Neural Information Processing



New article published in "Nature Communications"

Title: "Representation of visual uncertainty through neural gain variability" by Olivier J. Hénaff, Zoe M. Boundy-Singer, Kristof Meding, Corey M. Ziemba and Robbe L. T. Goris

Uncertainty is intrinsic to perception. Neural circuits which process sensory information musttherefore 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 encodesstimulus features, while cross-neuron variability in response gain encodes the uncertainty ofthese features. To test this model, we studied spiking activity of neurons in macaque V1 andV2 elicited by repeated presentations of stimuli whose uncertainty was manipulated in dis-tinct ways. We show that gain variability of individual neurons is tuned to stimulus uncer-tainty, that this tuning is specific to the features encoded by these neurons and largelyinvariant to the source of uncertainty. We demonstrate that this behavior naturally arisesfrom known gain-control mechanisms, and illustrate how downstream circuits can jointlydecode stimulus features and their uncertainty from sensory population activity.

Please have a look at our publications page to see the whole paper