Fast Sparsely Synchronized Brain Rhythms in A Scale-Free Neural Network
Sang-Yoon Kim, Woochang Lim

TL;DR
This study investigates how scale-free neural network structures influence the emergence and characteristics of sparsely synchronized brain rhythms, revealing the impact of network topology and individual neuron dynamics.
Contribution
It introduces a detailed analysis of sparse synchronization in scale-free neural networks, highlighting the effects of network architecture and individual neuron contributions.
Findings
Full synchronization occurs at low noise levels.
Partial and sparse synchronization depend on neuron degrees.
Network topology influences synchronization patterns.
Abstract
We consider a directed Barab\'{a}si-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees, and study emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast spiking Izhikevich interneurons. For a study on the fast sparsely synchronized rhythms, we fix (synaptic inhibition strength) at a sufficiently large value, and investigate the population states by increasing (noise intensity). For small , full synchronization with the same population-rhythm frequency and mean firing rate (MFR) of individual neurons occurs, while for sufficiently large partial synchronization with (: ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; particularly, the case of $f_p > 4 {\langle f_i…
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