Hippocampal formation-inspired global self-localization: quick recovery from the kidnapped robot problem from an egocentric perspective

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-07-18 DOI:10.3389/fncom.2024.1398851
Takeshi Nakashima, Shunsuke Otake, Akira Taniguchi, Katsuyoshi Maeyama, Lotfi El Hafi, Tadahiro Taniguchi, Hiroshi Yamakawa
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Abstract

It remains difficult for mobile robots to continue accurate self-localization when they are suddenly teleported to a location that is different from their beliefs during navigation. Incorporating insights from neuroscience into developing a spatial cognition model for mobile robots may make it possible to acquire the ability to respond appropriately to changing situations, similar to living organisms. Recent neuroscience research has shown that during teleportation in rat navigation, neural populations of place cells in the cornu ammonis-3 region of the hippocampus, which are sparse representations of each other, switch discretely. In this study, we construct a spatial cognition model using brain reference architecture-driven development, a method for developing brain-inspired software that is functionally and structurally consistent with the brain. The spatial cognition model was realized by integrating the recurrent state—space model, a world model, with Monte Carlo localization to infer allocentric self-positions within the framework of neuro-symbol emergence in the robotics toolkit. The spatial cognition model, which models the cornu ammonis-1 and -3 regions with each latent variable, demonstrated improved self-localization performance of mobile robots during teleportation in a simulation environment. Moreover, it was confirmed that sparse neural activity could be obtained for the latent variables corresponding to cornu ammonis-3. These results suggest that spatial cognition models incorporating neuroscience insights can contribute to improving the self-localization technology for mobile robots. The project website is https://nakashimatakeshi.github.io/HF-IGL/.
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海马体形成启发的全局自我定位:从自我中心视角快速解决被绑架机器人问题
当移动机器人在导航过程中突然被传送到一个与其信念不同的位置时,它们仍然很难继续进行准确的自我定位。在开发移动机器人的空间认知模型时融入神经科学的见解,可能会使移动机器人获得对不断变化的情况做出适当反应的能力,类似于生物体。最近的神经科学研究表明,在大鼠导航的远距离传送过程中,海马角氨-3区的位置细胞神经群会离散切换,而这些神经群是彼此稀疏的表征。在这项研究中,我们利用大脑参考架构驱动的开发方法构建了一个空间认知模型,这种方法用于开发在功能和结构上与大脑一致的大脑启发软件。空间认知模型是在机器人工具包的神经符号涌现框架内,通过将循环状态空间模型(一种世界模型)与蒙特卡洛定位推断分配中心自我位置相结合而实现的。该空间认知模型利用每个潜变量对 cornu ammonis-1 和 -3 区域进行建模,在模拟环境中展示了移动机器人在远距传物过程中自我定位性能的提高。此外,研究还证实,与 cornu ammonis-3 相对应的潜变量可以获得稀疏的神经活动。这些结果表明,结合神经科学见解的空间认知模型有助于改进移动机器人的自我定位技术。项目网站:https://nakashimatakeshi.github.io/HF-IGL/。
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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
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