TY - JOUR

T1 - Synchronization of two non-identical Chialvo neurons

AU - Used, Javier

AU - Seoane, Jesús

AU - Bashkirtseva, Irina

AU - Ryashko, Lev

AU - Sanjuán, Miguel

N1 - Текст о финансировании #1 JU, JMS and MAFS acknowledge financial support from the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERDF, EU) under Project No. PID2019-105554GB-I00 (MCIN/AEI/10.13039/501100011033). IB and LR acknowledge financial support from the Ministry of Science and Higher Education of the Russian Federation under Project No \u201CUral Mathematical Center\u201D N 075-02-2024-1428. Текст о финансировании #2 JU, JMS and MAFS acknowledge financial support from the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERDF, EU) under Project No. PID2019-105554GB-I00 (MCIN/AEI/10.13039/501100011033).

PY - 2024

Y1 - 2024

N2 - We investigate the synchronization between two neurons using the stochastic version of the map-based Chialvo model. To simulate non-identical neurons, a mismatch is introduced in one of the main parameters of the model. Subsequently, the synchronization of the neurons is studied as a function of this mismatch, the noise introduced in the stochastic model, and the coupling strength between the neurons. We propose the simplest neural network for study, as its analysis is more straightforward and does not compromise generality. Within this network, two non-identical neurons are electrically coupled. In order to understand whether specific behaviors affect the global behavior of the system, we consider different cases related to the behavior of the neurons (chaotic or periodic). Furthermore, we study how variations in model parameters affect the firing frequency in each case. Additionally, we consider that the two neurons have both excitatory and inhibitory couplings. Consequently, we identify critical values of noise and mismatch for achieving satisfactory synchronization between the neurons in each case. Finally, we propose that the results have general applicability across various neuron models. © 2024.

AB - We investigate the synchronization between two neurons using the stochastic version of the map-based Chialvo model. To simulate non-identical neurons, a mismatch is introduced in one of the main parameters of the model. Subsequently, the synchronization of the neurons is studied as a function of this mismatch, the noise introduced in the stochastic model, and the coupling strength between the neurons. We propose the simplest neural network for study, as its analysis is more straightforward and does not compromise generality. Within this network, two non-identical neurons are electrically coupled. In order to understand whether specific behaviors affect the global behavior of the system, we consider different cases related to the behavior of the neurons (chaotic or periodic). Furthermore, we study how variations in model parameters affect the firing frequency in each case. Additionally, we consider that the two neurons have both excitatory and inhibitory couplings. Consequently, we identify critical values of noise and mismatch for achieving satisfactory synchronization between the neurons in each case. Finally, we propose that the results have general applicability across various neuron models. © 2024.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85191158942

U2 - 10.1016/j.chaos.2024.114888

DO - 10.1016/j.chaos.2024.114888

M3 - Article

VL - 183

JO - Chaos, Solitons and Fractals

JF - Chaos, Solitons and Fractals

SN - 0960-0779

M1 - 114888

ER -