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A Biological Plausible Spatial Recognition Model in Robots Based on Error Back- Propagation Algorithm

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Author(s): Naigong Yu | Huanzhao Chen | Lin Wang | Xiaogang Ruan

Journal: Transactions on Computer Science and Technology
ISSN 2327-090X

Volume: 2;
Issue: 3;
Start page: 31;
Date: 2013;
Original page

Keywords: Spatial Representation | Hippocampus | BP Algorithm | Cognition Map

ABSTRACT
This paper proposes a model based on rat’s hippocampus which can be used in the process of navigation for robot. Both grid cells and place cells in hippocampus play important roles in the process. The firing of grid and place cells are formed from persistent spiking grid cell model by Hasselmo; while the firing of place cells are formed by linear summation of appropriately weighted inputs from entorhinal grid cells through error back-propagation (BP) neural network. Every single confined place field could be formed by summing inputs from a modest number of grid cells with relatively similar grid phases, diverse grid orientations, and a biologically plausible range of grid spacings. As a result, spatial information is stored in robot in a way of place and grid cell firing.
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