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dc.contributor.authorKasabov, N
dc.contributor.authorBenuskova, L.
dc.contributor.authorWysoski, S.
dc.date.accessioned2009-05-27T22:18:51Z
dc.date.available2009-05-27T22:18:51Z
dc.date.copyright2005
dc.date.created2005
dc.date.issued2009-05-27T22:18:51Z
dc.identifier.urihttp://hdl.handle.net/10292/601
dc.description.abstractThe paper presents a novel, biologically plausible spiking neuronal model that includes a dynamic gene network. Interactions of genes in neurons affect the dynamics of the neurons and the whole network through neuronal parameters that change as a function of gene expression. The proposed model is used to build a spiking neural network (SNN) illustrated on a real EEC data case study problem. The paper also presents a novel computational approach to brain neural network modeling that integrates dynamic gene networks with a neural network model. Interaction of genes in neurons affects the dynamics of the whole neural network through neuronal parameters, which are no longer constant, but change as a function of gene expression. Through optimization of the gene interaction network, initial gene/protein expression values and ANN parameters, particular target states of the neural network operation can be achieved, and statistics about gene intercation matrix can be extracted. It is illustrated by means of a simple neurogenetic model of a spiking neural network (SNN). The behavior of SNN is evaluated by means of the local field potential, thus making it possible to attempt modeling the role of genes in different brain states, where EEC data is available to test the model. We use standard signal processing techniques like FFT to evaluate the SNN output to compare it with real human EEC data. © 2005 IEEE.
dc.publisherIEEE
dc.rights©2005 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.sourceInternational Joint Conference on Neural Networks, 1, 446-451
dc.titleA computational neurogenetic model of a spiking neuron
dc.typeConference Proceedings
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1109/IJCNN.2005.1555872


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