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混沌动态分析和伊希克维奇神经元模型的数字硬件设计
Mehdi Nadiri Andabili1, Soheila Nazari2, Tohid Moosazadeh1
1Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Scientific reports
|May 14, 2025
概括
本研究介绍了使用CORDIC算法实现伊希克维奇神经元模型的更快,更节能的神经形态硬件实现. 这种新的方法将速度提高了3倍以上,同时保持了大脑启发的AI和机器人的准确性.
科学领域:
- 神经形态工程的神经形态工程
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 神经形态硬件使得高效,大脑灵感的人工智能成为可能,这对机器人技术至关重要.
- 推进神经形态算法需要与生物神经原理和架构保持一致.
研究的目的:
- 为高效的数字硬件实现提出一个修改后的Izhikevich神经元模型.
- 调查拟议模型的动态行为和参数转换.
- 为了评估硬件的性能与原始模型和先前的研究.
主要方法:
- 使用坐标旋转数字计算机 (CORDIC) 算法实现了一个修改后的Izhikevich神经元模型.
- 利用加法和变换操作来消除硬件效率的乘数.
- 通过分叉图和利亚普诺夫指数分析模型动态,以识别状态过渡.
- 在Spartan6板上使用成本函数对速度,功率和错误率进行硬件性能评估.
主要成果:
- 基于CORDIC的Izhikevich模型准确地复制了生物神经元的行为.
- 数字分析成功地确定了从周期性到混乱动态的过渡.
- 与原始模型相比,拟议的硬件实现在Spartan6板上实现了3.18倍的速度增加.
- 该模型显示出较低的错误率和可接受的功耗.
结论:
- 基于CORDIC的神经形态硬件提供了Izhikevich神经元模型的高速,准确和兼容的实现.
- 这种方法对于涉及非线性和混乱动态的应用有效,例如模拟神经学习过程.
- 开发的硬件为大脑启发的AI和机器人系统带来了重大进步.
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