Place Learning and the Dynamics of Spatial Navigation: A Neural Network Approach

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Abstract
This study presents a real-time neural network capable of describing place learning and the dynamics of spatial navigation. The network incorporates detectors that can be tuned to the values of visual angles of different landmarks as perceived from the spatial location where a reinforcing event is encountered. After a detector has been tuned its output generates an effective stimulus that peaks at the distance from the landmark where positive or negative reinforcement was encountered previously. The outputs of the tuned detectors become associated with the reinforcing event. The network generates spatial generalization surfaces that can guide navigation from any location that is within view of familiar landmark cues, even if that location has never been visited before. Spatial navigation is accomplished by adopting a stimulus-approach principle-that is, by approaching appetitive places and avoiding aversive places. When generalization surfaces are assumed to represent forces exerted by the animal, the dynamics of spatial movements can be described. Computer simulations were carried out for appetitive, aversive, and aversive-appetitive place learning. This article shows that the network correctly describes the navigational trajectories and dynamics of many spatial learning tasks.