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dc.contributor.authorAdams, Roderick
dc.contributor.authorte Boekhorst, Rene
dc.contributor.authorRust, A.G.
dc.contributor.authorKaye, Paul H.
dc.contributor.authorSchilstra, M.
dc.date.accessioned2011-08-09T11:01:07Z
dc.date.available2011-08-09T11:01:07Z
dc.date.issued2004
dc.identifier.citationAdams , R , te Boekhorst , R , Rust , A G , Kaye , P H & Schilstra , M 2004 , Design of spatially extended neural networks for specific applications . in Proceedings of the International Joint Conference on Neural Networks . vol. 4 , IEEE , pp. 3101-3106 .
dc.identifier.otherPURE: 304217
dc.identifier.otherPURE UUID: 85e95794-8caa-41b9-8866-72fe9452c7f7
dc.identifier.otherScopus: 10944224637
dc.identifier.otherORCID: /0000-0001-6950-4870/work/32372032
dc.identifier.urihttp://hdl.handle.net/2299/6113
dc.description“This 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." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”
dc.description.abstractThe processes and mechanisms of biological neural development provide many powerful insights for the creation of artificial neural systems. Biological neural systems are, in general, much more effective in carrying out tasks such as face recognition and motion detection than artificial neural networks. An important difference between biological and (most) artificial neurons is that biological neurons have extensive treeshaped neurites (axons and dendrites) that are themselves capable of active signal transduction and integration. In this paper we present a model, inspired by the processes of neural development, which leads to the growth and formation of neuron-to-neuron connections. The neural architectures created have treeshaped neurites and contain spatial information on branch and synapse positions. Furthermore, we have prototyped a simple but efficient way of simulating signal transduction along neurites using a finite state automaton (FSA). We expect that the combination of our neuronal development method with the FSA that mimics signal transfer, will provide an efficient and effective tool for exploring the relationship between neural form and network function.en
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofProceedings of the International Joint Conference on Neural Networks
dc.rightsOpen
dc.titleDesign of spatially extended neural networks for specific applicationsen
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionParticle Instruments and diagnostics
dc.description.versiontypeFinal Accepted Version
dcterms.dateAccepted2004
rioxxterms.versionAM
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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