https://maneesh51.github.io/tags/neuro-ai/Neuro-AIHugo Blox Builder (https://hugoblox.com)en-usFri, 13 Mar 2026 00:00:00 +0000https://maneesh51.github.io/media/sharing.pnghttps://maneesh51.github.io/tags/neuro-ai/https://maneesh51.github.io/publications/emergent-ei-structure-2026/Fri, 13 Mar 2026 00:00:00 +0000https://maneesh51.github.io/publications/emergent-ei-structure-2026/<p>This preprint studies how <strong>performance-dependent network evolution (PDNE)</strong> can build compact and interpretable reservoir models for neuronal population dynamics.</p> <p>Using the Wilson-Cowan excitatory-inhibitory system as the target, the evolved reservoirs:</p> <ul> <li>predict both E(t) and I(t) accurately on unseen stimulus amplitudes,</li> <li>generalize zero-shot to new pulse configurations without retraining, and</li> <li>recover population-level E-I sign structure for most interaction types as an emergent property.</li> </ul> <p>The results support using performance-evolved reservoirs as data-efficient and interpretable computational surrogates for neuronal digital twins.</p> <p><strong>Read full preprint here:</strong> M. Yadav, </p>