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Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons

Wen-Hao Zhang, Si Wu, Krešimir Josić, Brent Doiron
doi: https://doi.org/10.1101/2022.01.26.477877
Wen-Hao Zhang
1Departments of Neurobiology and Statistics, University of Chicago, Chicago, IL, USA
2Grossman Center for Quantiative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
3Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
4Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
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Si Wu
5School of Electronics Engineering and Computer Science, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
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Krešimir Josić
6Department of Mathematics, University of Houston, TX, USA
7Department of Biology and Biochemistry, University of Houston, TX, USA
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  • For correspondence: bdoiron@uchicago.edu kresimir.josic@gmail.com
Brent Doiron
1Departments of Neurobiology and Statistics, University of Chicago, Chicago, IL, USA
2Grossman Center for Quantiative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
3Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
4Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
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  • For correspondence: bdoiron@uchicago.edu kresimir.josic@gmail.com
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Abstract

Two facts about cortex are widely accepted: neuronal responses show large spiking variability with near Poisson statistics and cortical circuits feature abundant recurrent connections between neurons. How these spiking and circuit properties combine to support sensory representation and information processing is not well understood. We build a theoretical framework showing that these two ubiquitous features of cortex combine to produce optimal sampling-based Bayesian inference. Recurrent connections store an internal model of the external world, and Poissonian variability of spike responses drives flexible sampling from the posterior stimulus distributions obtained by combining feedforward and recurrent neuronal inputs. We illustrate how this framework for sampling-based inference can be used by cortex to represent latent multivariate stimuli organized either hierarchically or in parallel. A neural signature of such network sampling are internally generated differential correlations whose amplitude is determined by the prior stored in the circuit, which provides an experimentally testable prediction for our framework.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 28, 2022.
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Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons
Wen-Hao Zhang, Si Wu, Krešimir Josić, Brent Doiron
bioRxiv 2022.01.26.477877; doi: https://doi.org/10.1101/2022.01.26.477877
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Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons
Wen-Hao Zhang, Si Wu, Krešimir Josić, Brent Doiron
bioRxiv 2022.01.26.477877; doi: https://doi.org/10.1101/2022.01.26.477877

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