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Deep brain stimulation and lag synchronization in a memristive two-neuron network.

Authors :
Yu X
Bao H
Xu Q
Chen M
Bao B
Source :
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2024 Dec; Vol. 180, pp. 106728. Date of Electronic Publication: 2024 Sep 10.
Publication Year :
2024

Abstract

In the pursuit of potential treatments for neurological disorders and the alleviation of patient suffering, deep brain stimulation (DBS) has been utilized to intervene or investigate pathological neural activities. To explore the exact mechanism of how DBS works, a memristive two-neuron network considering DBS is newly proposed in this work. This network is implemented by coupling two-dimensional Morris-Lecar neuron models and using a memristor synaptic synapse to mimic synaptic plasticity. The complex bursting activities and dynamical effects are revealed numerically through dynamical analysis. By examining the synchronous behavior, the desynchronization mechanism of the memristor synapse is uncovered. The study demonstrates that synaptic connections lead to the appearance of time-lagged or asynchrony in completely synchronized firing activities. Additionally, the memristive two-neuron network is implemented in hardware based on FPGA, and experimental results confirm the abundant neuronal electrical activities and chaotic dynamical behaviors. This work offers insights into the potential mechanisms of DBS intervention in neural networks.<br />Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Details

Language :
English
ISSN :
1879-2782
Volume :
180
Database :
MEDLINE
Journal :
Neural networks : the official journal of the International Neural Network Society
Publication Type :
Academic Journal
Accession number :
39299036
Full Text :
https://doi.org/10.1016/j.neunet.2024.106728