Simulation of Place Fields in the Computational Model of Rodent Spatial Learning

Contents

Recent work by Balakrishnan et al (1997) has explored a Kalman filter model of animal spatial learning the presence uncertainty in sensory as well as dead-reckoning estimates. This model was able to successfully account for several of the behavioral experiments reported in the animal navigation literature, namely, behavioral experiments by Morris (1981) and Collett and colleagues (1986). This paper extends this model in some important directions. It accounts for the observed firing patterns of hippocampal neurons found by Sharp and colleagues (1990) in visually symmetric environments that offer multiple sensory cues. It incorporates mechanisms that allow for differential contribution from proximal as opposed to distal landmarks during localization. It also supports learning of associations between rewards and places to guide goal-directed navigation.