论文标题
尖峰神经元网络中的度量分类性
Degree assortativity in networks of spiking neurons
论文作者
论文摘要
程度分类性是指相对于偶然的期望，基于其内度或外数的连接两个神经元的概率增加或降低。我们研究了这种分类性在theta神经元网络中的影响。 OTT/ANTONTEN ANSATZ用于得出每个神经元的预期状态的方程，然后这些方程在程度空间中是粗粒。我们生成具有有效连通性矩阵的家族，该家族通过分类系数参数，并使用这些系数的SVD分解来有效地对粗粒方程进行数值分叉分析。我们发现，在四种可能的程度分类性类型中，两种对网络的动态没有影响，而其他两个则可以产生重大影响。
Degree assortativity refers to the increased or decreased probability of connecting two neurons based on their in- or out-degrees, relative to what would be expected by chance. We investigate the effects of such assortativity in a network of theta neurons. The Ott/Antonsen ansatz is used to derive equations for the expected state of each neuron, and these equations are then coarse-grained in degree space. We generate families of effective connectivity matrices parametrised by assortativity coefficient and use SVD decompositions of these to efficiently perform numerical bifurcation analysis of the coarse-grained equations. We find that of the four possible types of degree assortativity, two have no effect on the networks' dynamics, while the other two can have a significant effect.
