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

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 synapses conveying incremental information to the attractor are plastic, allowing the integration gain to become transiently inhomogeneous during recalibration. In the second, plasticity is implemented in other components of the network, keeping the gain homogeneous during recalibration. Both mechanisms require explicit error signals that drive plasticity. We instantiate each mechanism within a CBAN, demonstrating their potential for biologically plausible, adaptive coding of continuous variables. How the brain adjusts the neural integration gain to keep its neural codes for continuous variables accurate is unknown. Using bump attractor networks, the authors derive the principles of this gain adjustment and identify two biologically plausible, error-driven mechanisms.