Mixed recurrent connectivity in primate prefrontal cortex.

ABSTRACT: The functional properties of a network depend on its connectivity, which includes the strength of its inputs and the strength of the connections between its units, or recurrent connectivity. Because we lack a detailed description of the recurrent connectivity in the lateral prefrontal cortex of primates, we developed an indirect method to estimate it. This method leverages the elevated noise correlation of mutually-connected units. To estimate the connectivity of prefrontal regions, we trained recurrent neural network models with varying percentages of bump attractor connectivity and noise levels to match the noise correlation properties observed in two specific prefrontal regions: the dorsolateral prefrontal cortex and the frontal eye field. We found that models initialized with approxima

SUBMITTER: Sigalas E 

PROVIDER: S-EPMC11918408 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature