菲茨休-纳古莫神经元的输入信号积累能力
A V Bukh1, I A Shepelev1,2, T E Vadivasova1
1Institute of Physics, Saratov State University, 83 Astrakhanskaya Street, Saratov 410012, Russia.
Chaos (Woodbury, N.Y.)
|December 2, 2024
概括
我们发现了一个新的参数区域,使FitzHugh-Nagumo神经元能够通过延迟的,低于值信号来激发神经元. 这一发现为使用这些神经元进行依赖尖峰时间的可塑性训练开辟了新的可能性.
科学领域:
- 计算神经科学是一种神经科学.
- 计算神经科学和神经启发的人工智能
背景情况:
- 菲茨休-纳古莫神经元模型是神经元激发的简化数学模型.
- 与漏洞集成和发射模型不同,菲茨休-纳古莫神经元通常难以从外部信号中积累能量,限制它们在某些神经网络应用中的使用.
- 研究结合的神经元的动态对于理解神经网络中的信息处理至关重要.
研究的目的:
- 为了研究一个后突触FitzHugh-Nagumo神经元在单向电气合下与两个异步前突触神经元的激发动态.
- 为了确定后突触神经元可以通过具有显著时间延迟的下值突触神经元被激发的条件.
- 探索FitzHugh-Nagumo神经元在尖峰时间依赖可塑性 (STDP) 训练中的潜在应用.
主要方法:
- 结合的FitzHugh-Nagumo神经元模型的数值模拟.
- 对不同幅度和时间延迟的异步前突触输入的后突触神经元反应的分析.
- 在特定条件下识别支持神经元激发的参数区域.
主要成果:
- 证明FitzHugh-Nagumo神经元可以通过异步激发,即使有显著的时间延迟,也可以激发下值的前突触,这是以前没有观察到的现象.
- 确定了这种激发发生的特定参数区域,详细说明了时间延迟和合系数的边界.
- 描述了使激发成为可能的潜在机制,尽管神经元的能量积累特性有限.
结论:
- 一个新的参数模式允许菲茨休-纳古莫神经元从延迟的,低于值的输入中表现出激发.
- 这一发现克服了先前的局限性,表明了FitzHugh-Nagumo神经元在基于STDP的神经网络训练中使用的潜力.
- 结果扩大了对神经元动力学和简化神经元模型中的计算能力的理解.
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