Inferring brain-wide interactions using data-constrained recurrent neural network models
Wednesday, March 24, 2021
17:00 UTC
Matthew Perich
Rajan lab, Icahn School of Medicine at Mount Sinai
Recording
Open recording
Hosted by WWNeuRise35 minutes
Abstract
Behavior arises from the coordinated activity of numerous distinct brain regions. Modern experimental tools allow access to neural populations brain-wide, yet understanding such large-scale datasets necessitates scalable computational models to extract meaningful features of inter-region communication. In this talk, I will introduce Current-Based Decomposition (CURBD), an approach for inferring multi-region interactions using data-constrained recurrent neural network models. I will first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. I will then apply CURBD to understand the brain-wide flow of information leading to behavioral state transitions in larval zebrafish. These examples will establish CURBD as a flexible, scalable framework to infer brain-wide interactions that are inaccessible from experimental measurements alone.
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