Signal propagation in small networks of Hodgkin-Huxley neurons

The study of neuron models and their networks is a riveting topic for many researchers worldwide because it allows to glimpse the fundamental processes using accessible methodology. The paper considers dynamics of small networks of Hodkin-Huxley neurons, namely a chain of three neurons and a small-world-like network of seven neurons. The ensembles of neurons are represented by systems of ordinary differential equations, so the research has been conducted numerically. It has been found that complex quasi-periodic and chaotic regimes may arise in the systems, and the existense of such regimes is caused by the inner parameters of the systems, such as individual currents of the neurons and the coupling between them. This research contributes to the fundamental understanding of signal propagation in networks of neuron models and may provide insight into the physiology of real neuronal systems.

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Publication Details

Journal
Frontiers in Network Physiology
Published
2025-12-02
DOI
https://doi.org/10.3389/fnetp.2025.1729999
Primary Topic
stochastic dynamics and bifurcation
Type
article
Field-Weighted Citation Impact
0.00
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article

Signal propagation in small networks of Hodgkin-Huxley neurons

Galina I. Strelkova, Tatiana R. Bogatenko, Konstantin Sergeev
Frontiers in Network Physiology
stochastic dynamics and bifurcation
article

Signal propagation in small networks of Hodgkin-Huxley neurons

Galina I. Strelkova, Tatiana R. Bogatenko, Konstantin Sergeev
article en

Abstract

The study of neuron models and their networks is a riveting topic for many researchers worldwide because it allows to glimpse the fundamental processes using accessible methodology. The paper considers dynamics of small networks of Hodkin-Huxley neurons, namely a chain of three neurons and a small-world-like network of seven neurons. The ensembles of neurons are represented by systems of ordinary differential equations, so the research has been conducted numerically. It has been found that complex quasi-periodic and chaotic regimes may arise in the systems, and the existense of such regimes is caused by the inner parameters of the systems, such as individual currents of the neurons and the coupling between them. This research contributes to the fundamental understanding of signal propagation in networks of neuron models and may provide insight into the physiology of real neuronal systems.

Frontiers in Network PhysiologyVol. 5
Saratov State University (RU)
Openalex Percentile: Top 70%
stochastic dynamics and bifurcation
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