标题： 平衡神经网络的网络结构变化点检测与重构 显示英文标题

标题： Network structural change point detection and reconstruction for balanced neuronal networks

摘要： 理解大脑动力学和功能在很大程度上依赖于对神经元之间网络连接的知识。然而，大脑结构连接的复杂性，加上由突触可塑性驱动的持续修改，使得其直接的实验测量尤其具有挑战性。基于神经元记录的传统连接推断方法通常假设一个静态的潜在结构连接，并需要神经活动的稳定统计特征，这使得它们不适合重建经历变化的结构连接。为了满足重建可能经历结构变化的网络的需求，我们提出了一种统一的网络重建框架，该框架结合了由连接引起的改变点检测（CPD）与成对的时间延迟相关系数（TDCC）方法。对于处于平衡状态的一般神经元网络，我们开发了一个理论分析，用于基于神经元电压时间序列的波动来区分结构连接的变化。然后，我们展示了一种成对的TDCC方法，利用在检测到的改变点处分割的尖峰训练记录来重建网络。我们使用包含多种神经元模型的大规模网络模拟展示了我们的CPD-TDCC网络重建的有效性。至关重要的是，我们的方法能够适应网络拓扑和突触耦合强度发生变化的网络，即使在稀疏采样的子网络数据中也能保持准确性，这对于实际实验情况下的应用实现了关键性的进展。我们的CPD-TDCC框架通过考虑由连接引起的改变点，填补了网络重建中的关键空白，可能为研究皮层大脑的结构和动态提供有价值的工具。 显示英文摘要

摘要： Understanding brain dynamics and functions critically depends on knowledge of the network connectivity among neurons. However, the complexity of brain structural connectivity, coupled with continuous modifications driven by synaptic plasticity, makes its direct experimental measurement particularly challenging. Conventional connectivity inference methods based on neuronal recordings often assumes a static underlying structural connectivity and requires stable statistical features of neural activities, making them unsuitable for reconstructing structural connectivity that undergoes changes. To fulfill the needs of reconstructing networks undergoing potential structural changes, we propose a unified network reconstruction framework that combines connectivity-induced change point detection (CPD) with pairwise time-delayed correlation coefficient (TDCC) method. For general neuronal networks in balanced regimes, we develop a theoretical analysis for discriminating changes in structural connectivity based on the fluctuation of neuronal voltage time series. We then demonstrate a pairwise TDCC method to reconstruct the network using spike train recordings segmented at the detected change points. We show the effectiveness of our CPD-TDCC network reconstruction using large-scale network simulations with multiple neuronal models. Crucially, our method accommodates networks with changes in both network topologies and synaptic coupling strengths while retaining accuracy even with sparsely sampled subnetwork data, achieving a critical advancement for practical applications in real experimental situations. Our CPD-TDCC framework addresses the critical gap in network reconstruction by accounting connectivity-induced changes points, potentially offering a valuable tool for studying structure and dynamics in the cortical brain.