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oai:cora.ucc.ie:10468/149802023-09-15T02:03:34Zcom_10468_388com_10468_5com_10468_88com_10468_1col_10468_389col_10468_859col_10468_8983

Amann, Andreas Tsachouridis, Vassilios A. Flynn, Andrew Irish Research Council 2023-09-14T10:41:02Z 2023-09-14T10:41:02Z 2023 2023 In the pursuit of developing artificially intelligent systems there is much to be gained from dually integrating further physiological features of biological neural networks and knowledge of dynamical systems into machine learning environments. In this Thesis such a two-armed approach is employed in order to translate 'multifunctionality' from biological to artificial neural networks via the reservoir computing machine learning paradigm. Multifunctionality describes the ability of a single neural network that exploits a form of multistability to perform a multitude of mutually exclusive tasks. The dynamics of multifunctional RCs are assessed across several tasks and from this many new application areas are explored which include, data-driven modelling of multistability, generating chaotic itinerancy, and reconstructing dynamical transitions present in the epileptic brain. Not peer reviewed Accepted Version application/pdf Flynn, A. 2023. Theory and applications of multifunctional reservoir computers. PhD Thesis, University College Cork. 240 https://hdl.handle.net/10468/14980 en University College Cork Irish Research Council (Grant EPSPG/2017/301) © 2023, Andrew Flynn. https://creativecommons.org/licenses/by-nc-nd/4.0/ Nonlinear dynamics Reservoir computing Machine learning Multifunctionality Computational neuroscience Theory and applications of multifunctional reservoir computers Doctoral thesis Doctoral PhD - Doctor of Philosophyinfo:eu-repo/semantics/openAccess