深部脑刺激与记忆性双神经元网络的滞后同步

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

摘要

为了寻找治疗神经系统疾病的潜在方法并减轻病人的痛苦,人们利用脑深部刺激(DBS)来干预或研究病理神经活动。为了探索 DBS 的确切作用机制,本研究提出了一种考虑到 DBS 的记忆性双神经元网络。该网络是通过耦合二维莫里斯-勒卡神经元模型并使用忆阻器突触来模拟突触可塑性而实现的。通过动态分析,复杂的突发性活动和动态效应在数值上得到了揭示。通过研究同步行为,揭示了忆阻器突触的去同步机制。研究表明,突触连接会导致完全同步的发射活动出现时滞或不同步。此外,基于 FPGA 的硬件实现了忆阻器双神经元网络,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启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep brain stimulation and lag synchronization in a memristive two-neuron network

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.

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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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