Neural Comput - Learning quadratic receptive fields from neural responses to natural stimuli.

Tópicos

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Resumo

Models of neural responses to stimuli with complex spatiotemporal correlation structure often assume that neurons are selective for only a small number of linear projections of a potentially high-dimensional input. In this review, we explore recent modeling approaches where the neural response depends on the quadratic form of the input rather than on its linear projection, that is, the neuron is sensitive to the local covariance structure of the signal preceding the spike. To infer this quadratic dependence in the presence of arbitrary (e.g., naturalistic) stimulus distribution, we review several inference methods, focusing in particular on two information theory-based approaches (maximization of stimulus energy and of noise entropy) and two likelihood-based approaches (Bayesian spike-triggered covariance and extensions of generalized linear models). We analyze the formal relationship between the likelihood-based and information-based approaches to demonstrate how they lead to consistent inference. We demonstrate the practical feasibility of these procedures by using model neurons responding to a flickering variance stimulus.

Resumo Limpo

model neural respons stimuli complex spatiotempor correl structur often assum neuron select small number linear project potenti highdimension input review explor recent model approach neural respons depend quadrat form input rather linear project neuron sensit local covari structur signal preced spike infer quadrat depend presenc arbitrari eg naturalist stimulus distribut review sever infer method focus particular two inform theorybas approach maxim stimulus energi nois entropi two likelihoodbas approach bayesian spiketrigg covari extens general linear model analyz formal relationship likelihoodbas informationbas approach demonstr lead consist infer demonstr practic feasibl procedur use model neuron respond flicker varianc stimulus

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