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- W3028366924 abstract "Few models designed to predict responses to natural, time-varying stimuli currently exist - e.g. Linear-Non-Linear (LNL) models, Gabor-based models, and Deep Convolutional Networks (DCNs). DCNs predict neural responses better than LNL-based models but are black-box models and do not offer much information about neural mechanisms. Alternatively, LNL-based models do not accurately predict V1 neural responses to natural, time-varying stimuli, despite incorporating accurate representations of experimentally-measured spatiotemporal receptive fields (STRFs). None of these take into account lateral inhibition, which is thought to make the responses of visual neurons more sparse. We hypothesize that experimentally-measured STRFs do not accurately account for responses to natural, time-varying stimuli by using the Locally-Competitive Algorithm (LCA) to compare neural responses to natural videos with and without lateral inhibition. To simulate responses to novel stimuli without lateral inhibition, a weighted sum between the stimuli and learned filter is performed, followed by a threshold. For lateral inhibition, the same procedure is performed but activity-based competition between nodes is engaged via the LCA algorithm. We observe a very weak correlation between responses generated with and without lateral inhibition, despite using the same classical receptive fields, which may explain why LNL models poorly predict neural responses to natural spatiotemporal stimuli." @default.
- W3028366924 created "2020-05-29" @default.
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- W3028366924 date "2020-03-01" @default.
- W3028366924 modified "2023-09-27" @default.
- W3028366924 title "Can Lateral Inhibition for Sparse Coding Help Explain V1 Neuronal Responses To Natural Stimuli?" @default.
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- W3028366924 doi "https://doi.org/10.1109/ssiai49293.2020.9094598" @default.
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