Representation of visual uncertainty through neural gain variability

Olivier J Hénaff,Corey M Ziemba,Zoe M Boundy-Singer,Kristof Meding,Robbe L T Goris

doi:10.1038/s41467-020-15533-0

Abstract

Uncertainty is intrinsic to perception. Neural circuits which process sensory information must therefore also represent the reliability of this information. How they do so is a topic of debate. We propose a model of visual cortex in which average neural response strength encodes stimulus features, while cross-neuron variability in response gain encodes the uncertainty of these features. To test this model, we studied spiking activity of neurons in macaque V1 and V2 elicited by repeated presentations of stimuli whose uncertainty was manipulated in distinct ways. We show that gain variability of individual neurons is tuned to stimulus uncertainty, that this tuning is specific to the features encoded by these neurons and largely invariant to the source of uncertainty. We demonstrate that this behavior naturally arises from known gain-control mechanisms, and illustrate how downstream circuits can jointly decode stimulus features and their uncertainty from sensory population activity.

Highlights

IntroductionWe propose a model of visual cortex in which average neural response strength encodes stimulus features, while cross-neuron variability in response gain encodes the uncertainty of these features
We found that gain variability selectively depends on stimulus uncertainty, and that this selectivity is roughly invariant to the source of uncertainty
We propose that cortical neurons behave as if two different receptive fields underlie these response statistics

Summary

Introduction

We propose a model of visual cortex in which average neural response strength encodes stimulus features, while cross-neuron variability in response gain encodes the uncertainty of these features. To test this model, we studied spiking activity of neurons in macaque V1 and V2 elicited by repeated presentations of stimuli whose uncertainty was manipulated in distinct ways. When humans and other animals perform perceptual tasks, they often follow these normative predictions[3,4,5,6] These behavioral effects imply that the neural circuits which mediate perception assess the uncertainty of sensory information. This model quantitatively matches the effects of stimulus uncertainty on response variability

Methods

Results

Conclusion