A grid field calculation model based on perceived speed and perceived angle.
10.7507/1001-5515.201911058
- Author:
Naigong YU
1
,
2
;
Hui FENG
1
,
2
;
Yishen LIAO
1
,
2
;
Xiangguo ZHENG
1
,
2
Author Information
1. Department of Informatics, Beijing University of Technology, Beijing 100124, P.R.China
2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, P.R.China.
- Publication Type:Journal Article
- Keywords:
grid cell;
grid field computing model;
perceived angle;
perceived speed;
speed cell
- MeSH:
Action Potentials;
Computer Simulation;
Computer Systems;
Entorhinal Cortex;
Grid Cells;
Hippocampus;
Models, Neurological
- From:
Journal of Biomedical Engineering
2020;37(5):863-874
- CountryChina
- Language:Chinese
-
Abstract:
The method of directly using speed information and angle information to drive attractors model of grid cells to encode environment has poor anti-interference ability and is not bionic. In response to the problem, this paper proposes a grid field calculation model based on perceived speed and perceived angle. The model has the following characteristics. Firstly, visual stream is decoded to obtain visual speed, and speed cell is modeled and decoded to obtain body speed. Visual speed and body speed are integrated to obtain perceived speed information. Secondly, a one-dimensional circularly connected cell model with excitatory connection is used to simulate the firing mechanism of head direction cells, so that the robot obtains current perception angle information in a biomimetic manner. Finally, the two kinds of perceptual information of speed and angle are combined to realize the driving of grid cell attractors model. The proposed model was experimentally verified. The results showed that this model could realize periodic hexagonal firing field mode of grid cells and precise path integration function. The proposed algorithm may provide a foundation for the research on construction method of robot cognitive map based on hippocampal cognition mechanism.
