Spatiotemporal Dynamics in the Network of Memristive Morris–Lecar Neurons with Chemical Synapses

The disruption of the human brain’s resting states, which are critical for normal cognitive function and information processing, has been frequently linked to alterations in spatiotemporal coordination. Among the most prominent patterns in such excitable neural media are spiral waves, whose formation and dynamics have been extensively studied across biological and computational contexts. In this regard, many studies have focused on electrical synapses in the spatially connected neuronal networks. In this study, we investigate the dynamics of a lattice of memristive Morris–Lecar neurons under the influence of chemical synaptic coupling. Specifically, we report the distinct wave patterns that emerge when the key chemical synaptic parameters, such as firing threshold, sigmoidal slope and reversal potential, are varied. Additionally, we investigate the influence of electromagnetic induction, external current, noise effects and other factors that drive the emergence of spiral waves. We have computed the statistical synchronization factor for the key parameter of the chemical synapse. We observe diverse spatiotemporal patterns; notably, the single-core spirals are prominent in the excitable media. Furthermore, these spirals frequently transition into turbulent patterns or vanish altogether. These results advance our understanding of the role of chemical synapses in neuron populations. Our results may offer novel perspectives on spiral wave mechanisms and related neurobiological research.

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Publication Details

Journal
International Journal of Bifurcation and Chaos
Published
2026-09-30
DOI
https://doi.org/10.1142/s0218127427300035
Primary Topic
Nonlinear Dynamics and Pattern Formation
Type
article
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article

Spatiotemporal Dynamics in the Network of Memristive Morris–Lecar Neurons with Chemical Synapses

V. Yu. Popov, Anitha Karthikeyan, Karthikeyan Rajagopal, Sabarathinam Srinivasan et al.
International Journal of Bifurcation and Chaos
Nonlinear Dynamics and Pattern Formation
article

Spatiotemporal Dynamics in the Network of Memristive Morris–Lecar Neurons with Chemical Synapses

V. Yu. Popov, Anitha Karthikeyan, Karthikeyan Rajagopal, Sabarathinam Srinivasan, S. Vinoth
article en

Abstract

The disruption of the human brain’s resting states, which are critical for normal cognitive function and information processing, has been frequently linked to alterations in spatiotemporal coordination. Among the most prominent patterns in such excitable neural media are spiral waves, whose formation and dynamics have been extensively studied across biological and computational contexts. In this regard, many studies have focused on electrical synapses in the spatially connected neuronal networks. In this study, we investigate the dynamics of a lattice of memristive Morris–Lecar neurons under the influence of chemical synaptic coupling. Specifically, we report the distinct wave patterns that emerge when the key chemical synaptic parameters, such as firing threshold, sigmoidal slope and reversal potential, are varied. Additionally, we investigate the influence of electromagnetic induction, external current, noise effects and other factors that drive the emergence of spiral waves. We have computed the statistical synchronization factor for the key parameter of the chemical synapse. We observe diverse spatiotemporal patterns; notably, the single-core spirals are prominent in the excitable media. Furthermore, these spirals frequently transition into turbulent patterns or vanish altogether. These results advance our understanding of the role of chemical synapses in neuron populations. Our results may offer novel perspectives on spiral wave mechanisms and related neurobiological research.

International Journal of Bifurcation and Chaos
National Research University Higher School of Economics (RU), SRM Institute of Science and Technology (IN), SRM Dental College (IN), Trichy SRM Medical College Hospital and Research Centre (IN)
Openalex Percentile: Top 9%
Nonlinear Dynamics and Pattern Formation
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