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We investigated the computational role of reciprocity in recurrent neural circuits by systematically varying the reciprocity parameter ($\rho$) across simulations while controlling for network size and connectivity. Results show that low reciprocity enhances stimulus-driven activity and performance on continuous-input tasks, while higher reciprocity improves performance on delayed-memory tasks by increasing local stability at the cost of representational capacity and task-state separability. This reveals a fundamental three-way trade-off mediated by reciprocity, impacting capacity, stability, and separability, with optimal behavior contingent on the task's reliance on stability versus state discrimination.
Reciprocity in neural circuits orchestrates a critical trade-off: boosting stability by sacrificing representational capacity and state separability.
Biological neural circuits contain both unidirectional and reciprocal connections, but how reciprocity affects circuit computation remains unclear. We varied reciprocity \r{ho} across 21 levels in recurrent-network simulations while holding network size, connection density, and directed-edge count fixed. Low-reciprocity networks produced more diverse stimulus-driven activity and performed better on four continuous-input tasks, whereas higher reciprocity generally improved performance on three delayed-memory tasks. Increasing reciprocity reduced online memory capacity and accelerated recovery from perturbations. A post-cue noise experiment showed that higher reciprocity suppressed internal noise propagation but made cue-evoked states less distinguishable, and component matching separated the contributions of cue separation and within-cue dispersion to readout robustness. Within this ensemble, reciprocity thus mediates a three-way trade-off among representational capacity, local stability, and task-state separability: higher reciprocity lowers capacity, strengthens stability, and compresses separability. Whether it improves behaviour depends on whether the stability gain outweighs the lost separation between task states. Connectome and microcircuit datasets from five species showed intermediate reciprocity, while reciprocity varied substantially across adult Drosophila brain regions, providing compatible structural context and motivating circuit-specific functional tests.