生物可信的局部突触学习规则坚定地实施深度监督学习
Masataka Konishi1, Kei M Igarashi2, Keiji Miura1
1Department of Biosciences, School of Biological and Environmental Sciences, Kwansei Gakuin University, Sanda, Hyogo, Japan.
Frontiers in neuroscience
|October 27, 2023
概括
生物学上可信的学习规则,如FA_Ex-100%,模仿深度神经网络的大脑功能. 这种新的方法与反向传播性能相匹配,并显示对噪声的稳定性,这表明了大脑启发的AI的途径.
科学领域:
- 计算神经科学是一种计算神经科学.
- 人工智能的人工智能是人工智能.
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 依赖于中间层的表示学习以提高效率.
- 目前的反向传播 (BP) 学习规则缺乏对大脑实施的生物可信性.
- 开发类似于大脑的学习规则对于理解记忆和认知至关重要.
研究的目的:
- 为DNN开发和评估生物学上可信的学习规则.
- 将这些规则与既定方法和动物学习表现进行比较.
- 为了研究一种新型变体,FA_Ex-100%,结合直接多巴胺信号.
主要方法:
- 对DNN执行奖励预测任务的数值模拟.
- 极端学习机器 (ELM),重量扰动 (WP) 和反调整 (FA) 规则的实施和比较.
- 使用模拟局部错误信号开发和测试FA_Ex-100%.
主要成果:
- FA取得了与BP相当的业绩,表现优于ELM和WP.
- FA_Ex-100%的性能也与BP相比.
- FA_Ex-100%证明了对干扰和噪音的强度.
结论:
- 简化,生物可信的学习规则,如FA_Ex-100%,可以支持深度监督学习.
- 准确的错误信号,可能通过多巴胺基神经元,是强大的学习的关键.
- 这项研究提供了对大脑启发的AI和神经计算的见解.
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