A Spatial Cognitive Model that Integrates the Effects of Endogenous and Exogenous Information on the Hippocampus and Striatum

Reproducing the spatial cognition of animals using computational models that make agents navigate autonomously has attracted much attention. Many biologically inspired models for spatial cognition focus mainly on the simulation of the hippocampus and only consider the effect of external environmenta...

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Bibliographic Details
Published inInternational journal of automation and computing Vol. 18; no. 4; pp. 632 - 644
Main Authors Huang, Jing, Yang, He-Yuan, Ruan, Xiao-Gang, Yu, Nai-Gong, Zuo, Guo-Yu, Liu, Hao-Meng
Format Journal Article
LanguageEnglish
Published Beijing Institute of Automation, Chinese Academy of Sciences 01.08.2021
Springer Nature B.V
Subjects
Animals
Autonomous navigation
CAE) and Design
Coding
Cognition
Cognition & reasoning
Cognitive models
Computer Applications
Computer-Aided Engineering (CAD
Control
Engineering
Hippocampus
Mechatronics
Navigation
Research Article
Robotics
spatial cognition
brain-inspired computation
hippocampus
striatum
Exogenous and endogenous information
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Summary:Reproducing the spatial cognition of animals using computational models that make agents navigate autonomously has attracted much attention. Many biologically inspired models for spatial cognition focus mainly on the simulation of the hippocampus and only consider the effect of external environmental information (i.e., exogenous information) on the hippocampal coding. However, neurophysiological studies have shown that the striatum, which is closely related to the hippocampus, also plays an important role in spatial cognition and that information inside animals (i.e., endogenous information) also affects the encoding of the hippocampus. Inspired by the progress made in neurophysiological studies, we propose a new spatial cognitive model that consists of analogies between the hippocampus and striatum. This model takes into consideration how both exogenous and endogenous information affects coding by the environment. We carried out a series of navigation experiments that simulated a water maze and compared our model with other models. Our model is self-adaptable and robust and has better performance in navigation path length. We also discuss the possible reasons for the results and how our findings may help us understand real mechanisms in the spatial cognition of animals.
ISSN:1476-8186
2153-182X
1751-8520
2153-1838
DOI:10.1007/s11633-021-1286-z