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Title: Generalization of features in the assembly neural networks
Author (s): Goltsev, Alexander.
Wunsch, Donald C.
Department/Lab Affiliations: Applied Computational Intelligence Laboratory
Electrical and Computer Engineering
Keywords: Neural assembly
Neuron
excitatory binary connection
generation
neural column
recognition
sub-network
Issue Date: 2004
Publisher: World Scientific Publishing
Citation: Goltsev, Alexander., and Donald C. Wunsch. "Generalization of Features in the Assembly Neural Networks." International Journal of Neural Systems, 14, (2004).
Abstract: The purpose of the paper is an experimental study of the formation of class descriptions, taking place during learning, in assembly neural networks. The assembly neural network is artificially partitioned into several sub-networks according to the number of classes that the network has to recognize. The features extracted from input data are represented in neural column structures of the sub-networks. Hebbian neural assemblies are formed in the column structure of the sub-networks by weight adaptation. A specific class description is formed in each sub-network of the assembly neural network due to intersections between the neural assemblies. The process of formation of class descriptions in the sub-networks is interpreted as feature generalization. A set of special experiments is performed to study this process, on a task of character recognition using the MNIST database.
Type: Article - Journal
text
In Title: International Journal of Neural Systems
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titleGeneralization of features in the assembly neural networks
contributor.authorGoltsev, Alexander.
contributor.authorWunsch, Donald C.
contributor.deptlabApplied Computational Intelligence Laboratory
contributor.deptlabElectrical and Computer Engineering
subjectNeural assembly
subjectNeuron
subjectexcitatory binary connection
subjectgeneration
subjectneural column
subjectrecognition
subjectsub-network
date.issued2004
publisherWorld Scientific Publishing
identifier.citationGoltsev, Alexander., and Donald C. Wunsch. "Generalization of Features in the Assembly Neural Networks." International Journal of Neural Systems, 14, (2004).
description.abstractThe purpose of the paper is an experimental study of the formation of class descriptions, taking place during learning, in assembly neural networks. The assembly neural network is artificially partitioned into several sub-networks according to the number of classes that the network has to recognize. The features extracted from input data are represented in neural column structures of the sub-networks. Hebbian neural assemblies are formed in the column structure of the sub-networks by weight adaptation. A specific class description is formed in each sub-network of the assembly neural network due to intersections between the neural assemblies. The process of formation of class descriptions in the sub-networks is interpreted as feature generalization. A set of special experiments is performed to study this process, on a task of character recognition using the MNIST database.
typeArticle - Journal
type.DCMITypetext
type.statusFinal version
rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
rights.URI
http://www.worldscinet.com/authors/authorrights.shtml
relation.isPartOfInternational Journal of Neural Systems
date.accessioned2007-04-11T17:00:48Z
date.available2008-03-20T16:27:45Z
identifier.persist.URI
http://scholarsmine.mst.edu/post_prints/GeneralizationofFeaturesintheAssemblyNeuralNet_09007dcc804c0488.html