基于信息瓶的Hebbian学习规则自然地将工作记忆和突触更新联系在一起
Kyle Daruwalla1, Mikko Lipasti2
1Cold Spring Harbor Laboratory, Long Island, NY, United States.
Frontiers in computational neuroscience
|May 31, 2024
概括
我们为使用辅助记忆网络的尖端神经网络 (SNN) 引入了一种新的学习规则. 这种方法可以在神经形态硬件上对SNN进行高效,生物可信的训练,将工作记忆和突触更新联系起来.
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.
- 机器学习是机器学习.
背景情况:
- 深度神经网络 (DNN) 面临训练和部署的巨大能源成本.
- 尖端神经网络 (SNN) 为神经形态硬件提供了节能替代方案,但面临着训练挑战.
- 反向传播是一种标准的DNN培训方法,对SNN来说在生物学上是不可思议的.
研究的目的:
- 为SNNs开发一种生物学上可信和节能的训练方法.
- 解决现有的SNN培训规则的局限性,特别是需要并发样本处理.
- 在SNN中建立工作记忆和突触可塑性之间的直接联系.
主要方法:
- 为SNN提出了一个新的三因素Hebbian更新规则.
- 整合了一个辅助内存网络来处理样本中的全球错误信号.
- 在主网络之前独立训练辅助网络.
- 对图像分类任务的评估性能.
主要成果:
- 实现了与图像分类的基线方法相比较的性能.
- 工作记忆和突触更新之间有直接的联系,与反向传播不同.
- 展示了记忆能力对学习表现的影响.
结论:
- 拟议的学习规则为训练SNN提供了一种生物学上可信和有效的方法.
- 这项工作在工作记忆和突触可塑性之间建立了明确的联系.
- 建议神经计算的新视角,其中层平衡记忆信息压缩和任务性能.
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