基于Rulkov神经元模型的分数记忆器中的动态效应分析
Mahdieh Ghasemi1, Zeinab Malek Raeissi1, Ali Foroutannia2
1Neural Engineering Laboratory, Department of Biomedical Engineering, University of Neyshabur, Neyshabur 9319774446, Iran.
Biomimetics (Basel, Switzerland)
|September 27, 2024
概括
研究人员开发了一种分数记忆器鲁尔科夫神经元模型,增强了神经功能分析. 这种新模型改善了遗传性质,多时间尺度活动和发射频率响应,在神经网络中提供了更好的同步.
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.
- 分数微积分的计算.
背景情况:
- 复杂的神经系统建模依赖于数学神经元模型,如菲茨休-纳古莫和霍奇金-哈克斯利.
- 这些模型的复杂性阻碍了详细的神经功能分析.
- 离散的鲁尔科夫模型为研究神经元动态提供了一个更简单的方法.
研究的目的:
- 介绍了一个新的分数记忆器鲁尔科夫神经元模型.
- 通过结合memristors和分数衍生品来研究动态效应和改进.
- 增强神经元建模,以获得更准确的生物物理效应估计.
主要方法:
- 结合了鲁尔科夫神经元模型和一个memristor.
- 使用分叉图和0-1混乱测试评估系统参数.
- 将离散分数顺序方法应用于鲁尔科夫记忆器图,并分析合系统.
主要成果:
- 分数记忆器鲁尔科夫模型表现出强度,周期和混乱的发射行为.
- 分数顺序显著影响系统动态,并增强同步.
- 与全顺序模型相比,改善了遗传性质的生成和多时间尺度活动.
结论:
- 分数记忆器鲁尔科夫神经元模型提供了更高的准确性和性能.
- 分数计算和memristors有效地改进了离散的神经元模型.
- 这种综合方法对于模拟神经网络中的生物物理效应非常有价值.
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