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.
Dept. of Computer Science, Iowa State University, Ames 50011, USA. bousquet@cs.iastate.edu
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Bousquet
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Balakrishnan
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Honavar
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