In: NEURAL NETWORKS 18 (9): 1125-1140 NOV 2005
Abstract
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. (c) 2005 Elsevier Ltd.
Author Keywords: spatial learning model; hippocampus; place cells; head direction cells; path integration; calibration and drift removal; nucleus accumbens; reinforcement learning in continuous space
Generic KeyWords: HEAD-DIRECTION CELLS; NEURAL-NETWORK MODEL; FREELY-MOVING RATS; MORRIS WATER MAZE; PATH-INTEGRATION; NUCLEUS-ACCUMBENS; HIPPOCAMPECTOMIZED RATS; SPATIAL ORIENTATION; FIRING PROPERTIES; POPULATION CODES