Centre de Recerca Matemàtica, Bellaterra, Spain.
McGill University and Mila, Montréal, Canada.
Trends Neurosci. 2021 Oct;44(10):808-821. doi: 10.1016/j.tins.2021.07.007. Epub 2021 Sep 1.
Learning to act in an environment to maximise rewards is among the brain's key functions. This process has often been conceptualised within the framework of reinforcement learning, which has also gained prominence in machine learning and artificial intelligence (AI) as a way to optimise decision making. A common aspect of both biological and machine reinforcement learning is the reactivation of previously experienced episodes, referred to as replay. Replay is important for memory consolidation in biological neural networks and is key to stabilising learning in deep neural networks. Here, we review recent developments concerning the functional roles of replay in the fields of neuroscience and AI. Complementary progress suggests how replay might support learning processes, including generalisation and continual learning, affording opportunities to transfer knowledge across the two fields to advance the understanding of biological and artificial learning and memory.
学习在环境中采取行动以最大化奖励是大脑的关键功能之一。这个过程通常在强化学习的框架内进行概念化，强化学习在机器学习和人工智能 (AI) 中也因其优化决策的方式而受到关注。生物和机器强化学习的一个共同方面是对先前经历过的事件的重新激活，称为重放。重放在生物神经网络中的记忆巩固中很重要，也是稳定深度神经网络学习的关键。在这里，我们回顾了最近在神经科学和 AI 领域关于重放的功能作用的研究进展。互补的进展表明重放如何支持学习过程，包括泛化和持续学习，为在这两个领域之间转移知识提供了机会，从而促进对生物和人工学习和记忆的理解。
