基于多尺度网格细胞的路径整合模型

doi:10.12305/j.issn.1001-506X.2021.10.31

Abstract

Abstract:

To solve the problem of autonomic spatial location estimation using brain-like mechanism, we propose a path integration model based on new superposition algorithm. By introducing multi-scale grid cell, the accuracy of path integration is improved. In path integration model, both grid cell model and head-direction model are realized by attractor network. The path integration model judges the state through the head direction's change to complete different integration process, and calculates the displacement changes of multi-scale grid cell model during linear motion and curvilinear motion via a dynamic weight to improve the accuracy of path integration. The results show that both single-scale and multi-scale model can complete path integration, and in the distance of 3 000 m, the error of multi-scale grid cell model does not exceed 15 m. It is proved that the multi-scale model can improve the accuracy of path integration and verified that the path integration model is effective.

Key words: multi-scale, grid cell, head direction cell, attractor network model, path integration

CLC Number:

TP183

Chenhao ZHAO, Deiwei WU, Kun HAN, Haonan ZHU, Chuanjin DAI. Path integration model based on multi-scale grid cell[J]. Systems Engineering and Electronics, 2021, 43(10): 2961-2967.

Figures/Tables 7

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References 22

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参数	数值
头朝向细胞个数	36
旋转细胞个数	72
α_HD	0
β_HD	0.2
网格细胞的个数	40×40
α_gc	1
λ₁	13
λ₂	15
λ₃	17
λ₄	19
λ₅	21
β_gc	0.017 8
γ	0.019 5
l	2
a	1
τ/ms	10
dt/ms	0.5

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Path integration model based on multi-scale grid cell

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