Emergent E-I Structure in Performance-Evolved Reservoir Networks of Neuronal Population Dynamics

TL;DR

This paper demonstrates that performance-driven evolution of reservoir networks can produce compact models that accurately predict neuronal population dynamics and recover underlying excitatory-inhibitory structures without explicit design.

Contribution

The study introduces a method to evolve reservoir networks based solely on prediction performance, resulting in models that are both accurate and structurally interpretable of neuronal E-I interactions.

Findings

Evolved networks predict E(t) and I(t) across unseen stimuli.

Networks generalize to novel stimulus configurations without retraining.

Connectivity recovers the excitatory-inhibitory sign pattern of the Wilson-Cowan model.

Abstract

Understanding how network structure gives rise to neuronal dynamics and whether compact computational models can recover that structure from data alone is a central challenge in computational neuroscience. We apply the performance-dependent network evolution (PDNE) framework to model the dynamics of the Wilson-Cowan (WC) neuronal system, a canonical two-population model of excitatory-inhibitory (E-I) interaction underlying physiological rhythms. Starting from a minimal seed network, PDNE iteratively grows and prunes a reservoir computing (RC) network based solely on prediction performance, yielding compact, task-optimized reservoirs networks. The evolved networks accurately predict both excitatory $E(t)$ and inhibitory $I(t)$ population activities across unseen stimulus amplitudes and generalize in a zero-shot manner to novel stimulus configurations: varying pulse number, position and amplitude without retraining. Structural analysis of the evolved networks reveals a consistent functional organization with nodes specialized for E, I, and shared E-I representations. Importantly, the population-level connectivity of the evolved reservoirs spontaneously recovers the correct excitatory-inhibitory sign pattern of the WC model for three of four interaction types, without this being imposed by design. These results demonstrate that performance-driven network evolution can produce not only accurate but structurally interpretable models of physiological rhythms, opening a path toward compact, data-efficient digital twins of neuronal systems.