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• W4386953728 endingPage "e1011486" @default.
• W4386953728 startingPage "e1011486" @default.
• W4386953728 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, especially in late layers, when the center stimulus was altered, the most suppressive surround surprisingly can follow the change. Through the visualization approach, we generalized previous understanding of surround effects to more complex stimuli, in ways that have not been revealed in visual cortices. In contrast, the suppression based on center surround similarity was not observed in an untrained network. We identified further successes and mismatches of the feedforward CNNs to the biology. Our results provide a testable hypothesis of surround effects in higher visual cortices, and the visualization approach could be adopted in future biological experimental designs." @default.
• W4386953728 created "2023-09-23" @default.
• W4386953728 creator A5028553478 @default.
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• W4386953728 date "2023-09-22" @default.
• W4386953728 modified "2023-10-14" @default.
• W4386953728 title "Generalizing biological surround suppression based on center surround similarity via deep neural network models" @default.
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• W4386953728 doi "https://doi.org/10.1371/journal.pcbi.1011486" @default.
• W4386953728 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37738258" @default.
• W4386953728 hasPublicationYear "2023" @default.
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