Synchronous Patterns in an Ensemble of Two Excitatory Coupled Nonidentical Excitable $$\phi$$-Neurons

The purpose of this study is to investigate a simple model for the half-center oscillator (HCO), which consists of two nonidentical phase oscillators ( $$\phi$$ -neurons) with mutual chemical excitatory couplings. In the absence of couplings, each element is in an excitable state. Using one- and two-parameter bifurcation analysis, we determine regions of stability of various synchronous temporal patterns typical for HCO and describe in detail bifurcation transitions between them. The proposed simple HCO model allows one to reproduce the main effects observed in biologically plausible HCO models, and can be easily implemented in locomotor units in neuromorphic robotics.

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Publication Details

Journal
Regular and Chaotic Dynamics
Published
2025-12-22
DOI
https://doi.org/10.1134/s1560354725550015
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
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article

Synchronous Patterns in an Ensemble of Two Excitatory Coupled Nonidentical Excitable $$\phi$$-Neurons

Grigory V. Osipov, Tatiana A. Levanova, Alexander G. Korotkov
Regular and Chaotic Dynamics
Neural dynamics and brain function
article

Synchronous Patterns in an Ensemble of Two Excitatory Coupled Nonidentical Excitable $$\phi$$-Neurons

Grigory V. Osipov, Tatiana A. Levanova, Alexander G. Korotkov
article en

Abstract

The purpose of this study is to investigate a simple model for the half-center oscillator (HCO), which consists of two nonidentical phase oscillators ( $$\phi$$ -neurons) with mutual chemical excitatory couplings. In the absence of couplings, each element is in an excitable state. Using one- and two-parameter bifurcation analysis, we determine regions of stability of various synchronous temporal patterns typical for HCO and describe in detail bifurcation transitions between them. The proposed simple HCO model allows one to reproduce the main effects observed in biologically plausible HCO models, and can be easily implemented in locomotor units in neuromorphic robotics.

Regular and Chaotic Dynamics
N. I. Lobachevsky State University of Nizhny Novgorod (RU)
Openalex Percentile: Top 34%
Neural dynamics and brain function
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