Inferring neuronal network functional connectivity with directed information
Inferring neuronal network functional connectivity with directed information
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DOI：
10.1152/jn.00086.2017
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发表时间：
2017-08-01
影响因子：
2.5
通讯作者：
Aazhang, Behnaam
中科院分区：
文献类型：
--
作者：
Cai, Zhiting;Neveu, Curtis L.;Aazhang, Behnaam
神经科学中的一个主要挑战是开发有效的工具，从大规模的神经元活动模式记录中推断电路的连通性。在这项研究中，上下文树最大化（CTM）被用来估计有向信息（DI），它测量的因果关系之间的神经尖峰列车，以推断假定的突触连接。与现有方法相比，本文提出的方法是数据驱动的，可以很容易地识别神经元之间的线性和非线性关系。该CTM-DI方法可靠地识别了基于真实电导网络的仿真所依据的电路结构。它还推断电路性能从电压敏感染料记录的颊神经节的Aaplasia。该方法也适用于其他大规模的记录。它提供了一个系统的工具来映射网络连接和跟踪网络结构的变化，如突触强度以及单个神经元的连接程度，这反过来又可以提供深入了解如何通过学习产生的修改在神经网络中分布。新&值得注意的是这项研究汇集了电压-敏感染料记录和信息理论来推断失智症摄食中枢模式生成网络的功能连接体。与目前的统计方法相比，本研究中开发的推理方法是由基于电导的模型电路驱动和验证的数据，可以区分兴奋性和抑制性连接，对突触可塑性具有鲁棒性，并且能够检测介导运动模式的网络结构。
A major challenge in neuroscience is to develop effective tools that infer the circuit connectivity from large-scale recordings of neuronal activity patterns. In this study, context tree maximizing (CTM) was used to estimate directed information (DI), which measures causal influences among neural spike trains in order to infer putative synaptic connections. In contrast to existing methods, the method presented here is data driven and can readily identify both linear and nonlinear relations between neurons. This CTM-DI method reliably identified circuit structures underlying simulations of realistic conductance-based networks. It also inferred circuit properties from voltage-sensitive dye recordings of the buccal ganglion of Aplysia. This method can be applied to other large-scale recordings as well. It offers a systematic tool to map network connectivity and to track changes in network structure such as synaptic strengths as well as the degrees of connectivity of individual neurons, which in turn could provide insights into how modifications produced by learning are distributed in a neural network.NEW & NOTEWORTHY This study brings together the techniques of voltage-sensitive dye recording and information theory to infer the functional connectome of the feeding central pattern generating network of Aplysia. In contrast to current statistical approaches, the inference method developed in this study is data driven and validated by conductance-based model circuits, can distinguish excitatory and inhibitory connections, is robust against synaptic plasticity, and is capable of detecting network structures that mediate motor patterns.