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dc.contributor.authorKasabov, N
dc.contributor.authorSchliebs, S
dc.contributor.authorMohemmed, A
dc.date.accessioned2011-08-09T03:15:24Z
dc.date.available2011-08-09T03:15:24Z
dc.date.copyright2011-06-30
dc.date.issued2011-08-09
dc.identifier.citation8th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Gargnano-Lago di Garda, Italy, 2011-06-30 - 2011-07-02
dc.identifier.urihttp://hdl.handle.net/10292/1663
dc.description.abstractComputational neuro-genetic models (CNGM) combine two dynamic models – a gene regulatory network (GRN) model at a lower level, and a spiking neural network (SNN) model at a higher level to model the dynamic interaction between genes and spiking patterns of activity under certain conditions. The paper demonstrates that it is possible to model and trace over time the effect of a gene on the total spiking behavior of the SNN when the gene controls a parameter of a stochastic spiking neuron model used to build the SNN. Such CNGM can be potentially used to study neurodegenerative diseases or develop CNGM for cognitive robotics. 1
dc.publisherAUT University
dc.subjectComputational neurogenetic modeling; Spiking neural networks; Gene regulatory networks; Probabilistic neural models
dc.titleModelling the effect of genes on the dynamics of probabilistic spiking neural networks for computational neurogenetic modelling
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess


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