Nonlinear multiregion neural dynamics with parametric impulse response communication channels
Matthew Dowling Cristina Savin
Cognition arises from the coordinated interaction of brain regions with distinct computational roles. Despite improvements in our ability to extract the dynamics underlying circuit computation from population activity recorded in individual areas, understanding how multiple areas jointly support distributed computation remains a challenge. As part of this effort, we propose a multi-region neural dynamics model composed of two building blocks:  _i)_ within-region (potentially driven) nonlinear dynamics and _ii)_ communication channels between regions, parameterized through their impulse response. Together, these choices make it possible to learn nonlinear neural population dynamics and understand the flow of information between regions by drawing from the rich literature of linear systems theory.  We develop a state noise inversion free variational filtering and learning algorithm for our model and show, through neuroscientifically inspired numerical experiments, how the proposed model can reveal interpretable characterizations of the local computations within and the flow of information between neural populations.  We further validate the efficacy of our approach using simultaneous population recordings from areas V1 and V2.
neural dynamics, multiregion, variational inference
a multiregion neural population dynamics model capable of extracting nonlinear neural population dynamics with communication channels between regions parameterized by their impulse response.