Spatial Learning and Localization in Animals: A Computational Model and Behavioral Experiments

This paper describes a computational model of spatial learning and localization. The model is based on the suggestion (based on a large body of experimental data) that rodents learn metric spatial representations of their environments by associating sensory inputs with dead-reckoning based position estimates in the hippocampal place cells. Both these sources of information have some uncertainty associated with them because of errors in sensing, range estimation, and path integration. The proposed model incorporates explicit mechanisms for information fusion from uncertain sources. We demonstrate that the proposed model adequately reproduces several key results of behavioral experiments with animals.