A neural network model for a hierarchical spatio-temporal memory

• Main Authors: RAMANATHAN, Kiruthika, SHI, Luping, LI, Jianming, LIM, Kian Guan, ANG, Zhi Ping, TOW, Chong Chong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2008
• Subjects: Databases and Information Systems; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/7392; https://ink.library.smu.edu.sg/context/sis_research/article/8395/viewcontent/LNCS_5506___Advances_in_Neuro_Information_Processing.pdf
• Institution: Singapore Management University

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.