Place Cells and Spatial Navigation Based on 2D Visual Feature Extraction, Path Integration, and Reinforcement Learning

Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)

Authors

Angelo Arleo, Fabrizio Smeraldi, Stéphane Hug, Wulfram Gerstner

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.