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在记忆电磁感应下,一个无乘数的鲁尔科夫神经元:动力学分析,能量计算和电路实现
Shaohua Zhang1, Cong Wang1, Hongli Zhang1
1School of Electrical Engineering, Xinjiang University, Urumqi, Xinjiang 830017, China.
Chaos (Woodbury, N.Y.)
|December 7, 2023
概括
这项研究引入了一种新的,无乘数的Rulkov (mRulkov) 记忆神经元模型. 它揭示了增强的极端多稳定性,并分析了能量转换,推进了神经形态电路设计.
科学领域:
- 计算神经科学是一种神经科学.
- 神经形态工程的神经形态工程
- 非线性动力学是一种非线性动力学.
背景情况:
- 现实的生物神经元模型对于理解神经发射和神经形态电路至关重要.
- 记忆器对于模拟神经元模型中的突触和电磁感应至关重要.
- 现有的模型往往忽略了初始增强的极端多稳定性和能量动态.
研究的目的:
- 提出与周期记忆器集成的鲁尔科夫神经元模型的无乘数实现.
- 为了实现非自主记忆力鲁尔科夫 (mRulkov) 神经元的生物模拟建模.
- 在拟议的模型中研究增强的极端多稳定性及其能量分析.
主要方法:
- 开发了鲁尔科夫神经元模型的无乘数实现.
- 使用周期记忆器来模拟电磁感应.
- 进行理论分析,数值模拟 (包括PSpice) 和能量分布计算.
主要成果:
- 已证明参数和memristor初始条件对稳定性的影响.
- 观察到局部尖刺,局部隐藏尖刺,以及定期爆发的射击行为.
- 发现了新的极端多稳定性,并分析了相关的能量转换.
结论:
- 拟议的mRulkov神经元模型有效模拟生物仿真发射行为.
- 通过操纵memristor的初始条件来实现增强的极端多稳定性.
- 无乘数实现和能量分析为神经形态电路提供了显著的进步.
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