A neural network model for a hierarchical spatio-temporal memory

Authors:
Kiruthika Ramanathan;Luping Shi;Jianming Li;Kian Guan Lim;Ming Hui Li;Zhi Ping Ang;Tow Chong Chong
Affiliations:
Data Storage Institute, Agency for Science, Technology and Research, Singapore;Data Storage Institute, Agency for Science, Technology and Research, Singapore;Data Storage Institute, Agency for Science, Technology and Research, Singapore;Data Storage Institute, Agency for Science, Technology and Research, Singapore;Dept of Electrical and Computer Engineering, National University of Singapore;Dept of Electrical and Computer Engineering, National University of Singapore;Data Storage Institute, Agency for Science, Technology and Research, Singapore and Dept of Electrical and Computer Engineering, National University of Singapore
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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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.