Evidence map›Paper›PMID 39678726›Full record

ArticleCognitive neurodynamics2024

Unraveling the dynamics of a flux coupled Chialvo neurons and the existence of extreme events.

Sathiyadevi Kanagaraj, Premraj Durairaj, Anitha Karthikeyan, Karthikeyan Rajagopal

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Article in Cognitive neurodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Sathiyadevi KanagarajCentre for Nonlinear Systems, Chennai Institute of Technology, Chennai, Tamilnadu 600 069 India.
Premraj DurairajCentre for Nonlinear Systems, Chennai Institute of Technology, Chennai, Tamilnadu 600 069 India.
Anitha KarthikeyanDepartment of Electronics and Communication Engineering, Vemu Institute of Technology, Chitoor, Andhra Pradesh 517112 India.
Karthikeyan RajagopalCentre for Nonlinear Systems, Chennai Institute of Technology, Chennai, Tamilnadu 600 069 India.ORCID 0000-0003-2993-7182

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To illustrate the occurrences of extreme events in the neural system we consider a pair of Chialvo neuron maps. Importantly, we explore the dynamics of the proposed system by including a flux term between the neurons. Primarily, the dynamical behaviors of the coupled Chialvo neurons are examined using the Lyapunov spectrum and bifurcation analysis. We find the transitions between the periodic and chaotic dynamics in relation to the injected ion current of the first and second neurons and the flux coupling strength. It is interesting to note that, the extreme events can occur in the chaotic zone for some parameters. The analysis is then extended to a network of Chialvo neurons with various network connectivities. We discover that coexisting coherent and incoherent behaviour can arise and that nodes in the network can exhibit extreme event features. The findings of this study could help to better understand the rare large-amplitude events that occur in neural networks, which can help detect and prevent a variety of neurological disorders.

Indexed as

ChaosExtreme eventsLyapunov exponents

Identifiers

PMID39678726
PMCPMC11639440

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.