Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-02490-0_53
Title: A neural network model for a hierarchical spatio-temporal memory
Authors: Ramanathan, K.
Shi, L.
Li, J.
Lim, K.G.
Li, M.H.
Ang, Z.P.
Chong, T.C. 
Issue Date: 2009
Source: 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

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

1
checked on Dec 5, 2017

Page view(s)

40
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.