A hybrid model for rodent spatial learning and localization

Balakrishnan et al. (1998, 1999) have explored a Kalman filter model of animal spatial learning the presence of uncertainty in sensory as well as path integration estimates. This model was able to successfully account for several of the behavioral experiments reported in the animal navigation literature. This paper extends this model in some important directions. It accounts for the observed firing patterns of hippocampal neutrons in visually symmetric environments that offer polarizing sensory cues. It incorporates mechanisms that allow for differential contribution from proximal and distal landmarks during localization. It also supports learning of associations between rewards and places to guide goal-directed navigation