Crossmodal learning and prediction of autobiographical episodic experiences using a sparse distributed memory [Elektronische Ressource] / submitted by Sascha Jockel

University of HamburgFaculty of Mathematics, Informatics and Natural SciencesDepartment of InformaticsCrossmodal Learning andPrediction of AutobiographicalEpisodic Experiences using aSparse Distributed MemoryDoctoral Thesissubmitted bySascha Jockelof HamburgNovember 2009Dissertationzur Erlangung des akademischen GradesDr. rer. nat.an der Fakult at fur Mathematik, Informatik und Naturwissenschaftender Universit at Hamburgeingereicht beim Department InformatikGenehmigt von der MIN-Fakult at, Department Informatikder Universit at Hamburg auf Antrag vonJianwei Zhang, Prof. Dr. (Erstgutachter, Betreuer)Bernd Neumann, Prof. PhD (Zweitgutachter)Hamburg, 12. Mai 2010 (Tag der Disputation)AbstractThis work develops a connectionist memory model for a service robot that satis es a numberof desiderata: associativity, vagueness, approximation, robustness, distribution and paral-lelism. A biologically inspired and mathematically sound theory of a highly distributed andsparse memory serves as the basis for this work. The so-called sparse distributed memory(SDM), developed by P. Kanerva, corresponds roughly to a random-access memory (RAM)of a conventional computer but permits the processing of considerably larger address spaces.Complex structures are represented as binary feature vectors. The model is able to produceexpectations of world states and complement partial sensory patterns of an environmentbased on memorised experience.