title: Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex.
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.
authors.affiliation: Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK; MTA Wigner Research Center for Physics, Budapest 1121, Hungary. Electronic address: orban.gergo@wigner.mta.hu.
authors.forename: Gergő
authors.initials: G
authors.lastname: Orbán
authors.affiliation: Volen National Center for Complex Systems, Brandeis University, Waltham, MA 02454, USA.
authors.forename: Pietro
authors.initials: P
authors.lastname: Berkes
authors.affiliation: Volen National Center for Complex Systems, Brandeis University, Waltham, MA 02454, USA; Department of Cognitive Science, Central European University, Budapest 1051, Hungary.
authors.forename: József
authors.initials: J
authors.lastname: Fiser
authors.affiliation: Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK; Department of Cognitive Science, Central European University, Budapest 1051, Hungary.
authors.forename: Máté
authors.initials: M
authors.lastname: Lengyel
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D011336:Probability; D013269:Stochastic Processes; D035501:Uncertainty; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*
keywords: Bayesian computations; V1; natural images; noise correlations; normative model; spontaneous activity; stochastic sampling; theory; variability; vision