在经过多频交替电流注入的改进的记忆性后-神经元模型中,共存的发射模式
Mengjiao Wang1, Jie Ding1, Bingqing Deng1
1School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China.
Micromachines
|December 23, 2023
概括
这项研究引入了一种新的Hindmarsh-Rose神经元模型,其中包含了流量控制的memristor和多频率刺激. 该模型展示了多样化的发射模式和独特的稳定性行为,通过模拟电路进行验证.
科学领域:
- 计算神经科学是一种神经科学.
- 非线性动力学是一种非线性动力学.
- 记忆器应用程序 应用程序
背景情况:
- 记忆器理论正在进步,显示神经系统应用的前景.
- 流量控制的memristors可以模拟神经元磁感应效应.
研究的目的:
- 提出一个新的Hindmarsh-Rose (HR) 神经元模型,使用流量控制的memristor和多频激发.
- 在拟议的模型中调查各种燃烧模式和稳定现象.
主要方法:
- 将流量控制的memristor集成到HR神经元模型中.
- 应用叠加的高频和低频电流刺激.
- 在单个和多个刺激下分析发射模式.
- 在不同的memristor合强度下研究模型稳定性.
- 使用模拟电路进行实验验证.
主要成果:
- 该模型在不同的刺激下展现出不同的共存的不同发射模式.
- 通过改变memristor合强度,观察到一种罕见的消除多重稳定性的现象.
- 数字模拟通过模拟电路实验成功验证.
结论:
- 提出的基于memristor的HR神经元模型有效地复制复杂的神经元动态.
- 这项研究强调了流量控制的memristors在计算神经科学中的潜力.
- 这些发现提供了关于神经元发射模式和稳定性控制的见解.
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