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首页> 外文期刊>Journal of Computational Neuroscience >A kinetic theory approach to capturing interneuronal correlation: the feed-forward case
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A kinetic theory approach to capturing interneuronal correlation: the feed-forward case

机译：捕获神经元间相关性的动力学理论方法：前馈情况

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摘要

We present an approach for using kinetic theory to capture first and second order statistics of neu-ronal activity. We coarse grain neuronal networks into populations of neurons and calculate the population average firing rate and output cross-correlation in response to time varying correlated input. We derive coupling equations for the populations based on first and second order statistics of the network connectivity. This coupling scheme is based on the hypothesis that second order statistics of the network connectivity are sufficient to determine second order statistics of neuronal activity. We implement a kinetic theory representation of a simple feed-forward network and demonstrate that the kinetic theory model captures key aspects of the emergence and propagation of correlations in the network, as long as the correlations do not become too strong. By analyzing the correlated activity of feedforward networks with a variety of connectivity patterns, we provide evidence supporting our hypothesis of the sufficiency of second order connectivity statistics.
机译：我们提出一种使用动力学理论来捕捉神经活动的一阶和二阶统计量的方法。我们将谷物神经网络粗化为神经元种群，并计算种群平均放电率和输出互相关以响应随时间变化的相关输入。我们基于网络连通性的一阶和二阶统计推导出总体的耦合方程。该耦合方案基于以下假设：网络连接的二阶统计足以确定神经元活动的二阶统计。我们实现了简单前馈网络的动力学理论表示，并证明了动力学理论模型可以捕获相关性在网络中出现和传播的关键方面，只要相关性不会变得太强即可。通过分析具有各种连通性模式的前馈网络的相关活动，我们提供了证据支持我们关于二阶连通性统计数据充分性的假设。

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