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dc.contributor.authorGlackin, C.
dc.contributor.authorMaguire, L.
dc.contributor.authorMcDaid, L.
dc.contributor.authorWade, J.
dc.identifier.citationGlackin , C , Maguire , L , McDaid , L & Wade , J 2011 , Lateral inhibitory networks : Synchrony, edge enhancement, and noise reduction . in Procs of the 2011 International Joint Conference on Neural Networks : (IJCNN) . IEEE , pp. 1003-1009 , 2011 Int Joint Conf on Neural Networks IJCNN , San Jose , United States , 31/07/11 .
dc.identifier.otherPURE: 736887
dc.identifier.otherPURE UUID: 7ce7b137-fc7c-4c28-adfd-7f1048c1e769
dc.identifier.otherBibtex: urn:561fd982cc89113036402e4164cddd44
dc.identifier.otherScopus: 80054722113
dc.description.abstractThis paper investigates how layers of spiking neurons can be connected using lateral inhibition in different ways to bring about synchrony, reduce noise, and extract or enhance features. To illustrate the effects of the various connectivity regimes spectro-temporal speech data in the form of isolated digits is employed. The speech samples are preprocessed using the Lyon’s Passive Ear cochlear model, and then encoded into tonotopically arranged spike arrays using the BSA spiker algorithm. The spike arrays are then subjected to various lateral inhibitory connectivity regimes configured by two connectivity parameters, namely connection length and neighbourhood size. The combination of these parameters are demonstrated to produce various effects such as transient synchrony, reduction of noisy spikes, and sharpening of spectrotemporal featuresen
dc.relation.ispartofProcs of the 2011 International Joint Conference on Neural Networks
dc.titleLateral inhibitory networks : Synchrony, edge enhancement, and noise reductionen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionAdaptive Systems
dc.relation.schoolSchool of Computer Science

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