Simulation of place fields in the computational model of rodent spatial learning

Recent work (Balakrishnan, Bousquet, & Honavar 1997; Balakfishnan, Bhatt, & Honavar 1998) has ex- plored a Kalman filter model of animal spatial learning the.presence uncertainty in sensory as well as dead- reckoning estimates. This model was able to success- fully account for several of the behavioral experiments reported in the animal navigation literature (Morris 1981; Collett, Cartwright, & Smith 1986). This paper extends this model in some important directions. It ac- counts for the observed firing patterns of hippocampal neurons (Sharp, Kubie, & Muller 1990) in visually sym- metric environments that offer multiple sensory cues. It incorporates mechanisms that allow for differential contribution from proximal as opposed to distal land- marks during localization. It also supports learning of associations between rewards a~d places to guide goal- directed navigation.