Dynamics of adaptive recurrent neural networks

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dc.contributor.advisorAmann, Andreas
dc.contributor.advisorKeane, Andrew
dc.contributor.authorFox, Daviden
dc.contributor.funderUniversity College Cork
dc.date.accessioned2024-10-03T14:19:29Z
dc.date.available2024-10-03T14:19:29Z
dc.date.issued2023
dc.date.submitted2023
dc.description.abstractIn this thesis a simple, phenomenological model of a neural network with plasticity is presented in the form of a slow-fast adaptive dynamical recurrent neural network. The plasticity rule is chosen from the class of Hebbian learning rules, in which the synaptic connection between two neurons evolves continuously as a function of their correlation in the recent past. Initially an analysis of networks of two neurons is presented, which exhibit relaxation oscillations in which one neuron switches between an ’off’ state, where it takes a negative value, and an ’on’ state, where it takes a positive value, while the other neuron stays in one on/off state. Then, by means of an example with a nine neuron network, the system is shown to exhibit both stable frequency cluster synchronization and transient frequency cluster synchronization.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationFox, D. 2023. Dynamics of adaptive recurrent neural networks. MSc Thesis, University College Cork.
dc.identifier.endpage69
dc.identifier.urihttps://hdl.handle.net/10468/16503
dc.language.isoenen
dc.publisherUniversity College Corken
dc.relation.projectUniversity College Cork (School of Mathematical Sciences)
dc.rights© 2023, David Fox.
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectNonlinear dynamics
dc.subjectBifurcation theory
dc.subjectDynamical systems
dc.subjectComputaional neuroscience
dc.subjectNeural networks
dc.subjectRecurrent neural networks
dc.subjectSynchronization
dc.subjectSlow-fast systems
dc.subjectMultiple timescales
dc.subjectAdaptive dynamical networks
dc.titleDynamics of adaptive recurrent neural networks
dc.typeMasters thesis (Research)en
dc.type.qualificationlevelMastersen
dc.type.qualificationnameMSc - Master of Scienceen
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