性能进化共振网络中浮现的E-I结构
Emergent E-I Structure in Performance-Evolved Reservoir Networks of Neuronal Population Dynamics
AI总结：
研究通过性能驱动网络进化框架，揭示共振网络在预测神经动态中的结构特性，展示了其在恢复生理节律结构中的有效性。
摘要：
理解网络结构如何产生神经动态以及紧凑计算模型能否仅从数据中恢复该结构是计算神经科学的核心挑战。本文应用性能依赖网络进化（PDNE）框架来建模威尔逊-科万（WC）神经元系统，即一个经典的双群体模型，用于描述兴奋性-抑制性（E-I）相互作用。从最小种子网络开始，PDNE根据预测性能迭代地生长和修剪共振计算（RC）网络，产生紧凑且任务优化的共振网络。进化网络能够准确预测未见过的刺激幅度下的兴奋性E(t)和抑制性I(t)群体活动，并在零样本方式下泛化到新的刺激配置。进化网络的结构分析揭示了一种一致的功能组织，其中节点专门用于E、I以及共享的E-I表示。重要的是，进化共振网络的群体级连接自发恢复WC模型的正确兴奋性-抑制性符号模式，而无需设计强制。这些结果表明，性能驱动的网络进化不仅能产生准确的模型，还能产生结构可解释的生理节律模型，为神经系统的紧凑、数据高效的数字双开辟了新路径。
英文摘要：
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.
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