An extended model of the neocognitron for pattern partitioning and pattern composition

Author

Wang, Deliang

Author_Institution

Dept. of Comput. Sci., Univ. of Southern California, Los Angeles, CA, USA

fYear

1989

fDate

0-0 1989

Firstpage

267

Abstract

Two neuron growth rules are proposed: the neuron splitting rule and the neuron formation rule for self-organization of adaptive neural networks. Using these two rules the author extends the model of the neocognitron so that when it is presented with part of a stored pattern or with a composite image consisting of many stored patterns, the pattern will be recognized properly. The extended model does pattern partitioning when it recognizes that the input image is a part of a stored pattern, and pattern composition or association when many stored patterns are presented at the same time. This extended neocognitron is demonstrated by computer simulation. The author suggests that the proposed mechanism is a plausible model for child cognitive development and useful for visual pattern recognition.<>

Keywords

brain models; digital simulation; learning systems; neural nets; pattern recognition; self-adjusting systems; adaptive neural networks; brain models; child cognitive development; learning systems; neocognitron; neuron formation rule; neuron growth rules; neuron splitting rule; pattern composition; pattern partitioning; self adjusting systems; self-organization; visual pattern recognition; Brain modeling; Learning systems; Neural networks; Pattern recognition; Simulation;

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.1989.118709

Filename

118709