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

Additional URL

https://doi.org/10.1007/978-3-642-02490-0_53

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