Tuning Diversity Improves Discrimination and Detection Performance under Metabolic Constraints

Abstract

Cortical populations exhibit a wide range of tuning properties, raising the question of whether such variability is a feature or a bug of cortical function. Prior work has shown that tuning diversity can improve population codes by mitigating the effects of correlated noise and increasing the discrimination and identification capacity of geometric representations. Motivated by these findings, we study a model in which a heterogeneous family of tuning curves, coding for a circular variable, is replicated at equally spaced preferred angles. We show that this heterogeneous population achieves better discrimination and detection than an equally sized homogeneous population constructed from shifted copies of the familys mean tuning curve, while using the same spike budget. Thus, homogeneous tuning is unstable under perturbations that preserve the mean tuning curve, because such perturbations leave metabolic cost unchanged while improving coding performance. We propose that such instability creates evolutionary pressure toward heterogeneity of tuning, making its prevalence a consequence of a process that optimizes coding performance under metabolic constraints. Significance StatementThe tuning curves of cortical neurons vary substantially, raising the question of whether this diversity has functional significance or merely reflects biological noise. We show that perturbing an initially homogeneous population while preserving its mean tuning curve produces a heterogeneous population with better discrimination and detection performance at the same metabolic cost. Thus, tuning diversity may emerge as a natural consequence of selection for efficient coding under metabolic constraints.