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Related Experiment Video

Updated: Jul 15, 2025

Homochronic Transplantation of Interneuron Precursors into Early Postnatal Mouse Brains

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Published on: June 8, 2018

Chimera state in a feed-forward neuronal network.

Peihua Feng¹, Jiayi Yang¹, Ying Wu¹

¹State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an, 710049 People's Republic of China.

Cognitive Neurodynamics

|October 4, 2023

Summary

This summary is machine-generated.

Partial synchronization, known as chimera states, can emerge in feed-forward neuronal networks, preserving neuron firing encoding abilities. This study confirms chimera states can exist and propagate in multi-layer networks.

Keywords:

Chimera state FHN neuron model Feed-forward effect

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Area of Science:

Computational Neuroscience
Complex Systems

Background:

Complete synchronization in neuronal networks can impair information encoding.
Feed-forward networks exhibit synchronization, posing a challenge for maintaining encoding capacity.

Purpose of the Study:

To investigate the emergence and propagation of partial synchronization (chimera states) in feed-forward multi-layer neuronal networks.
To determine if chimera states can serve as a compromise strategy to maintain encoding ability while allowing for synchronization.

Main Methods:

Designed a multi-layer neuronal network with ring topology in each layer.
Simulated intra- and inter-layer neuron firing propagation.
Analyzed the influence of initial conditions and feed-forward effect strength on pattern emergence.

Main Results:

Chimera states, characterized by coexisting disordered and synchronized neuron firing, were observed.
The emergence and propagation of chimera states depend critically on initial conditions and feed-forward strength.
While other patterns propagate, chimera states can be disrupted by strong feed-forward effects but can persist under specific conditions.

Conclusions:

Chimera states can emerge and propagate in feed-forward multi-layer networks under strict conditions.
This finding offers insights into neuronal firing propagation and encoding mechanisms.
Partial synchronization is a viable strategy for neuronal networks to balance synchronization and information processing.