AI 中文总结
本研究基于储备池计算（RC）框架从多通道电生理记录提取神经元培养物的内在连接图谱（ICM），通过图论分析验证了RC连接性推断的有效性，建立了神经元培养物功能网络表征的可扩展数据驱动框架。
AI 中文摘要
图论分析为量化神经元培养物中的涌现动力学提供了原则性框架。本文提出了一种分析流程，用于从多通道电生理记录中推断体外皮层培养物的网络级属性。该方法基于近期提出的储备池计算（Reservoir Computing, RC）框架（Auslender等人，2025），该框架可从神经活动中直接提取内在连接图谱（Intrinsic Connectivity Map, ICM）。我们将ICM解释为有效邻接矩阵，并应用图论中心性度量来量化节点和边级对培养物集体动力学的贡献。我们系统评估了局部和全局图度量，并考察了它们与实验测量的活动特征（包括放电率和网络级描述符）的关系。为验证推断流程，我们还模拟了实验环境，实现了对RC衍生连接性与已知真实邻接矩阵的受控基准测试，并评估了模型性能与图结构的函数关系。结果表明，图论度量与实验观察到的活动模式之间存在强度各异的统计稳健关联。这些发现进一步支持了基于RC的连接性推断的有效性，并为神经元培养物系统的功能网络表征建立了可扩展的数据驱动框架。
英文摘要
Graph-theoretical analysis offers a principled framework for quantifying emergent dynamics in neuronal cultures. Here, we present an analytical pipeline for inferring network-level properties of in vitro cortical cultures from multichannel electrophysiological recordings. The approach builds on a recently proposed Reservoir Computing (RC) framework (Auslender et al., 2025), which enables direct extraction of an Intrinsic Connectivity Map (ICM) from neural activity. We interpret the ICM as an effective adjacency matrix and apply graph-theoretic centrality measures to quantify node- and edge-level contributions to the culture's collective dynamics. We systematically evaluate both local and global graph metrics and examine their relationships with experimentally measured activity features, including firing rates and network-level descriptors. To validate the inference procedure, we also simulate the experimental environment, enabling controlled benchmarking of the RC-derived connectivity against a known ground-truth adjacency matrix and assessment of model performance as a function of graph structure. Our results demonstrate statistically robust associations, of varying strength, between graph-theoretic measures and experimentally observed activity patterns. These findings additionally support the validity of the RC-based connectivity inference and establish a scalable, data-driven framework for functional network characterization in neuronal culture systems.
