Strength of Correlations in Strongly Recurrent Neuronal Networks
Strength of Correlations in Strongly Recurrent Neuronal Networks
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DOI：
10.1103/physrevx.8.031072
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发表时间：
2018-09-17
影响因子：
12.5
通讯作者：
Hansel, David
中科院分区：
文献类型：
--
作者：
Darshan, Ran;van Vreeswijk, Carl;Hansel, David
大脑活动中的时空相关性在功能上是重要的，并且与感知、学习和可塑性、探索行为以及认知的各个方面有关。大脑皮质中的神经元相互作用很强。它们的活动在时间上是不规则的，并且可以表现出实质性的相关性。然而，高度循环和强烈相互作用的神经元的集体动力学如何演变成一种状态，在这种状态下，单个细胞的活动是高度不规则的，但宏观上是相关的，这是一个悬而未决的问题。在这里，我们开发了一个一般性的理论，将成对相关性的强度与强耦合神经元网络的解剖特征联系起来。为此，我们研究网络的二进制单位。当相互作用很强时，活动在参数空间的大区域中是不规则的。我们发现，尽管有很强的相互作用，相关性通常很弱。尽管如此，我们确定的建筑特征，如果存在，产生强烈的相关性，而不破坏活动的不规则性。对于具有这些特征的网络，我们确定相关性如何随网络大小和连接数量而变化。我们的工作显示了强相关性与高度不规则活动一致的机制，这是中枢神经系统神经元动力学的两个标志。
Spatiotemporal correlations in brain activity are functionally important and have been implicated in perception, learning and plasticity, exploratory behavior, and various aspects of cognition. Neurons in the cerebral cortex are strongly interacting. Their activity is temporally irregular and can exhibit substantial correlations. However, how the collective dynamics of highly recurrent and strongly interacting neurons can evolve into a state in which the activity of individual cells is highly irregular yet macroscopically correlated is an open question. Here, we develop a general theory that relates the strength of pairwise correlations to the anatomical features of networks of strongly coupled neurons. To this end, we investigate networks of binary units. When interactions are strong, the activity is irregular in a large region of parameter space. We find that despite the strong interactions, the correlations are generally very weak. Nevertheless, we identify architectural features, which if present, give rise to strong correlations without destroying the irregularity of the activity. For networks with such features, we determine how correlations scale with the network size and the number of connections. Our work shows the mechanism by which strong correlations can be consistent with highly irregular activity, two hallmarks of neuronal dynamics in the central nervous system.