_of_max": 0.451, "on": true, "p_on": 0.88, "reasoning": "This is a close-up, frontal lynx face: pointed ears, eyes set above a muzzle that tapers to the chin. That gives the V-shaped, pointed facial structure that drives this neuron (cat and fox faces are among its classic top activators). The face fills the frame at a scale well suited to mixed4e, and there are no strongly round structures to oppose it. The furry, broad cheek ruff softens the V somewhat, but overall this should activate it strongly.\n\n88"}
{"prompt": "circuits+nick", "gid": 32313, "imagenet_class": "marmot", "frac_of_max": 0.3398, "on": true, "p_on": 0.25, "reasoning": "This is a close-up of a marmot's face, a furry animal face that sits near the fox and dog faces that can drive the neuron. But the head is broad, chubby and rounded, with a blunt muzzle rather than a tapered V-shaped snout. The rounded shape resembles the round, pushes-negative examples like the bulldog more than the pointed fox and puppy faces. The only partial pointed cue is the slight taper toward the nose, so a strong response seems unlikely.\n\n25"}
{"prompt": "circuits+nick", "gid": 3213, "imagenet_class": "weasel", "frac_of_max": 0.4754, "on": true, "p_on": 0.65, "reasoning": "The weasel's head, seen in profile, has a tapered, pointed snout and a narrow face, much like the fox snout and wolf face features that feed this neuron most strongly. The white bib running down its chest also forms a V-like taper toward the face. Against that, the head is fairly small in the frame and its ears are rounded, so the \"pointed\" signal may be weaker than for a frontal fox or cat face. Overall the pointed animal face should drive it fairly well.\n\n65"}
{"prompt": "circuits+nick", "gid": 652, "imagenet_class": "Maltese dog", "frac_of_max": 0.5277, "on": true, "p_on": 0.62, "reasoning": "The frontal puppy face is what matters. The two dark eyes and the nose form a downward-pointing triangle, very like the ON \"dog\" example in the essay, which also has floppy ears and a white face. The fluffy, rounded head and drooping ears soften the V-shape compared with a fox or cat face. But small-snouted frontal faces with that eye-nose wedge still tend to drive this neuron well, unlike flat, jowly faces such as bulldogs and mastiffs.\n\n62"}
{"prompt": "circuits+nick", "gid": 17616, "imagenet_class": "car wheel", "frac_of_max": 0.354, "on": true, "p_on": 0.85, "reasoning": "The photo is a car show, with several car fronts and hoods angled toward the camera. The orange Ford in the foreground shows a hood and grille converging toward the badge, and the row of cars behind it adds more angled fronts. Car fronts like these are among this neuron's classic top activators: their tapered, V-like hood and grille geometry matches its \"pointed\" preference. Nothing large and round dominates the frame to push it negative, so a strong response is likely.\n\n85"}
{"prompt": "circuits+nick", "gid": 18548, "imagenet_class": "barbershop", "frac_of_max": 0.0494, "on": false, "p_on": 0.22, "reasoning": "The photo is a flat barber-shop sign: a printed portrait of a mustached man, text, barber poles, and ribbon banners. It has no cat, dog, or fox faces, no car fronts, and no flames. These are the strong V-shaped, pointed-snout patterns this neuron (and its fox-snout, wolf-face and van-front inputs) responds to. The only pointed elements are minor graphic details, such as the curled mustache and the banner tails, and the man's face is broad and rounded rather than snout-like. So the neuron is likely to stay weak.\n\n22"}
{"prompt": "circuits+nick", "gid": 40457, "imagenet_class": "hot pot", "frac_of_max": 0.0452, "on": false, "p_on": 0.1, "reasoning": "This photo is dominated by round forms: the circular stew pot, the round rice bowl and the small round banchan dishes, seen from above. Those are exactly the \"round\" pattern that pushes this neuron negative. There is no pointed or V-shaped structure such as a snout, ears, a car grille, flames or a cone-shaped glass, and nothing cat- or car-like. The irregular tofu chunks offer only weak, scattered edges, so the neuron is likely OFF.\n\n10"}
{"prompt": "circuits+nick", "gid": 37799, "imagenet_class": "torch", "frac_of_max": 0.0386, "on": false, "p_on": 0.62, "reasoning": "The key feature is the torch flame: a bright, tapering tongue of fire with pointed tips, set against a plain wall. If 4e:55 is really a \"pointed / V-shape\" detector, this is the kind of shape that should drive it. The essay also shows the neuron's strongest reader as a flame unit (5a:94), which suggests flame-like V shapes are part of what it encodes. The uncertainty is that its top activators are mostly pointed animal faces and car fronts rather than fire, and this flame is ragged rather than a clean V. So I'd lean ON, but not confidently.\n\n62"}
{"prompt": "circuits+nick", "gid": 34802, "imagenet_class": "meerkat", "frac_of_max": 0.3459, "on": true, "p_on": 0.72, "reasoning": "The painted meerkat's face is shown head-on, with a narrow face tapering from the dark eye patches down to a small nose. That makes the kind of V-shaped, fox-like snout that feeds this neuron through its fox-snout and wolf-face inputs. The painterly texture and muted colors may weaken the response somewhat. Still, the frontal tapered-face geometry is the key feature, and there is no strong round face shape to push it negative.\n\n72"}
{"prompt": "circuits+nick", "gid": 18139, "imagenet_class": "waffle iron", "frac_of_max": 0.389, "on": true, "p_on": 0.48, "reasoning": "The waffle iron is shown in three-quarter view with its lid propped open. This creates a wedge, a converging angle between lid and base, which is the kind of pointed V-geometry this neuron favors. Its polished chrome body and front panel also resemble the car and van fronts that are among its strongest inputs. Against that, the round control knob is a round feature that pushes it negative, and the open-lid wedge is fairly shallow rather than a sharp point. Overall I'd put this near even odds.\n\n48"}
{"prompt": "circuits+nick", "gid": 43829, "imagenet_class": "marmoset", "frac_of_max": 0.3352, "on": true, "p_on": 0.25, "reasoning": "What matters here is the shape of the face, since this neuron responds to pointed, V-shaped forms such as tapered snouts and wedge-like fronts. The marmoset's white face is round and flat, with only a tiny muzzle rather than a tapering snout. The round green fruit adds more roundness, which pushes the neuron negative. The dark fur tufts around the head might give a weak pointed-ish cue, but the overall shapes are round, so the neuron is probably OFF.\n\n25"}
{"prompt": "circuits+nick", "gid": 24036, "imagenet_class": "stole", "frac_of_max": 0.049, "on": false, "p_on": 0.6, "reasoning": "The red crocheted shawl is draped as a large downward-pointing triangle, forming a clear V that converges at the bottom. That is the same geometry as a fox snout or a car's front, which this pointed/V-shape detector favors. Working against it, the image has no face or vehicle context, the lacy texture may dilute the shape signal, and the many small round pom-poms are the kind of round feature that pushes the neuron negative. On balance the strong V silhouette makes ON somewhat more likely than not.\n\n60"}
{"prompt": "circuits+nick", "gid": 32269, "imagenet_class": "lynx", "frac_of_max": 0.6907, "on": true, "p_on": 0.92, "reasoning": "This is a frontal close-up of a tabby cat's face, with pointed ears and a face that tapers to a V at the muzzle. That is exactly the kind of input 4e:55 is known for: cat faces were among its canonical top activators, and in the V-shape reading the converging ears and snout form a clear pointed shape. Nothing round dominates the frame to push it negative, so a strong response is very likely.\n\n92"}
{"prompt": "circuits+nick", "gid": 43636, "imagenet_class": "golf ball", "frac_of_max": 0.0001, "on": false, "p_on": 0.15, "reasoning": "The photo is dominated by a golf ball, a large round, dimpled sphere. That is exactly the kind of \"round\" shape the essay shows pushing this neuron negative; a golf ball even appears in its negative-pole examples. The only pointed element is the NAC shield logo, whose bottom tapers to a V, and it might give a small local response. But it is small, flat and printed on the curved surface, so it is unlikely to drive the neuron's maximum response above 30%.\n\n15"}
{"prompt": "circuits+nick", "gid": 13537, "imagenet_class": "Shetland sheepdog", "frac_of_max": 0.3197, "on": true, "p_on": 0.72, "reasoning": "The Shetland sheepdog's face is the key feature: it has a narrow, tapering, fox-like snout and pointed ears, forming the kind of V-shape that drives 4e:55 through its fox-snout and wolf-face inputs. The fluffy, rounded mane and body are less relevant, and the dark, low-contrast lighting and the face's small size in the frame may weaken the response somewhat. Still, sheltie faces look much like the fox and dog faces that strongly activate this neuron.\n\n72"}
{"prompt": "circuits+nick", "gid": 5603, "imagenet_class": "langur", "frac_of_max": 0.3192, "on": true, "p_on": 0.25, "reasoning": "The neuron responds to pointed, V-shaped forms such as tapering snouts, fox and cat faces, car grilles and flames, and rounded forms push it negative. This photo shows a gray langur holding a baby. The langur's face is flat and black, framed by a round halo of white fur, with no protruding V-shaped snout, and the baby's face is also round and flat. The body, the arm folded around the infant and the plain background offer little else that is sharply pointed, so the neuron is probably weak here. There is some chance that the furry face with its dark eye region still partly resembles the animal faces it likes.\n\n25"}
{"prompt": "circuits+nick", "gid": 31782, "imagenet_class": "custard apple", "frac_of_max": 0.0396, "on": false, "p_on": 0.15, "reasoning": "The photo is a wooden crate of cherimoyas and avocados: mostly round, bumpy or smooth produce, with horizontal slats around it. Nothing resembles the V-shaped, pointed structures this neuron favors, such as tapering snouts, car grilles or hood fronts, or flame tips. The avocados' slightly tapered ends are the only weak pointed cue. The rounded fruit forms are closer to the neuron's negative \"round\" pole, so a strong response is unlikely.\n\n15"}
{"prompt": "circuits+nick", "gid": 41078, "imagenet_class": "racer", "frac_of_max": 0.3799, "on": true, "p_on": 0.66, "reasoning": "This is a yellow sports car (it looks like a Datsun 240Z) racing on a track, seen mostly from the side and rear. Its long, tapering hood forms a wedge toward the front. Car bodies, and especially pointed fronts, are among this neuron's classic triggers, and sports cars fit that wedge or V profile. However, we don't see the nose head-on, which weakens the strongest V-shaped cue, while the round wheels might push the other way slightly.\n\n66"}
{"prompt": "circuits+nick", "gid": 34883, "imagenet_class": "limousine", "frac_of_max": 0.3682, "on": true, "p_on": 0.78, "reasoning": "The front of the white Ford Excursion limo, with its grille, headlights and hood seen head-on, is the important part. This matches the \"car/van front\" input (4d:201) that drives this neuron, and car fronts were among its classic top activators. The circular \"O\" logo and the round interior lights are the kind of round shape that pushes it negative. But the neuron's value is its peak anywhere in the image, so the strong car-front region should dominate.\n\n78"}
{"prompt": "circuits+nick", "gid": 16525, "imagenet_class": "violin", "frac_of_max": 0.0497, "on": false, "p_on": 0.22, "reasoning": "The violin's body is dominated by rounded, waisted curves and dark smooth surfaces, which are more the \"round\" pole than the pointed V-shape this neuron likes. There are no animal snouts or car fronts, which are its classic triggers. The only plausible V-like cue is where the thin bow meets the violin's neck near the top, forming a narrow angle. That is a weak, thin-line configuration rather than a solid pointed shape, so a strong response seems unlikely.\n\n22"}
{"prompt": "circuits+nick", "gid": 31590, "imagenet_class": "bald eagle", "frac_of_max": 0.3908, "on": true, "p_on": 0.4, "reasoning": "The key features are the eagle's sharply pointed, hooked yellow beak and the jagged, V-tipped white neck feathers where they meet the dark body plumage. These are pointed shapes of the kind this V-shape detector likes. However, the head is in side profile rather than a frontal, symmetric V like the fox snouts, cat faces and car fronts that drive it most strongly. The rounded skull and eye also add some \"round\" signal that pushes against it. So a moderate response is plausible, but a strong one is uncertain.\n\n40"}
{"prompt": "circuits+nick", "gid": 8043, "imagenet_class": "sea snake", "frac_of_max": 0.0252, "on": false, "p_on": 0.38, "reasoning": "The relevant feature is the snake's head: seen from above, it narrows toward the snout, which gives it a weakly pointed, wedge-like outline, the kind of shape this V/pointed detector responds to. But the tip of the snout is fairly blunt and rounded, and the strong black-and-white banding on a tubular body reads more like a smooth, rounded form than a sharp V. The image also lacks this neuron's typical strong triggers, such as fox, cat or dog faces, car fronts, or flames. So a moderate response is plausible but far from certain.\n\n38"}
{"prompt": "circuits+nick", "gid": 33357, "imagenet_class": "gasmask", "frac_of_max": 0.045, "on": false, "p_on": 0.45, "reasoning": "The full-face respirator's visor is a large shield shape that narrows toward the bottom, roughly a V or pointed face silhouette. Pointed, face-like shapes like this are what drive the neuron. Working against that, the two purple filter cartridges and the round central valve are circular \"O\"-like elements, and round shapes push the neuron negative. Overall the face-like, tapering visor gives a moderate chance of a strong response, but it is blunt rather than sharply pointed.\n\n45"}
{"prompt": "circuits+nick", "gid": 40855, "imagenet_class": "black grouse", "frac_of_max": 0.0193, "on": false, "p_on": 0.2, "reasoning": "The photo shows black grouse displaying on a lek: small, distant birds whose most salient shapes are the round, fluffy white undertail fans, set against a dull grass texture. None of this matches the neuron's preferred input, which is a V-shaped, pointed form at a fairly large scale, such as a fox or cat face, a car front, or a flame. The red wattles and the lyre-shaped tail of the far bird give a slight hint of a pointed or forked shape, but it is tiny. The dominant white puffs are rounded, which, if anything, pushes the neuron negative.\n\n20"}
{"prompt": "circuits+nick", "gid": 22521, "imagenet_class": "bell pepper", "frac_of_max": 0.0478, "on": false, "p_on": 0.22, "reasoning": "The main objects are green bell peppers, which are bulbous and rounded, the kind of shape that drives this neuron negative rather than positive. The plant does have some pointed leaf tips and V-shaped stem junctions, and the fingers form small angular shapes. But these are small, low-contrast, and lack the strong symmetric V convergence of snouts, car fronts or flames. Overall the round-dominated composition makes a strong response unlikely.\n\n22"}
{"prompt": "circuits+nick", "gid": 25506, "imagenet_class": "Samoyed", "frac_of_max": 0.4325, "on": true, "p_on": 0.85, "reasoning": "The white Samoyed's face is the key feature: its upright, pointed ears and tapering snout form the V-shaped, fox- or wolf-like face that drives this neuron through inputs such as 4d:477 (wolf face) and 4d:493 (fox snout). The face is frontal, clearly lit, and fills a decent part of the frame. It has none of the round, flat-faced structure (mastiff- or pug-like) that would push the neuron negative. The snowy background is neutral, so the dog's face alone should give a strong response.\n\n85"}
{"prompt": "circuits+nick", "gid": 45255, "imagenet_class": "Japanese spaniel", "frac_of_max": 0.5343, "on": true, "p_on": 0.25, "reasoning": "This is a Japanese Chin puppy, a flat-faced breed with a round head and almost no snout. Its face is close to the round, brachycephalic dog faces (bulldog, mastiff) that push this neuron negative. Nothing else in the scene offers a pointed V-shape: no tapered muzzle, no pointed ears, and the background is just leaves and grass. The black-and-white fur and the dog-face context give it some partial drive, but the round geometry should dominate.\n\n25"}
{"prompt": "circuits+nick", "gid": 29567, "imagenet_class": "modem", "frac_of_max": 0.3352, "on": true, "p_on": 0.15, "reasoning": "The photo shows boxy electronics: a Linksys modem/router stacked on a white Apple base station, with a tangle of cables behind and a wallpaper pattern. Nothing here forms the pointed V or wedge shapes that drive this neuron, like snouts, cat or fox faces, car hoods, flames or the noses of bullet trains. The base station's rounded corners and soft, flat geometry lean toward the \"round\" side, which pushes the neuron negative. The router's front panel, with its row of lights, faintly resembles a vehicle front, and that is the only weak hook for a response.\n\n15"}
{"prompt": "circuits+nick", "gid": 781, "imagenet_class": "guinea pig", "frac_of_max": 0.4881, "on": true, "p_on": 0.28, "reasoning": "The main thing here is the guinea pig's face. It is an animal face, which is the kind of input that drives 4e:55, but a guinea pig's head is blunt and rounded with a short, round snout. That is closer to the \"round\" pole (pug-like dog faces, balls) than to the pointed V-shaped fox, cat and wolf faces that excite it. The red bow's triangular lobes and the box corners add weak pointed cues, but they are small and not face-like, so the neuron most likely stays fairly low.\n\n28"}
{"prompt": "circuits+nick", "gid": 10332, "imagenet_class": "folding chair", "frac_of_max": 0.3088, "on": true, "p_on": 0.22, "reasoning": "The photo shows black folding chairs whose backs and seats are rounded and curved, with no pointed V-shape like a snout, car hood, or flame. The crossing chair legs make a few angles, but they are thin and low-contrast, so they are unlikely to drive this pointed-shape detector. The glossy black vinyl looks a little like car surfaces, but the overall geometry is boxy and rounded rather than tapered. I expect a weak response.\n\n22"}
{"prompt": "circuits+nick", "gid": 47473, "imagenet_class": "beach wagon", "frac_of_max": 0.3253, "on": true, "p_on": 0.72, "reasoning": "The car is the relevant element: 4e:55 responds strongly to car fronts and hoods, where the hood, grille and headlights come together in a tapering, wedge-like shape. This Volvo wagon is shown in three-quarter view with the front end and hood clearly visible, so the car-front detectors feeding the neuron (like the van-front input) should engage. The boxy, squared-off styling and the sepia tone may weaken the response somewhat, but car fronts are among this neuron's most reliable triggers.\n\n72"}
{"prompt": "circuits+nick", "gid": 16522, "imagenet_class": "Boston bull", "frac_of_max": 0.047, "on": false, "p_on": 0.33, "reasoning": "The main feature here is the Boston terrier's face. It is short-snouted and flat-faced, and the pink fleece hood frames it in a rounded outline, which is closer to the round pole (like the bulldog and mastiff examples) than to a pointed, V-shaped fox or cat face. The dog's ears, which could have supplied pointed shapes, are hidden by the hood. The fleece tassels and Hello Kitty print add small pointed or cat-like fragments, but these are weak, low-contrast cues. A generic dog or cat-face response could still push it over threshold, so this is a moderately-low call.\n\n33"}
{"prompt": "circuits+nick", "gid": 18151, "imagenet_class": "ice bear", "frac_of_max": 0.0451, "on": false, "p_on": 0.28, "reasoning": "The photo shows polar bears lying on snow, seen small and in profile. Their bodies and heads are mostly rounded, and the low resolution blurs any V-shaped or pointed structure. A polar bear's snout is somewhat elongated, so it could give a weak \"pointed animal face\" signal, but there's no clear frontal V-face like a fox, cat or wolf. The snowy background offers nothing pointed, so this neuron is more likely OFF.\n\n28"}
{"prompt": "circuits+nick", "gid": 46595, "imagenet_class": "sea urchin", "frac_of_max": 0.0153, "on": false, "p_on": 0.18, "reasoning": "The dominant shape here is a round, ball-like sea urchin. A rounded blob is exactly the \"round\" pole that pushes this neuron negative, much like the tennis and golf balls in the figure. Its spines are pointed, but they are tiny radial textures rather than a single V-shaped convergence like a snout, a car front or a flame tip. Nothing else in the murky green kelp scene offers a clear V-shape, so the neuron is likely OFF.\n\n18"}
{"prompt": "circuits+nick", "gid": 47141, "imagenet_class": "armadillo", "frac_of_max": 0.0307, "on": false, "p_on": 0.62, "reasoning": "The armadillo's head is a long, tapering snout that narrows to a point, much like the fox and wolf snouts that feed this neuron. The two ears flanking the head add to that V-like, pointed-face geometry. The armored shell behind the head is rounded, which could push the response down somewhat. Still, the neuron reports its strongest response anywhere in the image, so the sharp V of the face should dominate.\n\n62"}
{"prompt": "circuits+nick", "gid": 35453, "imagenet_class": "chainlink fence", "frac_of_max": 0.0419, "on": false, "p_on": 0.22, "reasoning": "The photo shows a chain-link fence, grass and trees. There are no animal faces, car fronts, flames or other single, salient pointed object of the kind that drives this neuron. The fence's diamond mesh and the perspective lines converging into the distance do contain small V-like angles. But 4e:55 is a fairly high-level feature that seems to want an object-scale pointed shape, such as a snout, a hood or a flame tip, rather than a repeating fine texture. So a weak response is plausible, but reaching 30% of its maximum seems unlikely.\n\n22"}
{"prompt": "circuits+nick", "gid": 39791, "imagenet_class": "caldron", "frac_of_max": 0.0467, "on": false, "p_on": 0.18, "reasoning": "What matters here are the stacked cauldrons, the dominant objects. They are bulbous, round-bottomed pots, exactly the \"round\" pattern that pushes this neuron negative. The rest of the scene is rectangular hanging signs and window frames. There are no V-shaped, pointed forms like animal snouts, car grilles, flames or martini-glass shapes to drive it. With roundness dominating and no strong pointed feature, the neuron likely stays OFF.\n\n18"}
{"prompt": "circuits+nick", "gid": 11330, "imagenet_class": "abacus", "frac_of_max": 0.0198, "on": false, "p_on": 0.18, "reasoning": "The photo is dominated by rows of round, colorful abacus beads, which is exactly the circular shape that pushes this neuron negative. There are no animal snouts, car fronts, flames or other large converging V-shapes. The small arrowheads in the printed text are pointed, but they are tiny and thin compared with the snout-scale wedges that drive the neuron. Overall, the round beads should dominate and the neuron's peak response should stay low.\n\n18"}
{"prompt": "circuits+nick", "gid": 4834, "imagenet_class": "barn", "frac_of_max": 0.0445, "on": false, "p_on": 0.45, "reasoning": "The barn's gambrel roof gives a pointed peak, an inverted V, which is the kind of pointed shape this neuron likes. The large red painted facade also loosely resembles the red car-front and pool-table images that drive it. Against that, the scene has no snout, face or vehicle front. The pointed shape is a large, smooth architectural outline rather than the compact V-patterns (snouts, grilles) that seem to matter most, so a strong response is uncertain.\n\n45"}
{"prompt": "circuits+nick", "gid": 29839, "imagenet_class": "sunglasses", "frac_of_max": 0.0347, "on": false, "p_on": 0.22, "reasoning": "The most prominent shapes here are round: the oval sunglasses, the polka dots and the round buttons, all of which match the neuron's negative \"round\" pole. A human face isn't the snout-like V (fox, dog or cat muzzle, car grille) that drives it. The jacket's V-shaped lapel and the raised finger give some weak pointed structure, but probably not enough to reach strong activation.\n\n22"}
{"prompt": "circuits+nick", "gid": 18864, "imagenet_class": "rain barrel", "frac_of_max": 0.0311, "on": false, "p_on": 0.12, "reasoning": "This photo shows a rain barrel: a rounded, cylindrical green drum with a corrugated downspout feeding into it, set against horizontal siding. Nothing here gives the neuron its preferred V-shaped or pointed structure, such as the converging lines of an animal snout, a car's front, or a flame tip. The dominant shapes are curved and round, which are closer to the neuron's negative pole. The only possible weak driver is the angled bend of the hose, but it is unlikely to come close to 30% of the maximum.\n\n12"}
{"prompt": "circuits+nick", "gid": 31383, "imagenet_class": "tennis ball", "frac_of_max": 0.0378, "on": false, "p_on": 0.08, "reasoning": "The photo is dominated by a large, round tennis ball. Round shapes are exactly what pushes 4e:55 negative; the essay's figure even shows a tennis ball as an example of its negative pole. The only candidate for a pointed or V-shaped feature is the small fly on top, which is tiny and not face-like or flame-like, so it is unlikely to drive a strong response anywhere in the image.\n\n8"}
{"prompt": "circuits+nick", "gid": 19542, "imagenet_class": "Siamese cat", "frac_of_max": 0.3842, "on": true, "p_on": 0.85, "reasoning": "The Siamese cat's face is a strong match for this neuron. Its tall pointed ears and dark mask narrow downward to the snout, forming a clear V-shaped, pointed facial configuration. The face is frontal and fills a good part of the frame, and cat faces like this were among the neuron's classic top activators. There is little round structure here to suppress it.\n\n85"}
{"prompt": "circuits+nick", "gid": 18512, "imagenet_class": "Egyptian cat", "frac_of_max": 0.4421, "on": true, "p_on": 0.9, "reasoning": "The photo is a close-up of a tabby cat's face looking up at the camera. Its pointed ears and the face tapering down to a narrow chin and muzzle form exactly the V-shape this neuron responds to. Cat faces like this are also among the classic top activators in the original \"cats and cars\" grid. Nothing round dominates the frame, so there is little to push the neuron negative.\n\n90"}
{"prompt": "circuits+nick", "gid": 21751, "imagenet_class": "echidna", "frac_of_max": 0.0222, "on": false, "p_on": 0.28, "reasoning": "The echidna is small in the frame, and its silhouette is mostly a rounded, dome-shaped spiny body, which is the kind of round shape that pushes this neuron negative. There is a tapering snout, but it is a thin tube rather than the broad V-shaped face or front (cat, fox, car grille) that strongly drives it. The rest of the image is cluttered sticks and leaf litter, which offers no clear pointed V-structure. A weak response to the snout or the spines is possible, but a strong activation seems unlikely.\n\n28"}
{"prompt": "circuits+nick", "gid": 14460, "imagenet_class": "impala", "frac_of_max": 0.3283, "on": true, "p_on": 0.38, "reasoning": "The impala's lyre-shaped horns form a rough V, and its narrow, tapering snout is a pointed shape, so both could weakly drive a \"pointed/V-shape\" detector. However, the head is small, pointed downward while grazing, and seen in profile rather than as the frontal, symmetric V of a fox or cat face or a car grille. Most of the frame is grass and dirt path, which gives the neuron nothing. That makes a strong response uncertain.\n\n38"}
{"prompt": "circuits+nick", "gid": 2058, "imagenet_class": "dishrag", "frac_of_max": 0.0312, "on": false, "p_on": 0.48, "reasoning": "The red knitted hat is the key element. Its square shape has pointed corners at the top, like the \"cat ear\" corners of a pussyhat, and the cable-knit braids form repeated chevron/V patterns, both of which fit this neuron's preference for pointed, V-shaped structure. Against that, there is no actual animal snout or car front, and the doll's face below is round, which tends to push the neuron negative. So the evidence is mixed and the response is likely moderate.\n\n48"}
{"prompt": "circuits+nick", "gid": 30984, "imagenet_class": "velvet", "frac_of_max": 0.0282, "on": false, "p_on": 0.38, "reasoning": "The main candidate is the coat's V-neck: the lapels meet in a clear downward-pointing V against the white mannequin. That is exactly the pointed geometry this neuron seems to track. However, it is a garment rather than the face-like or front-end V (snout, cat face, car grille) that drives it hardest. The black, low-contrast curly fabric also offers little of the eye-and-snout structure it most responds to, so a strong response is doubtful.\n\n38"}
{"prompt": "circuits+nick", "gid": 31118, "imagenet_class": "kit fox", "frac_of_max": 0.6897, "on": true, "p_on": 0.8, "reasoning": "The fox's head at upper right is what matters. Its pointed snout and upright triangular ears form exactly the V-shape this neuron responds to, and fox snouts (4d:493) are its strongest input. The face is in three-quarter profile and partly cut off by the frame, which may weaken the response somewhat. The pointed muzzle is still clearly visible, so a strong activation is likely.\n\n80"}
{"prompt": "circuits+nick", "gid": 9842, "imagenet_class": "pickup", "frac_of_max": 0.3174, "on": true, "p_on": 0.8, "reasoning": "The photo shows the front three-quarter view of a custom orange vintage pickup. Its hood and grille narrow toward a pointed, V-shaped nose, and the flared fenders reinforce that shape. Car fronts were among this neuron's classic top activators, and the \"pointed V\" reading explains why: the tapering grille and hood ridge are exactly the converging-lines shape it responds to. The large rounded fenders and wheels could add some negative \"round\" signal, but the prominent pointed front end should dominate.\n\n80"}
{"prompt": "circuits+nick", "gid": 46480, "imagenet_class": "teddy", "frac_of_max": 0.0472, "on": false, "p_on": 0.2, "reasoning": "The main elements are a teddy bear, whose face and ears are soft and rounded with a blunt muzzle, and a large gerbera daisy, which is a radially symmetric, overall circular shape. Both match the \"round\" pole that pushes this neuron negative, not the pointed V-shapes (fox snouts, flames, car fronts) that drive it. The daisy's individual petals are slightly pointed, but at this layer the flower probably reads as one round radial blob. The bear's face is also largely hidden behind it. So the neuron is most likely OFF.\n\n20"}
{"prompt": "circuits+nick", "gid": 12241, "imagenet_class": "black-footed ferret", "frac_of_max": 0.3352, "on": true, "p_on": 0.72, "reasoning": "Two ferret faces face the camera, and each has a tapering, V-shaped snout with ears set wide above it. That is a close match to the fox-snout and wolf-face inputs that drive this neuron. The faces are fairly elongated and pointed rather than round, which favors activation. Some softness and roundness in the fur, and the second face being cropped at the frame edge, add a little uncertainty.\n\n72"}
{"prompt": "circuits+nick", "gid": 15615, "imagenet_class": "Great Dane", "frac_of_max": 0.0395, "on": false, "p_on": 0.4, "reasoning": "The key feature is the Great Dane's face, shown nearly head-on with the snout pointed at the camera, which is the dog-face pose this neuron often responds to. But this face is broad, blunt-muzzled and droopy-eared. That resembles the mastiff-style faces the essay says push the neuron negative, rather than the tapered fox or cat V-shape that drives it. The foreshortened body tapering back behind the head could add a weak converging, V-like cue, so the outcome is uncertain and leans slightly toward OFF.\n\n40"}
{"prompt": "circuits+nick", "gid": 6964, "imagenet_class": "recreational vehicle", "frac_of_max": 0.3707, "on": true, "p_on": 0.72, "reasoning": "The photo shows an RV built on a van chassis, with the cab's front clearly visible: a sloped hood, a windshield and the grille region. This matches the \"van front\" input (4d:201), one of the neuron's strong drivers. Vehicle fronts, where the hood and windshield taper together into a pointed, V-like shape, are classic triggers for this neuron's car side. The boxy camper body adds little, but the cab front alone should give a decent response.\n\n72"}
{"prompt": "circuits+nick", "gid": 17713, "imagenet_class": "lesser panda", "frac_of_max": 0.3362, "on": true, "p_on": 0.78, "reasoning": "The red panda's face is the key: it has a fox-like, tapering snout with a dark nose tip, a pointed triangular ear, and white facial markings that form a V toward the muzzle. These closely match the neuron's strongest input, the 4d fox-snout detector, and the \"pointed\" pattern that drives it. Some roundness in the cheeks and face shape could pull it down slightly. Overall, though, this is a close relative of the fox and dog faces that strongly activate it.\n\n78"}
{"prompt": "circuits+nick", "gid": 2519, "imagenet_class": "Siamese cat", "frac_of_max": 0.614, "on": true, "p_on": 0.85, "reasoning": "The two Siamese cats face the camera with large pointed ears and tapering, V-shaped faces, especially with the dark masks narrowing to the chin. This is exactly the frontal cat-face pattern that made 4e:55 famous, and it fits the \"pointed/V-shape\" reading. Being a painting rather than a photo might weaken the response a little, but the faces are clear and fairly large in the frame. The round blue-and-white vase is the kind of rounded shape that pushes it negative, but it shouldn't override the strong pointed cues from the faces.\n\n85"}
{"prompt": "circuits+nick", "gid": 3449, "imagenet_class": "Arctic fox", "frac_of_max": 0.353, "on": true, "p_on": 0.72, "reasoning": "The arctic fox's head is the key: its tapered, pointed snout seen in profile, with small ears, is exactly the V-shaped fox-face pattern that feeds this neuron through its strongest input (4d:493, fox snout). The rest of the scene, a fluffy rounded body and tail on blank snow, offers nothing pointed and if anything leans toward the round pole. The very low white-on-white contrast and the small size of the face weaken the signal somewhat. Still, a fox snout is one of this neuron's most reliable triggers.\n\n72"}
{"prompt": "circuits+nick", "gid": 842, "imagenet_class": "Lakeland terrier", "frac_of_max": 0.0238, "on": false, "p_on": 0.72, "reasoning": "The key feature is the terrier puppy's frontal face: the ears fold down at the top and the face narrows to a dark nose, giving the triangular, V-shaped face pattern this neuron responds to in dogs, foxes and cats. Unlike the flat, rounded faces of mastiffs or boxers that push it negative, this muzzle tapers. The face is fairly small and the fur is fluffy, which may soften the V, but a centered, front-facing canine face like this usually drives the neuron substantially.\n\n72"}
{"prompt": "circuits+nick", "gid": 22875, "imagenet_class": "brass", "frac_of_max": 0.0405, "on": false, "p_on": 0.08, "reasoning": "This is a flat, dark stone plaque covered in lines of engraved text (a JFK memorial inscription). It has none of what drives the neuron: no animal faces with pointed snouts or ears, no car fronts, flames or other converging V-shaped forms. The only geometry is horizontal text rows and a slightly foreshortened rectangle. Text and flat stone texture give a mid-level shape detector like this little to respond to, so it should stay near baseline.\n\n8"}
{"prompt": "circuits+nick", "gid": 7770, "imagenet_class": "bolo tie", "frac_of_max": 0.0472, "on": false, "p_on": 0.2, "reasoning": "The photo is dominated by a large oval stone in a rounded bezel on a bolo tie, a smooth enclosed curve much like the round \"O\" shapes that push this neuron negative. The small silver bird or airplane inlay has some thin pointed edges, but it is tiny and flat. It doesn't form the kind of strong V-shaped, converging structure (snout, car front, flame) that drives the neuron. Overall the image reads as round rather than pointed, so the neuron is likely OFF.\n\n20"}
{"prompt": "circuits+nick", "gid": 28098, "imagenet_class": "chest", "frac_of_max": 0.0318, "on": false, "p_on": 0.15, "reasoning": "This mosaic box is covered in round elements: pebbles, shell discs, circular rosettes and glass cabochons. Those are exactly the round shapes that push this neuron negative. The only pointed structure is the box's corner seen in perspective, but it is weak and cluttered, with no clear V-shaped face, flame or car-front form. So the neuron most likely stays OFF.\n\n15"}
{"prompt": "circuits+nick", "gid": 26399, "imagenet_class": "Ibizan hound", "frac_of_max": 0.3842, "on": true, "p_on": 0.85, "reasoning": "The dog's head is strongly V-shaped. It has tall, pointed upright ears and a tapering snout, and the white blaze runs down to the nose, which reinforces the V. This is exactly the fox- and coyote-like pointed face that drives 4e:55 through inputs like the fox-snout and wolf-face units. The round, fluffy fleece bed is a mild counter-signal, but the face is prominent and well-framed, so the neuron is likely ON.\n\n85"}
{"prompt": "circuits+nick", "gid": 36612, "imagenet_class": "warthog", "frac_of_max": 0.0242, "on": false, "p_on": 0.18, "reasoning": "The photo shows three warthogs lying on gravel, and what dominates is their rounded, bulbous bodies. That is the \"round\" pole that pushes this neuron negative. The faces are mostly hidden or seen from odd angles, so there is no clear V-shaped snout, pointed ear pair or tapered front of the kind that drives it. A small ear or snout on the left animal might give a weak response, but probably not reach 30% of the maximum.\n\n18"}
{"prompt": "circuits+nick", "gid": 8020, "imagenet_class": "snail", "frac_of_max": 0.0224, "on": false, "p_on": 0.06, "reasoning": "The only salient object is a small, round, spiral snail shell on a flat rock surface. That is essentially the neuron's negative pole: round and coiled, like the snail and ball examples that push it negative. Nothing in the image is V-shaped or pointed (no snouts, ears, flames, car fronts or wedge shapes), and the rough rock texture offers no tapering structure. The neuron is very likely OFF.\n\n6"}
{"prompt": "circuits+nick", "gid": 5687, "imagenet_class": "cicada", "frac_of_max": 0.0295, "on": false, "p_on": 0.27, "reasoning": "This photo shows a cicada on a rock, with no cat face, fox snout or car front, which are the neuron's classic triggers. The only candidate \"pointed\" structure is where the folded wings taper into a wedge or tent shape behind the body. The head itself is blunt and rounded, and round shapes push the neuron negative. A weak V cue from the wings might give some activation, but it is unlikely to reach 30% of the neuron's maximum.\n\n27"}
{"prompt": "circuits+nick", "gid": 47951, "imagenet_class": "European fire salamander", "frac_of_max": 0.0463, "on": false, "p_on": 0.18, "reasoning": "What matters here is whether the photo contains a V-shaped, pointed form, such as a tapering snout, a wedge-shaped car front, or a flame-like point. This fire salamander has a blunt, rounded head and round yellow spots on a black body, which lean toward the \"round\" pole that pushes the neuron negative. The gravel background is textured but has no clear V or wedge shapes. The salamander's tapering tail and splayed limbs might give a weak pointed signal, but probably not enough to reach 30% of the neuron's maximum.\n\n18"}
{"prompt": "circuits+nick", "gid": 38802, "imagenet_class": "chiton", "frac_of_max": 0.0461, "on": false, "p_on": 0.1, "reasoning": "The photo shows three chitons: smooth, oval, rounded shells on a plain gray background. Nothing in it forms a pointed V. There are no snouts, pointed ears, angled car fronts or flame-like tips, and the rounded outlines are the kind of shape that pushes this neuron negative. The ovals' slightly tapered ends and the segmented plate texture could give a weak response, but probably not enough to reach 30% of its maximum.\n\n10"}
{"prompt": "circuits+nick", "gid": 4847, "imagenet_class": "gown", "frac_of_max": 0.0362, "on": false, "p_on": 0.28, "reasoning": "The photo shows a gold-patterned halter dress on a dress form. The halter straps and neckline make a V/triangular shape at the top, which is the kind of pointed geometry this neuron is argued to detect. But the scene has none of the strong drivers: no animal snout, no wolf or fox face, no car or van front, no flame. Fabric drapery is soft and mostly vertical rather than sharply converging, so any activation would likely be weak.\n\n28"}
{"prompt": "circuits+nick", "gid": 15650, "imagenet_class": "golfcart", "frac_of_max": 0.3047, "on": true, "p_on": 0.78, "reasoning": "The golf cart is styled with a Mercedes-like car front: a grille, a badge, headlights and a tapered hood, seen from a three-quarter frontal view. Car and van fronts are among this neuron's classic strongest drivers, and the 4d \"van front\" unit feeds it directly. The front end's converging, V-ish geometry fits the \"pointed\" reading. The vehicle is small and boxy rather than sleek, which may weaken the response somewhat, but a clear frontal car face like this usually activates the neuron well.\n\n78"}
{"prompt": "circuits+nick", "gid": 48532, "imagenet_class": "dung beetle", "frac_of_max": 0.0264, "on": false, "p_on": 0.15, "reasoning": "What matters here is overall shape. The dominant object is a round dung ball, and the beetle's body is rounded and glossy, which matches the neuron's negative \"round\" pole. Nothing in the photo forms the V-shaped convergence (snout, car hood, flame) that drives 4e:55. The beetle's head and legs give at most weak pointed cues, so the neuron is likely OFF.\n\n15"}
{"prompt": "circuits+nick", "gid": 18357, "imagenet_class": "window shade", "frac_of_max": 0.0343, "on": false, "p_on": 0.12, "reasoning": "The photo is a flat, brushed-metal panel (like a dishwasher or appliance door) with vertical stripes and a thin rod. It has no converging V-shape, pointed snout, animal face, or car or van front. Its parallel vertical lines and flat surface give the neuron nothing pointed to latch onto, though the metallic sheen faintly resembles car bodywork. The neuron is most likely weak here.\n\n12"}
{"prompt": "circuits+nick", "gid": 34835, "imagenet_class": "Pembroke", "frac_of_max": 0.4076, "on": true, "p_on": 0.92, "reasoning": "This corgi's face is a strong V-shape. Its large pointed ears, tapered snout and white V-shaped blaze down the forehead match the fox- and wolf-face inputs and the coyote-face reader that drive 4e:55. It's a classic fox-like, dog-face case, very similar to the top \"dog\" and \"fox\" examples. Nothing round dominates the image, so there is little to push it negative.\n\n92"}
{"prompt": "circuits+nick", "gid": 8933, "imagenet_class": "cardoon", "frac_of_max": 0.0448, "on": false, "p_on": 0.32, "reasoning": "The purple thistle/artichoke bloom is a radiating burst of fine spikes, and the bracts beneath it are pointed. That spikiness loosely fits the \"pointed\" side of this neuron, and the upward fan of filaments is somewhat flame-like. But nothing here forms the converging V-shaped, face-like or front-end structure (fox snout, cat face, car grille) that drives the neuron most strongly. The rounded overall head of the flower may also pull it toward the \"round\" side, so I expect a weak to moderate response at best.\n\n32"}
{"prompt": "circuits+nick", "gid": 49071, "imagenet_class": "computer keyboard", "frac_of_max": 0.0457, "on": false, "p_on": 0.22, "reasoning": "The photo shows two ergonomic keyboards with broad, gently curved, rounded outlines and regular grids of keys. There is no pointed V-shape, snout, flame or car-front wedge, which is the kind of convergent pointed form that drives this neuron. The rows of keys might faintly resemble a car grille, and the curved black keyboard is loosely hood-like. But the overall shapes are wide and rounded rather than pointed, so the neuron is most likely OFF.\n\n22"}