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Place cells and spatial navigation based on vision, path integration, and reinforcement learning

  1. Arleo, F. Smeraldi, S. Hug and W. Gerstner, Advances in Neural Information Processing Systems, vol. 13, pages 89-95, MIT Press, December 2000

Abstract


We model hippocampal place cells and head-direction cells by combining allothetic (visual) and idiothetic (proprioceptive) stimuli. Visual input, 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. Unsupervised Hebbian learning is employed to incrementally build a population of localized overlapping place fields. Place cells serve as basis functions for reinforcement learning. Experimental results for goal-oriented navigation of a mobile robot are presented.

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