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首页> 外文期刊>Journal of Computational Neuroscience >Information-geometric measure of 3-neuron firing patterns characterizes scale-dependence in cortical networks
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Information-geometric measure of 3-neuron firing patterns characterizes scale-dependence in cortical networks

机译：3-神经元放电模式的信息几何测量表征皮层网络中的尺度依赖性

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摘要

To understand the functional connectivity of neural networks, it is important to develop simple and incisive descriptors of multineuronal firing patterns. Analysis at the pairwise level has proven to be a powerful approach in the retina, but it may not suffice to understand complex cortical networks. Here we address the problem of describing interactions among triplets of neurons. We consider two approaches: an information-geometric measure (Amari 2001), which we call the "strain," and the Kullback-Leibler divergence. While both approaches can be used to assess whether firing patterns differ from those predicted by a pairwise maximum-entropy model, the strain provides additional information. Specifically, when the observed firing patterns differ from those predicted by a pairwise model, the strain indicates the nature of this difference-whether there is an excess or a deficit of synchrony-while the Kullback-Leibler divergence only indicates the magnitude of the difference. We show that the strain has technical advantages, including ease of calculation of confidence bounds and bias, and robustness to the kinds of spike-sorting errors associated with tetrode recordings. We demonstrate the biological importance of these points via an analysis of multineuronal firing patterns in primary visual cortex. There is a striking scale-dependent behavior of triplet firing patterns: deviations from the pairwise model are substantial when the neurons are within 300 microns of each other, and negligible when they are at a distance of >600 microns. The strain identifies a consistent pattern to these interactions: when triplet interactions are present, the strain is nearly always negative, indicating that there is less synchrony than would be expected from the pairwise interactions alone.
机译：要了解神经网络的功能连通性，重要的是要开发出简单而敏锐的多神经元放电模式描述符。事实证明，成对分析在视网膜中是一种有效的方法，但可能不足以了解复杂的皮质网络。在这里，我们解决了描述神经元三胞胎之间相互作用的问题。我们考虑两种方法：信息几何测度（Amari 2001），我们称其为“应变”，以及Kullback-Leibler散度。虽然两种方法都可用于评估点火模式是否与成对最大熵模型预测的点火模式不同，但应变可提供其他信息。具体来说，当观察到的发射模式与成对模型预测的发射模式不同时，应变表明了这种差异的性质，即是否存在同步的过度或不足，而Kullback-Leibler散度仅表明了差异的大小。我们表明该菌株具有技术优势，包括易于计算置信区间和偏差，以及对与四极体记录相关的尖峰排序错误的鲁棒性。我们通过分析初级视觉皮层中的多神经元放电模式证明了这些观点的生物学重要性。三重激发模式具有明显的尺度依赖性行为：当神经元彼此之间的距离在300微米之内时，与成对模型的偏差就很大，而当它们之间的距离> 600微米时，则可以忽略不计。应变确定了这些相互作用的一致模式：当存在三重相互作用时，应变几乎总是为负，表明同步性比单独的成对相互作用要少。

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