Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2008
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.
Discipline
Databases and Information Systems | OS and Networks
Research Areas
Data Science and Engineering
Publication
Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28: Proceedings
Volume
5506
First Page
428
Last Page
435
ISBN
9783642024894
Identifier
10.1007/978-3-642-02490-0_53
Publisher
Springer
City or Country
Cham
Citation
RAMANATHAN, Kiruthika; SHI, Luping; LI, Jianming; LIM, Kian Guan; ANG, Zhi Ping; and TOW, Chong Chong.
A neural network model for a hierarchical spatio-temporal memory. (2008). Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28: Proceedings. 5506, 428-435.
Available at: https://ink.library.smu.edu.sg/sis_research/7392
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-642-02490-0_53