A neuromorphic SLAM system shows potential for more efficient implementation than its traditional counterpart. We demonstrate a mixed-mode implementation for spatial encoding neurons including theta cells, vector cells, and place cells. Together, they form a biologically plausible network that could reproduce the localization functionality of place cells. The system consists of a theta chip with 128 units and an FPGA encoding 4 networks for vector cells formation that provides the capability for tracking on a 11 by 11 place cell grid. Experimental results validate the robustness of our model when suffering from 18% standard deviation from mathematical models induced by variations of analog circuits. We provide a foundation for implementing dynamic neuromorphic SLAM systems for nonhomogeneous mapping and inspirations for the formation of spatial cells in biology.
Cite
@article{arxiv.2310.10790,
title = {Neuromorphic Place Cells},
author = {Zhaoqi Chen and Ralph Etienne-Cummings},
journal= {arXiv preprint arXiv:2310.10790},
year = {2023}
}