Recent advances in artificial intelligence (AI) and neuroscience are impressive. In AI, this includes the development of computer programs that can beat a grandmaster at GO or outperform human radiologists at cancer detection. A great deal of these technological developments are directly related to progress in artificial neural networks—initially inspired by our knowledge about how the brain carries out computation. In parallel, neuroscience has also experienced significant advances in understanding the brain. For example, in the field of spatial navigation, knowledge about the mechanisms and brain regions involved in neural computations of cognitive maps—an internal representation of space—recently received the Nobel Prize in medicine. Much of the recent progress in neuroscience has partly been due to the development of technology used to record from very large populations of neurons in multiple regions of the brain with exquisite temporal and spatial resolution in behaving animals. With the advent of the vast quantities of data that these techniques allow us to collect there has been an increased interest in the intersection between AI and neuroscience, many of these intersections involve using AI as a novel tool to explore and analyze these large data sets. However, given the common initial motivation point—to understand the brain—these disciplines could be more strongly linked. Currently much of this potential synergy is not being realized. We propose that spatial navigation is an excellent area in which these two disciplines can converge to help advance what we know about the brain. In this review, we first summarize progress in the neuroscience of spatial navigation and reinforcement learning. We then turn our attention to discuss how spatial navigation has been modeled using descriptive, mechanistic, and normative approaches and the use of AI in such models. Next, we discuss how AI can advance neuroscience, how neuroscience can advance AI, and the limitations of these approaches. We finally conclude by highlighting promising lines of research in which spatial navigation can be the point of intersection between neuroscience and AI and how this can contribute to the advancement of the understanding of intelligent behavior.

中文翻译：

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空间导航的神经科学及其与人工智能的关系


人工智能 (AI) 和神经科学的最新进展令人印象深刻。在人工智能领域，这包括开发可以在围棋方面击败大师或在癌症检测方面胜过人类放射科医生的计算机程序。这些技术发展中有很大一部分与人工神经网络的进步直接相关——最初是受到我们关于大脑如何进行计算的知识的启发。与此同时，神经科学在理解大脑方面也取得了重大进展。例如，在空间导航领域，有关认知图（空间的内部表示）的神经计算所涉及的机制和大脑区域的知识最近获得了诺贝尔医学奖。神经科学的最新进展在一定程度上归功于技术的发展，该技术用于记录动物大脑多个区域的大量神经元，并具有精确的时间和空间分辨率。随着这些技术允许我们收集大量数据的出现，人们对人工智能和神经科学之间的交叉点越来越感兴趣，其中许多交叉点都涉及使用人工智能作为探索和分析这些大型数据集的新工具。然而，考虑到共同的初始动机——了解大脑——这些学科之间的联系可能会更紧密。目前，这种潜在的协同效应大部分尚未实现。我们认为，空间导航是一个很好的领域，这两个学科可以融合在一起，帮助推进我们对大脑的了解。在这篇综述中，我们首先总结了空间导航和强化学习的神经科学进展。 然后，我们将注意力转向讨论如何使用描述性、机械性和规范性方法对空间导航进行建模，以及如何在此类模型中使用人工智能。接下来，我们讨论人工智能如何推进神经科学，神经科学如何推进人工智能，以及这些方法的局限性。最后，我们强调了一些有前途的研究方向，其中空间导航可以成为神经科学和人工智能之间的交叉点，以及这如何有助于促进对智能行为的理解。