Place Cells and Spatial Navigation Based on 2D Visual Feature Extraction, Path Integration, and Reinforcement Learning
Abstract
We model hippocampal place cells and head-direction cells by combin(cid:173) ing allothetic (visual) and idiothetic (proprioceptive) stimuli. Visual in(cid:173) put, provided by a video camera on a miniature robot, is preprocessed by a set of Gabor filters on 31 nodes of a log-polar retinotopic graph. Unsu(cid:173) pervised Hebbian learning is employed to incrementally build a popula(cid:173) tion of localized overlapping place fields. Place cells serve as basis func(cid:173) tions for reinforcement learning. Experimental results for goal-oriented navigation of a mobile robot are presented.
Cite
Text
Arleo et al. "Place Cells and Spatial Navigation Based on 2D Visual Feature Extraction, Path Integration, and Reinforcement Learning." Neural Information Processing Systems, 2000.Markdown
[Arleo et al. "Place Cells and Spatial Navigation Based on 2D Visual Feature Extraction, Path Integration, and Reinforcement Learning." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/arleo2000neurips-place/)BibTeX
@inproceedings{arleo2000neurips-place,
title = {{Place Cells and Spatial Navigation Based on 2D Visual Feature Extraction, Path Integration, and Reinforcement Learning}},
author = {Arleo, Angelo and Smeraldi, Fabrizio and Hug, Stéphane and Gerstner, Wulfram},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {89-95},
url = {https://mlanthology.org/neurips/2000/arleo2000neurips-place/}
}