Max-Planck Institute for Biological Intelligence, Department Circuits-Computation-Models, Martinsried, Germany.
Fakultät für Biologie & Bernstein Center Freiburg, Albert-Ludwigs-Universität Freiburg, D-79104, Freiburg, Germany
J Neurosci. 2023 May 17;43(20):3599-3610. doi: 10.1523/JNEUROSCI.2208-22.2023.
With the advent of volumetric EM techniques, large connectomic datasets are being created, providing neuroscience researchers with knowledge about the full connectivity of neural circuits under study. This allows for numerical simulation of detailed, biophysical models of each neuron participating in the circuit. However, these models typically include a large number of parameters, and insight into which of these are essential for circuit function is not readily obtained. Here, we review two mathematical strategies for gaining insight into connectomics data: linear dynamical systems analysis and matrix reordering techniques. Such analytical treatment can allow us to make predictions about time constants of information processing and functional subunits in large networks. This viewpoint provides a concise overview on how to extract important insights from Connectomics data by mathematical methods. First, it explains how new dynamics and new time constants can evolve, simply through connectivity between neurons. These new time-constants can be far longer than the intrinsic membrane time-constants of the individual neurons. Second, it summarizes how structural motifs in the circuit can be discovered. Specifically, there are tools to decide whether or not a circuit is strictly feed-forward or whether feed-back connections exist. Only by reordering connectivity matrices can such motifs be made visible.
随着容积式 EM 技术的出现，大量的连接组学数据集正在被创建，为神经科学研究人员提供了关于所研究的神经回路全连接的知识。这允许对参与回路的每个神经元的详细、生物物理模型进行数值模拟。然而，这些模型通常包含大量的参数，并且对于哪些参数对于回路功能是必不可少的，很难直接获得洞察力。在这里，我们回顾了两种用于深入了解连接组学数据的数学策略：线性动力系统分析和矩阵重排技术。这种分析处理可以使我们能够对大网络中的信息处理时间常数和功能子单元做出预测。这种观点提供了一个简洁的概述，说明如何通过数学方法从连接组学数据中提取重要的见解。首先，它解释了新的动力学和新的时间常数如何仅仅通过神经元之间的连接而演变。这些新的时间常数可能比单个神经元的固有膜时间常数长得多。其次，它总结了如何发现电路中的结构基元。具体来说，有工具可以决定一个电路是否是严格的前馈，或者是否存在反馈连接。只有通过重新排列连接矩阵，才能使这些基元可见。
