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oai:cora.ucc.ie:10468/165032024-10-04T02:03:45Zcom_10468_388com_10468_5com_10468_88com_10468_1col_10468_9859col_10468_9978col_10468_8983

Amann, Andreas Keane, Andrew Fox, David University College Cork 2024-10-03T14:19:29Z 2024-10-03T14:19:29Z 2023 2023 In 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. Not peer reviewed Accepted Version application/pdf Fox, D. 2023. Dynamics of adaptive recurrent neural networks. MSc Thesis, University College Cork. 69 https://hdl.handle.net/10468/16503 en University College Cork University College Cork (School of Mathematical Sciences) © 2023, David Fox. https://creativecommons.org/licenses/by-nc-nd/4.0/ Nonlinear dynamics Bifurcation theory Dynamical systems Computaional neuroscience Neural networks Recurrent neural networks Synchronization Slow-fast systems Multiple timescales Adaptive dynamical networks Dynamics of adaptive recurrent neural networks Masters thesis (Research) Masters MSc - Master of Scienceinfo:eu-repo/semantics/openAccess