Distributing task-related neural activity across a cortical network through task-independent connections

Christopher M. Kim (), Arseny Finkelstein, Carson C. Chow, Karel Svoboda and Ran Darshan ()
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Christopher M. Kim: National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health
Arseny Finkelstein: Tel Aviv University
Carson C. Chow: National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health
Karel Svoboda: Howard Hughes Medical Institute
Ran Darshan: Howard Hughes Medical Institute

Nature Communications, 2023, vol. 14, issue 1, 1-21

Abstract: Abstract Task-related neural activity is widespread across populations of neurons during goal-directed behaviors. However, little is known about the synaptic reorganization and circuit mechanisms that lead to broad activity changes. Here we trained a subset of neurons in a spiking network with strong synaptic interactions to reproduce the activity of neurons in the motor cortex during a decision-making task. Task-related activity, resembling the neural data, emerged across the network, even in the untrained neurons. Analysis of trained networks showed that strong untrained synapses, which were independent of the task and determined the dynamical state of the network, mediated the spread of task-related activity. Optogenetic perturbations suggest that the motor cortex is strongly-coupled, supporting the applicability of the mechanism to cortical networks. Our results reveal a cortical mechanism that facilitates distributed representations of task-variables by spreading the activity from a subset of plastic neurons to the entire network through task-independent strong synapses.

Date: 2023
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DOI: 10.1038/s41467-023-38529-y

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