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|Title:||A neural network model for a hierarchical spatio-temporal memory||Authors:||Ramanathan, K.
|Issue Date:||2009||Citation:||Ramanathan, K.,Shi, L.,Li, J.,Lim, K.G.,Li, M.H.,Ang, Z.P.,Chong, T.C. (2009). A neural network model for a hierarchical spatio-temporal memory. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5506 LNCS (PART 1) : 428-435. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-02490-0_53||Abstract:||The architecture of the human cortex is uniform and hierarchical in nature. In this paper, we build upon works on hierarchical classification systems that model the cortex to develop a neural network representation for a hierarchical spatio-temporal memory (HST-M) system. The system implements spatial and temporal processing using neural network architectures. We have tested the algorithms developed against both the MLP and the Hierarchical Temporal Memory algorithms. Our results show definite improvement over MLP and are comparable to the performance of HTM. © 2009 Springer Berlin Heidelberg.||Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||URI:||http://scholarbank.nus.edu.sg/handle/10635/68908||ISBN:||3642024890||ISSN:||03029743||DOI:||10.1007/978-3-642-02490-0_53|
|Appears in Collections:||Staff Publications|
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