Inferring brain-wide interactions using data-constrained recurrent neural network models
Year: 2026
Type: article
Abstract: Behavior arises from the coordinated activity across anatomically and functionally distinct brain regions. Modern experimental tools allow unprecedented access to large neural populations spanning many interacting regions brain-wide. Yet, understanding such large-scale datasets necessitates robust, scalable computational models to extract meaningful features of inter-region communication and principled theories to interpret those features. Here, we introduce current-based decomposition (CURBD), an approach for inferring brain-wide interactions using data-constrained recurrent neural network models that autonomously produce dynamics consistent with experimentally obtained neural data. CURBD leverages the functional interactions inferred from such models to reveal directional currents between multiple brain regions simultaneously. We first show that CURBD accurately isolates inter-region currents in simulated, ground-truth networks with known connectivity and dynamics. We then apply CURBD to multi-region neural recordings obtained from many species-larval zebrafish, mice, macaques, and humans-to demonstrate the widespread applicability of CURBD in untangling brain-wide interactions and inter-area communication principles underlying behavior.
Source: Neuron
Institutions: Mila - Quebec Artificial Intelligence Institute, Université de Montréal, Icahn School of Medicine at Mount Sinai, Harvard University, Cedars-Sinai Medical Center, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, California Institute of Technology, Stanford University, Howard Hughes Medical Institute
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $10,400
Funders: National Science Foundation, McKnight Endowment Fund for Neuroscience, Brain and Behavior Research Foundation, James S. McDonnell Foundation, European Molecular Biology Organization, McKnight Foundation, National Alliance for Research on Schizophrenia and Depression, Chan Zuckerberg Initiative, Gatsby Charitable Foundation, National Institutes of Health, Fonds de Recherche du Québec - Santé, National Institute of Mental Health, National Institute on Drug Abuse, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering
Awards: Toward mechanistic cognitive neuroscience: cell types, connectivity, and patterned perturbations, G138391424, G2070036510, G208005389, Relating structure and function in synapse-level wiring diagrams, G395169362, G434751032, CAREER:Coordination of neural activity during memory formation in humans, Multi-region 'Network of Networks' Recurrent Neural Network Models of Adaptive and Maladaptive Learning, Neural mechanisms of affective processing in prefrontal-limbic circuits, NCS-FO: State Representations in Multi-purpose and Multi-region Neural Network Models of Cognition, Integrative theory of memory and cognitive processes, CAREER: Untangling Inter-Area Communication in the Brain Using Multi-Region Neural Networks, Neural Network Models Constrained by Multiscale Data to Infer Minimal Functional Motifs in the Brain, G9000008458, Neural circuit mechanisms of affective probabilistic learning, Parietal Cortex Networks for Sensorimotor Processing During Navigation
OpenAlex ID: W3114251522