Effects of Macrostructure on Synchrony in SONET Model Neuron Networks

Abstract

The SONET model randomly generates neural networks with microstructure using four second order statistics. In order to make the SONET model more realistic, another parameter was added to the model that would control the macrostructure. The new parameter affects the probability that two neurons are connected based on the distance between the neurons; the closer two neurons are to each other, the more likely they are to be connected. To test the new parameter, L, against the existing parameters, alpha-chain, alpha-converge, alpha-diverge, and alpha-reciprocal, 400 neuron networks were generated with random values of each variable, and a 2000 millisecond simulation was run on each network using the Brian2 neuron simulating software. The synchrony of each network was then measured monotonically. Before the new parameter was added, it was known that the rate of the chain motif, alpha-chain, had the greatest effect on the synchrony of a network. The testing with macrostructure showed alpha-chain was still the most important factor for predicting synchrony, though L, the intensity of the macrostructure, did somewhat affect the synchrony. When the macrostructure of a network was more prominent, had a smaller L value, the network tended to be more synchronous.