Generalizing biological surround suppression based on center surround similarity via deep neural network models.

ABSTRACT: Sensory perception is dramatically influenced by the context. Models of contextual neural surround effects in vision have mostly accounted for Primary Visual Cortex (V1) data, via nonlinear computations such as divisive normalization. However, surround effects are not well understood within a hierarchy, for neurons with more complex stimulus selectivity beyond V1. We utilized feedforward deep convolutional neural networks and developed a gradient-based technique to visualize the most suppressive and excitatory surround. We found that deep neural networks exhibited a key signature of surround effects in V1, highlighting center stimuli that visually stand out from the surround and suppressing responses when the surround stimulus is similar to the center. We found that in some neurons, especi

SUBMITTER: Pan X 

PROVIDER: S-EPMC10550176 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature