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CLC number: TP391; TP24

On-line Access: 2016-05-04

Received: 2015-10-21

Revision Accepted: 2016-03-16

Crosschecked: 2016-04-11

Cited: 0

Clicked: 1620

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yang Zhou

http://orcid.org/0000-0002-8384-7842

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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.5 P.435-448

http://doi.org/10.1631/FITEE.1500364


Biologically inspired model of path integration based on head direction cells and grid cells


Author(s):  Yang Zhou, De-wei Wu

Affiliation(s):  College of Information and Navigation, Air Force Engineering University, Xi’an 710077, China

Corresponding email(s):   yydayl@sina.cn, wudewei74609@126.com

Key Words:  Head direction cells (HDCs), Grid cells (GCs), Path integration, Bionic navigation


Yang Zhou, De-wei Wu. Biologically inspired model of path integration based on head direction cells and grid cells[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(5): 435-448.

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Abstract: 
Some neurons in the brain of freely moving rodents show special firing pattern. The firing of head direction cells (HDCs) and grid cells (GCs) is related to the moving direction and distance, respectively. Thus, it is considered that these cells play an important role in the rodents’ path integration. To provide a bionic approach for the vehicle to achieve path integration, we present a biologically inspired model of path integration based on the firing characteristics of HDCs and GCs. The detailed implementation process of this model is discussed. Besides, the proposed model is realized by simulation, and the path integration performance is analyzed under different conditions. Simulations validate that the proposed model is effective and stable.

This paper develops a novel path integration model based on neurobiological concepts. Overall, the model was well explained.

基于头朝向细胞和网格细胞的生物启发式路径整合模型

目的:以脑神经科学为基础,结合头朝向细胞(head direction cells)和网格细胞(grid cells)的放电特性,为运行体智能自主导航提供一种生物启发式的路径整合模型。
创新点:分别建立了头朝向细胞和网格细胞与方向和距离间的对应关系,以此解决路径整合问题。并针对模型的实现提出了网格间距和放电野半径的设置条件。
方法:首先,采用头朝向细胞感知其表征方向上的速度,并对运行方向进行度量。其次,相对头朝向细胞引入对应的距离细胞(distance cells),用于处理各头朝向细胞感知的速度,并对相对方位进行度量。最后,距离细胞处理得到的位移输入到网格细胞中用于计算各网格细胞的放电率,并结合网格细胞的放电样式,对运行距离进行度量。以此,运行体通过各类细胞的放电情况感知运行方向、相对方位以及运行距离,最终实现路径整合。
结论:本文有效地将头朝向细胞和网格细胞的放电特性与路径整合所需的方向和距离进行了关联。运行体能够通过感知各类细胞的放电情况实现路径整合,且实现的路径整合性能具有一定稳定性。

关键词:头朝向细胞;网格细胞;路径整合;仿生导航

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