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dc.contributor.authorOros, N.
dc.contributor.authorSteuber, Volker
dc.contributor.authorDavey, N.
dc.contributor.authorCanamero, L.
dc.contributor.authorAdams, R.G.
dc.date.accessioned2008-10-02T14:14:08Z
dc.date.available2008-10-02T14:14:08Z
dc.date.issued2008
dc.identifier.citationOros , N , Steuber , V , Davey , N , Canamero , L & Adams , R G 2008 , ' Adaptive Olfactory Encoding in Agents Controlled by Spiking Neural Networks ' , Lecture Notes in Computer Science (LNCS) , vol. 5040 , no. From Animals to Animats 10 , pp. 148-158 . https://doi.org/10.1007/978-3-540-69134-1_15
dc.identifier.issn0302-9743
dc.identifier.otherdspace: 2299/2422
dc.identifier.otherORCID: /0000-0003-0186-3580/work/133139215
dc.identifier.urihttp://hdl.handle.net/2299/2422
dc.description“The original publication is available at www.springerlink.com”. Copyright Springer
dc.description.abstractWe created a neural architecture that can use two different types of information encoding strategies depending on the environment. The goal of this research was to create a simulated agent that could react to two different overlapping chemicals having varying concentrations. The neural network controls the agent by encoding its sensory information as temporal coincidences in a low concentration environment, and as firing rates at high concentration. With such an architecture, we could study synchronization of firing in a simple manner and see its effect on the agent’s behaviour.en
dc.format.extent638620
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (LNCS)
dc.subjectneural encoding
dc.subjectfiring rate
dc.subjecttemporal coincidence
dc.titleAdaptive Olfactory Encoding in Agents Controlled by Spiking Neural Networksen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionCentre of Data Innovation Research
dc.contributor.institutionCentre for Future Societies Research
dc.description.statusPeer reviewed
rioxxterms.versionofrecord10.1007/978-3-540-69134-1_15
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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