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基于忆阻的双神经元网络中的深度脑刺激和滞后同步。

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

机构信息

School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213159, PR China.

School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213159, PR China.

出版信息

Neural Netw. 2024 Dec;180:106728. doi: 10.1016/j.neunet.2024.106728. Epub 2024 Sep 10.

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.

摘要

在探索治疗神经紊乱和减轻患者痛苦的潜在方法过程中,深部脑刺激(DBS)已被用于干预或研究病理性神经活动。为了探究 DBS 的确切作用机制,本工作中提出了一个考虑 DBS 的忆阻双神经元网络。该网络通过耦合二维 Morris-Lecar 神经元模型和使用忆阻突触来模拟突触可塑性来实现。通过数值动力学分析揭示了复杂的爆发活动和动力学效应。通过检查同步行为,揭示了忆阻突触的去同步机制。研究表明,突触连接导致完全同步的放电活动中出现时间滞后或异步。此外,忆阻双神经元网络基于 FPGA 进行了硬件实现,实验结果证实了丰富的神经元电活动和混沌动力学行为。这项工作为 DBS 干预神经网络的潜在机制提供了深入的见解。

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