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An application of brain-inspired computing architecture to time series prediction tasks

Authors
Journal
International Congress Series
0531-5131
Publisher
Elsevier
Publication Date
Volume
1269
Identifiers
DOI: 10.1016/j.ics.2004.05.134
Keywords
  • Bi-Directional Computing Architecture
  • Time Series Prediction
  • Future Prediction
  • Past Prediction
  • Information Integration
Disciplines
  • Biology
  • Computer Science

Abstract

Abstract In general, a biological nervous system consists of two major parts, i.e., a motor control subsystem and a sensory reception subsystem, and they work together cooperatively as direct-inverse transformation. Paying attention to this bi-directional computing manner, it is essential to perform various kinds of complex activities. Also, it must be important in most application-oriented tasks. In order to confirm its effectiveness, a bi-directional computing architecture inspired from above-mentioned scheme is constructed and applied to time series prediction tasks. At that time, one subsystem in the bi-directional model is assigned to ordinary future prediction (present→future), while the other is to past prediction (present→past) newly introduced in this study. As a result of computer simulations, it is clear that the proposed bi-directional computing architecture shows better performance than the conventional uni-directional one.

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