Summary: 2005 Special issue
Robust self-localisation and navigation based on hippocampal place cells
Thomas Stro¨sslin *, Denis Sheynikhovich, Ricardo Chavarriaga, Wulfram Gerstner
Laboratory of Computational Neuroscience, Brain and Mind Centre, EPFL, 1015 Lausanne, Switzerland
A computational model of the hippocampal function in spatial learning is presented. A spatial representation is incrementally acquired during
exploration. Visual and self-motion information is fed into a network of rate-coded neurons. A consistent and stable place code emerges by
unsupervised Hebbian learning between place- and head direction cells. Based on this representation, goal-oriented navigation is learnt by
applying a reward-based learning mechanism between the hippocampus and nucleus accumbens. The model, validated on a real and simulated
robot, successfully localises itself by recalibrating its path integrator using visual input. A navigation map is learnt after about 20 trials,
comparable to rats in the water maze. In contrast to previous works, this system processes realistic visual input. No compass is needed for
localisation and the reward-based learning mechanism extends discrete navigation models to continuous space. The model reproduces
experimental findings and suggests several neurophysiological and behavioural predictions in the rat.
q 2005 Elsevier Ltd. All rights reserved.
Keywords: Spatial learning model; Hippocampus; Place cells; Head direction cells; Path integration; Calibration and drift removal; Nucleus accumbens;
Reinforcement learning in continuous space
The striking discovery of place cells in the rat
hippocampus (O'Keefe & Dostrovsky, 1971) has triggered
a wave of interest on spatial learning that holds until today.