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The effects of within-neuron degree correlations in networks of spiking neurons.
Biological Cybernetics ( IF 1.9 ) Pub Date : 2020-03-02 , DOI: 10.1007/s00422-020-00822-0
Carlo R Laing 1 , Christian Bläsche 1
Affiliation  

We consider the effects of correlations between the in- and out-degrees of individual neurons on the dynamics of a network of neurons. By using theta neurons, we can derive a set of coupled differential equations for the expected dynamics of neurons with the same in-degree. A Gaussian copula is used to introduce correlations between a neuron's in- and out-degree, and numerical bifurcation analysis is used determine the effects of these correlations on the network's dynamics. For excitatory coupling, we find that inducing positive correlations has a similar effect to increasing the coupling strength between neurons, while for inhibitory coupling it has the opposite effect. We also determine the propensity of various two- and three-neuron motifs to occur as correlations are varied and give a plausible explanation for the observed changes in dynamics.

中文翻译:

神经元内相关性在尖峰神经元网络中的作用。

我们考虑了各个神经元的进出度之间的相关性对神经元网络动力学的影响。通过使用theta神经元,我们可以导出一组耦合微分方程,用于具有相同in度的神经元的预期动力学。使用高斯copula引入神经元的进度和出度之间的相关性,并使用数值分叉分析确定这些相关性对网络动力学的影响。对于兴奋性偶联,我们发现诱导正相关与增加神经元之间的偶联强度具有相似的作用,而对于抑制性偶联则具有相反的作用。
更新日期:2020-04-23
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