TY - JOUR A2 - GraƱa, Manuel AU - Wang, Zuo-wei PY - 2016 DA - 2016/11/03 TI - A Self-Organizing Incremental Spatiotemporal Associative Memory Networks Model for Problems with Hidden State SP - 7158507 VL - 2016 AB - 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. SN - 1687-5265 UR - https://doi.org/10.1155/2016/7158507 DO - 10.1155/2016/7158507 JF - Computational Intelligence and Neuroscience PB - Hindawi Publishing Corporation KW - ER -