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Deep reinforcement learning to study spatial navigation, learning and memory in artificial and biological agents
Biological Cybernetics ( IF 1.9 ) Pub Date : 2021-02-09 , DOI: 10.1007/s00422-021-00862-0
Edgar Bermudez-Contreras 1
Affiliation  

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.



中文翻译:

深度强化学习研究人工和生物制剂的空间导航、学习和记忆

尽管由于众多工业应用的出现,深度学习最近取得了进步和普及,但人工神经网络 (ANN) 仍然缺乏来自其生物学对应物的关键特征,这些特征可以提高其性能和促进我们对大脑如何理解的潜力作品。已提议改变这种状况的一种途径是加强人工智能 (AI) 研究与神经科学之间的互动。自从它们的历史开始以来,一般来说,人工神经网络和人工智能的发展与神经科学和心理学密切相关。除了深度学习之外,强化学习 (RL) 是另一种与 AI 和神经科学密切相关的方法,用于了解学习是如何在大脑中实现的。在最近发表的一篇文章中,Botvinick 等人。(神经元,107:603–616, 2020) 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。在这里,我总结了 Botvinick 等人的主要论点,并将它们置于学习、记忆和空间导航研究的背景下。我相信将这种方法应用于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。

更新日期:2021-02-09
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