Extended Data Fig. 7: Identifying real V4 dot detectors with experimental validation.
From: Compact deep neural network models of the visual cortex
To confirm the presence of dot-detecting V4 neurons, we ran validation experiments specifically tailored for compact models that resemble dot detectors. We first identified compact models by training on previous recording sessions. From the identified compact models, we chose 5 compact models that most resembled dot detectors based on their stimulus preferences (response-maximizing natural and synthesized images) and their responses to artificial dot stimuli (see Fig. 4a,b and Extended Data Fig. 8). We note that the chosen dot detecting compact model in Fig. 4 was not one of the chosen, as this compact model matched to a V4 neuron from another animal. For a future recording session, we presented the response-maximizing natural and synthesized images of the five chosen models as well as artificial dot stimuli (same dot stimuli as in Fig. 4b and Extended Data Fig. 8b). We identified the five recorded V4 neurons that best matched the predictions of the five compact models (by computing the noise-corrected R2 from all other images shown in the session, same procedure as in Fig. 2b–f) and show their responses here. a. The response-maximizing natural images (left, examples) and response-maximizing synthesized images (middle, examples) chosen from the five compact models tended to more strongly drive responses than responses to randomly-chosen natural images (right, ‘resp.-max. synth. images’ and ‘resp.-max. natural images’ dots more to the right than ‘natural’ black dots). Each dot is the repeat-averaged response to one image; lines denote medians. All response-maximizing stimuli yielded median responses significantly greater than the median response to natural images (p < 0.02, one-sided permutation test, n = 10, asterisks) except one set of maximizing synthesized stimuli (bottom row, V4 neuron 5, red dots, p = 0.922, one-sided permutation test, n = 10). The response-maximizing natural images were reproduced from Adobe Stock. b. Real V4 responses to the artificial dot stimuli that varied in dot location (left), dot size (middle) and number of dots (right). Same format as in Fig. 4b and Extended Data Fig. 8b. Dot locations were subsampled to 28 × 28 locations to limit the number of images. Error bars in ‘vary dot number’ denote 1 s.e.m. across 10 different images, where each image had the same number of dots but in randomly-chosen, non-overlapping locations within 25 pixels of the preferred dot location. We found that these V4 neurons had preferred dot locations (left column), preferred dot sizes (middle column), and preferred numbers of dots (right column), consistent with these V4 neurons being dot detectors. Thus, these results provide strong evidence for the presence of dot detectors in V4. We observed diverse selectivity to dot size, including V4 neurons selective to the tiniest dots (neurons 1, 2, and 5) and small dots (neurons 3 and 4). Similarly, we observed selectivity to one dot (neurons 2 and 4), 2 dots (neuron 3), and 3 or more dots (neurons 1 and 5). Thus, even within the class of dot detectors, there appears to be large diversity in stimulus preferences.
