大脑启发的混乱的尖反向传播
Zijian Wang1, Peng Tao1, Luonan Chen1,2,3,4
1Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
National science review
|May 6, 2024
概括
混乱的尖端反向传播 (CSBP) 增强了对节能尖端神经网络 (SNN) 的培训. 这种新的方法通过模仿类似大脑的混乱动态来提高准确性和稳定性,克服SNN学习中的局部最小问题.
科学领域:
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 尖端神经网络 (SNNs) 通过模仿生物神经系统来提供高能效,但很难有效地训练.
- 当前的训练方法,如替代梯度,往往导致SNN因为依赖梯度动力学而陷入局部最小值.
研究的目的:
- 引入一种新的训练方法 - - 混乱的尖端反向传播 (CSBP),以提高SNN的性能.
- 利用大脑启发的混乱动态来提高SNN学习的有效性和稳定性.
主要方法:
- 开发了CSBP,结合了损失函数,在训练期间产生类似大脑的混乱动态.
- 利用混乱动态的ergodic和伪随机性质来促进SNN学习.
- 分析了理论学习过程,观察了最初的混乱,随后是分叉和趋同到梯度动力学.
主要成果:
- 在神经形态 (DVS-CIFAR10,DVS-Gesture) 和大规模静态数据集 (CIFAR100,ImageNet) 上,CSBP显著超过了最先进的方法.
- 在使用CSBP方法的训练有素的SNNs中表现出卓越的准确性和稳定性.
- 理论分析证实了学习轨迹反映了生物大脑活动模式.
结论:
- CSBP提供了一种强大的新工具,用于直接和有效地训练尖端神经网络.
- 该方法通过利用混乱动态来提高SNN性能,解决传统梯度式方法的局限性.
- 通过计算建模,提供了对生物大脑学习机制的宝贵见解.
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