A model for self-organization in WTA networks and its application to map prediction problems

Author

Lemmon, Michael ; Kumar, B. V K Vijaya

Author_Institution

Dept. of Electr. & Comput. Eng., Carnegie-Mellon Univ., Pittsburgh, PA, USA

fYear

1989

fDate

0-0 1989

Firstpage

509

Abstract

A mathematical model for long-term memory (LTM) reorganization in self-organizing winner-take-all (WTA) networks is developed. The model describes the temporal evolution of the density of neural LTM states using a diffusive partial differential equation. Solutions to this equation show that, in the long run, LTM states tend to cluster about the modes of the stimulating source´s probability density function. This behavior is precisely what is required by many engineering problems involving maximum a posteriori (MAP) prediction. The connection between self-organizing WTA networks and MAP prediction is discussed. A simulated example demonstrating this connection is provided.<>

Keywords

adaptive systems; neural nets; partial differential equations; MAP prediction; WTA networks; diffusive partial differential equation; long-term memory; map prediction problems; mathematical model; maximum a posteriori; neural LTM states; probability density function; self-organization; temporal evolution; winner-take-all; Adaptive systems; Neural networks; Partial differential equations;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1989. IJCNN., International Joint Conference on

Conference_Location

Washington, DC, USA

Type

conf

DOI

10.1109/IJCNN.1989.118291

Filename

118291