https://doi.org/10.1140/epjs/s11734-025-01533-y
Regular Article
Lag synchronization in an unidirectional ring of memristive neurons
1
Center for Research, SRM Easwari Engineering College, Chennai, India
2
Center for Cognitive Science, Trichy SRM Medical College Hospital and Research Center, Trichy, India
3
Ministry of Higher Education and Scientific Research, 10024, Baghdad, Iraq
4
Department of Computer Technology Engineering, College of Information Technology, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq
5
Nonlinear Dynamics Research Center (NDRC), Ajman University, 20550, Ajman, United Arab Emirates
6
Department of Mathematics, Faculty of Science, University of Jordan, 11942, Amman, Jordan
7
Faculty of Electronics Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam
8
Faculty of Natural Sciences and Mathematics, University of Maribor, 2000, Maribor, Slovenia
9
Community Healthcare Center Dr. Adolf Drolc Maribor, 2000, Maribor, Slovenia
10
University College, Korea University, 02841, Seoul, Republic of Korea
11
Department of Physics, Kyung Hee University, 02447, Seoul, Republic of Korea
Received:
22
January
2025
Accepted:
16
February
2025
Published online:
25
February
2025
In recent years, the study of neuronal models has provided significant insights into brain dynamics and neurological disorders. Map-based neuronal models, such as the Rulkov map, have gained considerable popularity due to their computational efficiency and ability to replicate complex neuronal dynamics. We thus here study the collective dynamics of an unidirectional ring network composed of three memristive Rulkov maps, with particular emphasis on synchronization patterns and their dependence on coupling types. By employing electrical and memristive/field couplings, we analyze the emergence of complete synchronization, lag synchronization, and phase synchronization under varying coupling strengths. Our findings highlight how diffusive-based synaptic pathways modulate synchronization and collective behavior in the network. The presented results also offer new perspectives on the role of coupling functions in shaping neuronal synchronization, and they reveal their deeper implications for understanding pathological brain states and for designing neuromorphic systems.
© The Author(s) 2025
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