Effects of Synaptic Pruning on Phase Synchronization in Chimera States of Neural Network

Zhengyuan Zhang,Liming Dai

doi:10.3390/app12041942

Zhengyuan Zhang, Liming Dai

Open Access

https://doi.org/10.3390/app12041942

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Abstract

This research explores the effect of synaptic pruning on a ring-shaped neural network of non-locally coupled FitzHugh–Nagumo (FHN) oscillators. The neurons in the pruned region synchronize with each other, and they repel the coherent domain of the chimera states. Furthermore, the width of the pruned region decides the precision and efficiency of the control effect on the position of coherent domains. This phenomenon gives a systematic comprehension of the relation between pruning and synchronization in neural networks from a new aspect that has never been addressed. An explanation of this mechanism is also given.

Highlights

The coexistence of synchronization and desynchronization in complex systems has been attracting attention for a long time in many fields, including neuroscience, physics, cybernetics, and mechanics
Researchers have already noticed that changes in the topology and coupling of the complex system can affect the spatial and temporal distribution of synchronized regions
The spatial distribution of coherent domains is sensitive to the initial condition. This means that there are some mechanisms in neural networks that can control the position of the coherent structures, and synaptic pruning may be one of them

Summary

Introduction

The coexistence of synchronization and desynchronization in complex systems has been attracting attention for a long time in many fields, including neuroscience, physics, cybernetics, and mechanics. Chimera states are phenomena characterized by the coexistence of coherent domains (periodic in-phase oscillations) and incoherent domains (characterized by a chaotic behavior in time and space) in the coupling system of identical oscillators [10,11] This phenomenon occurs in coupled FHN neural oscillators [12,13] and relates to many brain functions and diseases [14], including neural bump states [15], uni-hemispheric sleep [16], Parkinson’s disease [17], and Alzheimer’s disease [18]. Despite the research mentioned above, the mechanism of how changes in coupling can effectively control the spatial distribution of coherent and incoherent regions is still not clear. These theories still haven’t been widely applied in neuroscience.

Model Description

Effect of Pruning on Neural Network

Mechanism Explanation

Conclusions and Outlook