Neural Comput - Attention as reward-driven optimization of sensory processing.

Tópicos

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Resumo

Attention causes diverse changes to visual neuron responses, including alterations in receptive field structure, and firing rates. A common theoretical approach to investigate why sensory neurons behave as they do is based on the efficient coding hypothesis: that sensory processing is optimized toward the statistics of the received input. We extend this approach to account for the influence of task demands, hypothesizing that the brain learns a probabilistic model of both the sensory input and reward received for performing different actions. Attention-dependent changes to neural responses reflect optimization of this internal model to deal with changes in the sensory environment (stimulus statistics) and behavioral demands (reward statistics). We use this framework to construct a simple model of visual processing that is able to replicate a number of attention-dependent changes to the responses of neurons in the midlevel visual cortices. The model is consistent with and provides a normative explanation for recent divisive normalization models of attention (Reynolds & Heeger, 2009).

Resumo Limpo

attent caus divers chang visual neuron respons includ alter recept field structur fire rate common theoret approach investig sensori neuron behav base effici code hypothesi sensori process optim toward statist receiv input extend approach account influenc task demand hypothes brain learn probabilist model sensori input reward receiv perform differ action attentiondepend chang neural respons reflect optim intern model deal chang sensori environ stimulus statist behavior demand reward statist use framework construct simpl model visual process abl replic number attentiondepend chang respons neuron midlevel visual cortic model consist provid normat explan recent divis normal model attent reynold heeger

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