Neural Comput - Decorrelation of spiking variability and improved information transfer through feedforward divisive normalization.

Tópicos

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Resumo

Response variability is often positively correlated in pairs of similarly tuned neurons in the visual cortex. Many authors have considered correlated variability to prevent postsynaptic neurons from averaging across large groups of inputs to obtain reliable stimulus estimates. However, a simple average of variability ignores nonlinearities in cortical signal integration. This study shows that feedforward divisive normalization of a neuron's inputs effectively decorrelates their variability. Furthermore, we show that optimal linear estimates of a stimulus parameter that are based on normalized inputs are more accurate than those based on nonnormalized inputs, due partly to reduced correlations, and that these estimates improve with increasing population size up to several thousand neurons. This suggests that neurons may possess a simple mechanism for substantially decorrelating noise in their inputs. Further work is needed to reconcile this conclusion with past evidence that correlated noise impairs visual perception.

Resumo Limpo

respons variabl often posit correl pair similar tune neuron visual cortex mani author consid correl variabl prevent postsynapt neuron averag across larg group input obtain reliabl stimulus estim howev simpl averag variabl ignor nonlinear cortic signal integr studi show feedforward divis normal neuron input effect decorrel variabl furthermor show optim linear estim stimulus paramet base normal input accur base nonnorm input due part reduc correl estim improv increas popul size sever thousand neuron suggest neuron may possess simpl mechan substanti decorrel nois input work need reconcil conclus past evid correl nois impair visual percept

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