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Paper: PS-1B.72
Session: Poster Session 1B
Location: H Fl├Ąche 1.OG
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: A Functional Model of Neuronal Response Variability in Primary Visual Cortex
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Dylan Festa, Amir Aschner, Adam Kohn, Ruben Coen-Cagli, Albert Einstein College of Medicine, United States
Abstract: Responses of sensory neurons to repeated presentations of identical stimuli are variable. Despite extensive studies on the structure and mechanisms of this variability, its functional role remains debated. Here we propose and test a functional account of both response selectivity and variability, based on two recent hypotheses about neural coding: first, that probabilistic inference about localized visual features explains how primary visual cortex (V1) neurons integrate information inside and outside their receptive fields (RFs). Second, that the inferred probability distribution is reflected in the across-trial distribution of neuronal responses (termed sampling hypothesis), and therefore higher uncertainty in the inference implies higher variability. The resulting model predicts that stimuli surrounding the RF should reduce response variability reflecting that surround information reduces uncertainty about stimuli inside the RF. We test the predictions on macaque V1 responses to compound gratings and natural images. We find that variability is generally suppressed by stimuli extending beyond the RF; that the suppression is weaker for uninformative surrounds (i.e. with mismatched orientation); and that the modulation of variability and average firing rate can be dissociated. Our results offer strong evidence for a functional role of cortical variability in probabilistic inference.