基于自我振荡神经网络相互抑制的局部膜动态偏差的CMOS LIF神经元
IEEE transactions on biomedical circuits and systems
|June 25, 2025
概括
这项研究引入了一种新的CMOS神经元网络,模仿生物神经振荡器. 该网络产生自我振荡的膜电位,使复杂的发射模式,并证明稳定的运行.
科学领域:
- 神经科学是一个神经科学.
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 生物神经振荡器表现出复杂的自我振荡偏差行为.
- 现有的人工神经元模型往往缺乏复制这些动态偏差机制的能力.
研究的目的:
- 设计和实施一种基于CMOS的神经元网络,能够模拟生物自我振荡偏差.
- 通过使用动态值来研究自我模式输出尖峰的生成.
主要方法:
- 使用漏洞的整合和发射 (LIF) 神经元模型,具有相互抑制和突触疲劳.
- 包含一个激发集成器和一个偏差控制器用于膜电位偏差.
- 使用250纳米CMOS工艺实现了网络.
主要成果:
- 拟议的网络成功地产生了振荡膜潜在偏差.
- 观察到自我模式的输出尖峰,包括切换和动态发射速度模式.
- 该电路表现出稳定的运行,低功耗 (每神经元99.31μW) 和在过程变化下可接受的变异 (18% std dev 在增益中,12% 在振荡周期中).
结论:
- 开发的CMOS神经元网络有效地复制了生物神经元模型的自我振荡行为.
- 该设计为更具生物学可信性的人工神经网络提供了基础.
- 自振动机制为神经形态计算中的动态值提供了一种新的方法.
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