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Related Experiment Video

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Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

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Published on: June 7, 2024

Dynamics of a multiplex neural network with delayed couplings.

Xiaochen Mao¹, Xingyong Li¹, Weijie Ding¹

¹Department of Engineering Mechanics, College of Mechanics and Materials, Hohai University, Nanjing, 211100 China.

Applied Mathematics and Mechanics

|February 8, 2021

Summary

Delayed interactions in multiplex neural networks significantly influence network dynamics. This study explores how these delays impact stability, synchronization, and the emergence of complex behaviors like multi-stability.

Keywords:

coexisting attractor neural network synchronization time delay

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Area of Science:

Neuroscience
Complex Systems
Network Science

Background:

Multiplex networks, featuring interconnected layers, are prevalent in systems like the brain and social structures.
Understanding the nonlinear dynamics within these networks is crucial for explaining complex system behaviors.

Purpose of the Study:

To investigate the nonlinear dynamics of a multiplex network composed of three neural groups with delayed interactions.
To analyze the stability, bifurcation, and emergent neural activities of this network model.

Main Methods:

Construction of a circuit platform simulating the neural network using neuron, transfer function, and time delay circuits.
Analysis of network equilibrium stability and bifurcation points.
Exploration of neural activities and synchronization patterns under varying delayed coupling conditions.

Main Results:

Delayed couplings were found to critically affect network stability, both enhancing and suppressing it.
Specific synchronization patterns between network layers were observed.
The network exhibited the generation of complex attractors and coexisting multiple stable states (multi-stability).

Conclusions:

Time-delayed interactions are pivotal in shaping the dynamics of multiplex neural networks.
These delays can lead to a rich repertoire of behaviors, including complex synchronization and multi-stability.
The findings provide insights into the functional mechanisms of neural systems and other complex networks.