A neural network model for a hierarchical spatio-temporal memory
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 spati...
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| Main Authors: | , , , , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2008
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| Subjects: | |
| 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 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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