Latent circuit inference from heterogeneous neural responses during cognitive tasks.

ABSTRACT: Higher cortical areas carry a wide range of sensory, cognitive and motor signals mixed in heterogeneous responses of single neurons tuned to multiple task variables. Dimensionality reduction methods that rely on correlations between neural activity and task variables leave unknown how heterogeneous responses arise from connectivity to drive behavior. We develop the latent circuit model, a dimensionality reduction approach in which task variables interact via low-dimensional recurrent connectivity to produce behavioral output. We apply the latent circuit inference to recurrent neural networks trained to perform a context-dependent decision-making task and find a suppression mechanism in which contextual representations inhibit irrelevant sensory responses. We validate this mechanism by conf

SUBMITTER: Langdon C 

PROVIDER: S-EPMC11893458 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature