Degree assortativity in networks of spiking neurons
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.
Keywords
Cite
@article{arxiv.2004.00206,
title = {Degree assortativity in networks of spiking neurons},
author = {Christian Blasche and Shawn Means and Carlo R. Laing},
journal= {arXiv preprint arXiv:2004.00206},
year = {2020}
}
Comments
To appear in Journal of Computational Dynamics