Neural Kalman filter

Authors:
Gábor Szirtes;Barnabás Póczos;András Lrincz
Affiliations:
Department of Information Systems, Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117 Budapest, Hungary;Department of Information Systems, Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117 Budapest, Hungary;Department of Information Systems, Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117 Budapest, Hungary
Venue:
Neurocomputing
Year:
2005

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Abstract

Anticipating future events is a crucial function of the central nervous system and can be modelled by Kalman filter-like mechanisms, which are optimal for predicting linear dynamical systems. Connectionist representation of such mechanisms with Hebbian learning rules has not yet been derived. We show that the recursive prediction error method offers a solution that can be mapped onto the entorhinal-hippocampal loop in a biologically plausible way. Model predictions are provided.