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

This preprint studies how performance-dependent network evolution (PDNE) can build compact and interpretable reservoir models for neuronal population dynamics.

Using the Wilson-Cowan excitatory-inhibitory system as the target, the evolved reservoirs:

• predict both E(t) and I(t) accurately on unseen stimulus amplitudes,
• generalize zero-shot to new pulse configurations without retraining, and
• recover population-level E-I sign structure for most interaction types as an emergent property.

The results support using performance-evolved reservoirs as data-efficient and interpretable computational surrogates for neuronal digital twins.

Read full preprint here: M. Yadav, Emergent E-I Structure in Performance-Evolved Reservoir Networks of Neuronal Population Dynamics