Representation of visual uncertainty through neural gain variability
| Autor: | Kristof Meding, Robbe L. T. Goris, Zoe M. Boundy-Singer, Olivier J. Hénaff, Corey M. Ziemba |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
0301 basic medicine Visual perception Computer science media_common.quotation_subject Science Models Neurological Population Computer Science::Neural and Evolutionary Computation General Physics and Astronomy Sensory system Stimulus (physiology) Macaque Article General Biochemistry Genetics and Molecular Biology 03 medical and health sciences 0302 clinical medicine Perception biology.animal medicine Biological neural network Animals lcsh:Science education Visual Cortex media_common Neurons education.field_of_study Multidisciplinary biology Quantitative Biology::Neurons and Cognition business.industry Uncertainty Pattern recognition General Chemistry Macaca mulatta Neural encoding 030104 developmental biology Visual cortex medicine.anatomical_structure Visual Perception lcsh:Q Artificial intelligence Visual system business 030217 neurology & neurosurgery |
| Zdroj: | Nature Communications, Vol 11, Iss 1, Pp 1-12 (2020) Nature Communications |
| ISSN: | 2041-1723 |
| Popis: | 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. How does the brain represent sensory uncertainty? The authors find that neural gain variability tracks stimulus uncertainty across the visual hierarchy and explain their findings with a simple generalization of canonical models of neural computation. |
| Databáze: | OpenAIRE |
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