Bermudez-Contreras Edgar
Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, Canada.
Biol Cybern. 2021 Apr;115(2):131-134. doi: 10.1007/s00422-021-00862-0. Epub 2021 Feb 9.
Despite the recent advancements and popularity of deep learning that has resulted from the advent of numerous industrial applications, artificial neural networks (ANNs) still lack crucial features from their biological counterparts that could improve their performance and their potential to advance our understanding of how the brain works. One avenue that has been proposed to change this is to strengthen the interaction between artificial intelligence (AI) research and neuroscience. Since their historical beginnings, ANNs and AI, in general, have developed in close alignment with both neuroscience and psychology. In addition to deep learning, reinforcement learning (RL) is another approach that is strongly linked to AI and neuroscience to understand how learning is implemented in the brain. In a recently published article, Botvinick et al. (Neuron, 107:603-616, 2020) explain why deep reinforcement learning (DRL) is important for neuroscience as a framework to study learning, representations and decision making. Here, I summarise Botvinick et al.'s main arguments and frame them in the context of the study of learning, memory and spatial navigation. I believe that applying this approach to study spatial navigation can provide useful insights for the understanding of how the brain builds, processes and stores representations of the outside world to extract knowledge.
尽管深度学习因众多工业应用的出现而取得了最新进展并广受欢迎，但人工神经网络（ANN）仍缺乏其生物对应物的关键特征，而这些特征可能会提高其性能，以及增进我们对大脑工作方式理解的潜力。为改变这种情况而提出的一条途径是加强人工智能（AI）研究与神经科学之间的相互作用。总体而言，自其诞生以来，人工神经网络和人工智能一直与神经科学和心理学紧密结合发展。除了深度学习之外，强化学习（RL）是另一种与人工智能和神经科学紧密相关的方法，用于理解大脑中学习是如何实现的。在最近发表的一篇文章中，博特温尼克等人（《神经元》，第107卷：603 - 616页，2020年）解释了为什么深度强化学习（DRL）作为研究学习、表征和决策的框架对神经科学很重要。在此，我总结博特温尼克等人的主要论点，并将其置于学习、记忆和空间导航的研究背景中。我认为，应用这种方法来研究空间导航可以为理解大脑如何构建、处理和存储外部世界的表征以提取知识提供有用的见解。
