Explicit error coding can mediate gain recalibration in continuous bump attractor networks.

ABSTRACT: Continuous bump attractor networks (CBANs) are a prevailing model for how neural circuits represent continuous variables. CBANs maintain these representations by temporally integrating inputs that encode differential (i.e., incremental) changes to a given variable. The accuracy of this computation hinges on a precisely tuned integration gain. Experiments have shown that the brain can recalibrate this gain using ground-truth sensory information, yet existing CBAN models rely on biologically implausible or currently unknown plasticity rules for recalibration. Here, we demonstrate that ring-type CBANs can recalibrate their integration gain through two mechanisms that rely on well-established, biologically plausible forms of plasticity. In the first mechanism, the spatially distributed synapse

SUBMITTER: Secer G 

PROVIDER: S-EPMC12534468 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature