在大规模神经人口中, Hindmarsh-Rose神经元模型的非线性行为的高效数字设计
Soheila Nazari1, Shabnam Jamshidi2
1Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran. soheilanazari21@yahoo.com.
Scientific reports
|February 15, 2024
概括
这项研究引入了一种新的数字硬件模型,用于使用CORDIC近似的Hindmarsh-Rose (HR) 神经元,从而实现高效,低功耗的尖端神经网络用于认知过程和图像分析.
科学领域:
- 神经科学是一个神经科学.
- 计算机工程 计算机工程
- 人工智能的人工智能
背景情况:
- 尖端神经网络 (SNN) 为认知任务提供低功耗.
- 神经形态系统是SNN硬件实现的关键.
- 高效的数字神经元设计对于复杂的认知过程至关重要.
研究的目的:
- 介绍第一个使用CORDIC近似来降低硬件成本的Hindmarsh-Rose (HR) 神经元的数字实现.
- 为SNN开发一种高效的硬件模型,能够实现高层次的认知功能.
- 在图像处理应用中评估拟议模型的性能.
主要方法:
- 使用CORDIC近似方法实现印度-罗斯 (HR) 神经元的数字化实现.
- 分析拟议的CORDIC_HR模型的行为,使用分叉图,相位空间和nullcline空间.
- 尖端网络的硬件实现用于边缘检测,消除噪音和图像放大.
- 拟议模型与原始HR神经元模型在准确性和性能方面进行比较.
主要成果:
- 与之前的研究相比,CORDIC_HR模型实现了较低的硬件实施成本.
- 拟议的HR神经元模型准确地遵循原始模型在时间域中的行为,并显著降低了误差.
- 分析证实,拟议模型准确地复制了原始HR神经元的复杂非线性动态.
- 基于拟议模型的神经元群体表现出与原始模型相同的功能和行为性能.
结论:
- 提出的HR神经元高效硬件模型提供了减少资源消耗和高精度.
- CORDIC_HR模型使SNNs的低消耗硬件实现用于高级认知功能.
- 拟议的模型在图像处理任务中显示了可接受的性能,例如消除噪音和边缘检测.
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