Emergent E-I Structure in Performance-Evolved Reservoir Networks of Neuronal Population Dynamics
Fuente:
arXiv
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915862138585088 |
|---|---|
| author | Yadav, Manish |
| author_facet | Yadav, Manish |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13635 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Emergent E-I Structure in Performance-Evolved Reservoir Networks of Neuronal Population Dynamics Yadav, Manish Adaptation and Self-Organizing Systems 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. |
| title | Emergent E-I Structure in Performance-Evolved Reservoir Networks of Neuronal Population Dynamics |
| topic | Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2603.13635 |