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arXiv · 2004.00206

Degree assortativity in networks of spiking neurons

Christian Blasche·Shawn Means·Carlo R. Laing
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Abstract

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.

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Christian Blasche, Shawn Means, Carlo R. Laing. 2020-04-01. Degree assortativity in networks of spiking neurons. https://arxiv.org/abs/2004.00206

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