Open Access

ARTICLE

A Map Construction Method Based on the Cognitive Mechanism of Rat Brain Hippocampus

by Naigong Yu^*, Hejie Yu

Department of Information Science, Beijing University of Technology, Beijing, 100124, China

* Corresponding Author: Naigong Yu. Email:

(This article belongs to the Special Issue: Modeling and Analysis of Autonomous Intelligence)

Computer Modeling in Engineering & Sciences 2022, 131(2), 1147-1169. https://doi.org/10.32604/cmes.2022.019430

Received 24 September 2021; Accepted 03 November 2021; Issue published 14 March 2022

Abstract

The entorhinal-hippocampus structure in the mammalian brain is the core area for realizing spatial cognition. However, the visual perception and loop detection methods in the current biomimetic robot navigation model still rely on traditional visual SLAM schemes and lack the process of exploring and applying biological visual methods. Based on this, we propose a map construction method that mimics the entorhinal-hippocampal cognitive mechanism of the rat brain according to the response of entorhinal cortex neurons to eye saccades in recent related studies. That is, when mammals are free to watch the scene, the entorhinal cortex neurons will encode the saccade position of the eyeball to realize the episodic memory function. The characteristics of this model are as follows: 1) A scene memory algorithm that relies on visual saccade vectors is constructed to imitate the biological brain's memory of environmental situation information matches the current scene information with the memory; 2) According to the information transmission mechanism formed by the hippocampus and the activation theory of spatial cells, a localization model based on the grid cells of the entorhinal cortex and the place cells of the hippocampus was constructed; 3) Finally, the scene memory algorithm is used to correct the errors of the positioning model and complete the process of constructing the cognitive map. The model was subjected to simulation experiments on publicly available datasets and physical experiments using a mobile robot platform to verify the environmental adaptability and robustness of the algorithm. The algorithm will provide a basis for further research into bionic robot navigation.

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