论文标题
峰值神经元网络中神经内度相关性的影响
The effects of within-neuron degree correlations in networks of spiking neurons
论文作者
论文摘要
我们考虑单个神经元内和外部之间的相关性对神经元网络的动力学的影响。通过使用theta神经元，我们可以为具有相同内的神经元的预期动力学得出一组耦合的微分方程。使用高斯copula来引入神经元的内和级别和数值分叉分析之间的相关性，确定了这些相关性对网络动力学的影响。对于兴奋性耦合，我们发现诱导正相关与增加神经元之间的耦合强度具有相似的作用，而对于抑制性耦合，它具有相反的效果。我们还确定了各种两种和三神经元基序的倾向，因为相关性变化，并为观察到的动力学变化提供了合理的解释。
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.
