采用Fitzhugh-Nagumo神经元模型的数字电路的动态检查,强调低功耗和高精度
Mehdi Nadiri Andabili1, Soheila Nazari2, Tohid Moosazadeh1
1Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
PloS one
|August 20, 2025
概括
这项研究介绍了基于CORDIC的Fitzhugh-Nagumo神经形态计算模型. 这种以大脑为灵感的尖端神经网络硬件为模拟神经过程提供了高精度和低功耗.
科学领域:
- 神经形态工程
- 计算神经科学
- 数字硬件设计
背景情况:
- 神经形态计算旨在复制大脑的功能.
- 尖端神经元和尖端神经网络 (SNN) 对于理解脑启发系统至关重要.
- 为了推进SNN,神经元模型的高效硬件实现至关重要.
研究的目的:
- 使用坐标旋转数字计算机 (CORDIC) 算法提出一个修改后的Fitzhugh-Nagumo (FHN) 神经元模型.
- 用高精度模拟原始FHN模型的生物行为.
- 展示低功耗的高效数字硬件实现.
主要方法:
- 使用CORDIC算法进行修改的Fitzhugh-Nagumo (FHN) 模型开发.
- 通过添加和转移操作消除乘数,以提高硬件效率.
- 对错误,动态,混乱行为 (分叉,莱普诺夫指数),操作频率和功耗的分析.
主要成果:
- 基于CORDIC的FHN模型准确地模拟了原始模型的生物行为.
- 在基于CORDIC和原始FHN模型之间的高度兼容性得到证实.
- 拟议的硬件具有低功耗,最小的错误率和高频能力.
结论:
- 基于CORDIC的FHN模型提供了高效的硬件实现,由于低功耗,优于原始模型.
- 开发的硬件适用于涉及非线性和混乱的神经过程的应用,例如学习模拟.
- 这种方法推进了复杂的脑启发计算的实用神经形态系统的发展.
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