Modeling state-dependent communication between brain regions with switching nonlinear dynamical systems

Orren Karniol-Tambour, David M. Zoltowski, E. Mika Diamanti, Lucas Pinto, Carlos D Brody, David W. Tank, Jonathan W. Pillow

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Keywords: neuroscience, neural dynamics, dynamical systems, decision making

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TL;DR: We propose a multiregion, switching nonlinear state space model to model neural dynamics and communication

Abstract: Understanding how multiple brain regions interact to produce behavior is a major challenge in systems neuroscience, with many regions causally implicated in common tasks such as sensory processing and decision making. A precise description of interactions between regions remains an open problem. Moreover, neural dynamics are nonlinear and non-stationary. Here, we propose MR-SDS, a multiregion, switching nonlinear state space model that decomposes global dynamics into local and cross-communication components in the latent space. MR-SDS includes directed interactions between brain regions, allowing for estimation of state-dependent communication signals, and accounts for sensory inputs effects. We show that our model accurately recovers latent trajectories, vector fields underlying switching nonlinear dynamics, and cross-region communication profiles in three simulations. We then apply our method to two large-scale, multi-region neural datasets involving mouse decision making. The first includes hundreds of neurons per region, recorded simultaneously at single-cell-resolution across 3 distant cortical regions. The second is a mesoscale widefield dataset of 8 adjacent cortical regions imaged across both hemispheres. On these multi-region datasets, our model outperforms existing piece-wise linear multi-region models and reveals multiple distinct dynamical states and a rich set of cross-region communication profiles.

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Primary Area: applications to neuroscience & cognitive science

Submission Number: 7836