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. Moreover, neural dynamics are nonlinear and non-stationary, exhibiting switches both within and across trials. Here we propose multi-region switching dynamical systems (MR-SDS), a switching nonlinear state space model that decomposes multi-region neural dynamics into local and cross-region components. MR-SDS includes directed interactions between brain regions, allowing for estimation of state-dependent communication signals and 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 distal cortical regions. The second is a mesoscale widefield dataset of 8 adjacent cortical regions imaged across both hemispheres. On these multi-region datasets, MR-SDS outperforms existing models, including multi-region recurrent switching linear models, and reveals multiple distinct dynamical states and a rich set of cross-region communication profiles.
Original language | English (US) |
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State | Published - 2024 |
Event | 12th International Conference on Learning Representations, ICLR 2024 - Hybrid, Vienna, Austria Duration: May 7 2024 → May 11 2024 |
Conference
Conference | 12th International Conference on Learning Representations, ICLR 2024 |
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Country/Territory | Austria |
City | Hybrid, Vienna |
Period | 5/7/24 → 5/11/24 |
All Science Journal Classification (ASJC) codes
- Language and Linguistics
- Computer Science Applications
- Education
- Linguistics and Language