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Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations

Joshua I. Glaser, Matthew Whiteway, John P. Cunningham, Liam Paninski, Scott W. Linderman
doi: https://doi.org/10.1101/2020.10.21.349282
Joshua I. Glaser
1Department of Statistics, Columbia University
2Center for Theoretical Neuroscience, Columbia University
3Zuckerman Mind Brain Behavior Institute, Columbia University
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  • For correspondence: j.glaser@columbia.edu
Matthew Whiteway
2Center for Theoretical Neuroscience, Columbia University
3Zuckerman Mind Brain Behavior Institute, Columbia University
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John P. Cunningham
1Department of Statistics, Columbia University
2Center for Theoretical Neuroscience, Columbia University
3Zuckerman Mind Brain Behavior Institute, Columbia University
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Liam Paninski
1Department of Statistics, Columbia University
2Center for Theoretical Neuroscience, Columbia University
3Zuckerman Mind Brain Behavior Institute, Columbia University
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Scott W. Linderman
4Department of Statistics and Wu Tsai Neurosciences Institute, Stanford University
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Abstract

Modern recording techniques can generate large-scale measurements of multiple neural populations over extended time periods. However, it remains a challenge to model non-stationary interactions between high-dimensional populations of neurons. To tackle this challenge, we develop recurrent switching linear dynamical systems models for multiple populations. Here, each high-dimensional neural population is represented by a unique set of latent variables, which evolve dynamically in time. Populations interact with each other through this low-dimensional space. We allow the nature of these interactions to change over time by using a discrete set of dynamical states. Additionally, we parameterize these discrete state transition rules to capture which neural populations are responsible for switching between interaction states. To fit the model, we use variational expectation-maximization with a structured mean-field approximation. After validating the model on simulations, we apply it to two different neural datasets: spiking activity from motor areas in a non-human primate, and calcium imaging from neurons in the nematode C. elegans. In both datasets, the model reveals behaviorally-relevant discrete states with unique inter-population interactions and different populations that predict transitioning between these states.

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 4.0 International license.
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Posted October 22, 2020.
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Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations
Joshua I. Glaser, Matthew Whiteway, John P. Cunningham, Liam Paninski, Scott W. Linderman
bioRxiv 2020.10.21.349282; doi: https://doi.org/10.1101/2020.10.21.349282
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Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations
Joshua I. Glaser, Matthew Whiteway, John P. Cunningham, Liam Paninski, Scott W. Linderman
bioRxiv 2020.10.21.349282; doi: https://doi.org/10.1101/2020.10.21.349282

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