title: A Self-Organizing Incremental Spatiotemporal Associative Memory Networks Model for Problems with Hidden State.
abstract: Identifying the hidden state is important for solving problems with hidden state. We prove any deterministic partially observable Markov decision processes (POMDP) can be represented by a minimal, looping hidden state transition model and propose a heuristic state transition model constructing algorithm. A new spatiotemporal associative memory network (STAMN) is proposed to realize the minimal, looping hidden state transition model. STAMN utilizes the neuroactivity decay to realize the short-term memory, connection weights between different nodes to represent long-term memory, presynaptic potentials, and synchronized activation mechanism to complete identifying and recalling simultaneously. Finally, we give the empirical illustrations of the STAMN and compare the performance of the STAMN model with that of other methods.
authors.affiliation: Department of Computer Science and Software, Tianjin Polytechnic University, Tianjin 300387, China.
authors.forename: Zuo-Wei
authors.identifier: 0000-0002-0058-9965
authors.initials: ZW
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001244:Association; D003198:Computer Simulation; D004777:Environment; D006801:Humans; D008390:Markov Chains; D008568:Memory / Q000502:physiology*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013997:Time Factors