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Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex
Title / Series / Name
Neuron
Publication Volume
92
Publication Issue
2
Pages
Editors
Keywords
Bayesian computations
V1
natural images
noise correlations
normative model
spontaneous activity
stochastic sampling
theory
variability
vision
General Neuroscience
V1
natural images
noise correlations
normative model
spontaneous activity
stochastic sampling
theory
variability
vision
General Neuroscience
URI
https://hdl.handle.net/20.500.14018/28593
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.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2016-10-19
Language
ISBN
Identifiers
10.1016/j.neuron.2016.09.038