Is the hippocampus a Kalman filter?
Based on a large body of neurophysiological, neuroanatomical, and behavioral data, it has been suggested that the hippocampal formation serves as a spatial learning and localization system. This spatial representation is metric in nature and arises as a result of associations between sensory inputs and dead-reckoning information generated by the animal. However, despite the fact that these two information streams provide uncertain information (e.g., recognition errors, dead-reckoning drifts, etc.), the hippocampal computational models suggested to date have not explicitly addressed information fusion from erroneous sources. In this paper we develop a computational model of hippocampal spatial learning and relate its functioning to a probabilistic tool used for uncertain sensory fusion in robots: the Kalman filter. This parallel allows us to derive statistically optimal update expressions for the localization performed by our computational model.
Bousquet
O
O
Dept. of Computer Science, Iowa State University, Ames 50011, USA. bousquet@cs.iastate.edu
Balakrishnan
K
K
Honavar
V
V
D000818:Animals; D001369:Axons / Q000502:physiology; D003198:Computer Simulation; D003205:Computing Methodologies; D006624:Hippocampus / Q000033:anatomy & histology / Q000502:physiology*; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D008960:Models, Psychological; D009474:Neurons / Q000145:classification / Q000166:cytology / Q000502:physiology*; D011336:Probability; D051381:Rats; D012371:Robotics; D013028:Space Perception / Q000502:physiology