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Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex

Published version
Peer-reviewed

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Authors

Orbán, G 
Berkes, P 
Fiser, J 

Abstract

Neural responses in the visual cortex are variable, and there is now an abundance of data characterizing how the magnitude and structure of this variability depends on the stimulus. Current theories of cortical computation fail to account for these data; they either ignore variability altogether or only model its unstructured Poisson-like aspects. We develop a theory in which the cortex performs probabilistic inference such that population activity patterns represent statistical samples from the inferred probability distribution. Our main prediction is that perceptual uncertainty is directly encoded by the variability, rather than the average, of cortical responses. Through direct comparisons to previously published data as well as original data analyses, we show that a sampling-based probabilistic representation accounts for the structure of noise, signal, and spontaneous response variability and correlations in the primary visual cortex. These results suggest a novel role for neural variability in cortical dynamics and computations.

Description

Keywords

Bayesian computations, V1, natural images, noise correlations, normative model, spontaneous activity, stochastic sampling, theory, variability, vision

Journal Title

Neuron

Conference Name

Journal ISSN

0896-6273
1097-4199

Volume Title

92

Publisher

Elsevier
Sponsorship
Wellcome Trust (095621/Z/11/Z)
European Union-FP7 (Marie Curie Intra-European Fellowship, Marie Curie CIG), Hungarian Academy of Sciences (Lendulet Award), Swartz Foundation, Swiss National Science Foundation, National Science Foundation, Wellcome Trust