Title :
An extended model of the neocognitron for pattern partitioning and pattern composition
Author_Institution :
Dept. of Comput. Sci., Univ. of Southern California, Los Angeles, CA, USA
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;
Conference_Titel :
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location :
Washington, DC, USA
DOI :
10.1109/IJCNN.1989.118709
