Self-organized function localization neural network

Takafumi Sasakawa, Jinglu Hu, Kotaro Hirasawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


This paper presents a self-organizing function localization neural network (FLNN) inspired by Hebb's cell assembly theory about how the brain worked. The proposed self-organizing FLNN consists of two parts: main part and control part. The main part is an ordinary 3-layered feedforward neural network, but each hidden neuron contains a signal from the control part, controlling its firing strength. The control part consists of a SOM network whose outputs are associated with the hidden neurons of the main part. Trained with an unsupervised learning, SOM control part extracts structural features of input-output spaces and controls the firing strength of hidden neurons in the main part. Such self-organizing FLNN realizes capabilities of function localization and learning. Numerical simulations show that the self-organizing FLNN has superior performance than an ordinary neural network.

Original languageEnglish
Title of host publication2004 IEEE International Joint Conference on Neural Networks - Proceedings
Number of pages6
Publication statusPublished - 2004 Dec 1
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 2004 Jul 252004 Jul 29

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576


Conference2004 IEEE International Joint Conference on Neural Networks - Proceedings

ASJC Scopus subject areas

  • Software


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