强化学习作为昆虫导航的机器人启发框架:从空间表征到神经实现

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-09-09 DOI:10.3389/fncom.2024.1460006
Stephan Lochner, Daniel Honerkamp, Abhinav Valada, Andrew D. Straw
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引用次数: 0

摘要

蜜蜂是昆虫世界的导航大师。尽管机器人导航研究取得了令人瞩目的进展,但就训练效率和泛化能力而言,这些昆虫的表现仍然是任何人工系统无法比拟的,特别是考虑到有限的计算能力。另一方面,人们对这些非凡壮举背后的计算原理仍然只有部分了解。强化学习(RL)的理论框架提供了一个理想的焦点,可将这两个领域结合起来,互惠互利。特别是,我们通过 RL 的视角来分析和比较机器人和昆虫导航模型中的空间表征,因为昆虫导航的效率很可能源于一种高效而强大的内部表征,它将视网膜(以自我为中心)的视觉输入与环境的几何形状联系在一起。虽然 RL 长期以来一直是机器人导航研究的核心,但目前昆虫导航的计算理论并不常见于这一框架内,而主要是在昆虫大脑,尤其是蘑菇体(MB)中实施的联想学习过程。在这里,我们提出了蘑菇体电路的具体假定组件,这些组件能够实现某类相对简单的 RL 算法,能够整合导航任务的不同组件,让人联想到机器人导航中使用的分层 RL 模型。我们讨论了当前的昆虫和机器人导航模型是如何探索经典的、完整的地图式表征之外的表征的,空间信息在不同程度上被嵌入到各自的潜在表征中。
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Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation
Bees are among the master navigators of the insect world. Despite impressive advances in robot navigation research, the performance of these insects is still unrivaled by any artificial system in terms of training efficiency and generalization capabilities, particularly considering the limited computational capacity. On the other hand, computational principles underlying these extraordinary feats are still only partially understood. The theoretical framework of reinforcement learning (RL) provides an ideal focal point to bring the two fields together for mutual benefit. In particular, we analyze and compare representations of space in robot and insect navigation models through the lens of RL, as the efficiency of insect navigation is likely rooted in an efficient and robust internal representation, linking retinotopic (egocentric) visual input with the geometry of the environment. While RL has long been at the core of robot navigation research, current computational theories of insect navigation are not commonly formulated within this framework, but largely as an associative learning process implemented in the insect brain, especially in the mushroom body (MB). Here we propose specific hypothetical components of the MB circuit that would enable the implementation of a certain class of relatively simple RL algorithms, capable of integrating distinct components of a navigation task, reminiscent of hierarchical RL models used in robot navigation. We discuss how current models of insect and robot navigation are exploring representations beyond classical, complete map-like representations, with spatial information being embedded in the respective latent representations to varying degrees.
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
期刊最新文献
Editorial: Computational modeling and machine learning methods in neurodevelopment and neurodegeneration: from basic research to clinical applications. Simulated synapse loss induces depression-like behaviors in deep reinforcement learning. Systematic review of cognitive impairment in drivers through mental workload using physiological measures of heart rate variability. Facial emotion recognition using deep quantum and advanced transfer learning mechanism. BrainNet: an automated approach for brain stress prediction utilizing electrodermal activity signal with XLNet model.
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