Theory and applications of multifunctional reservoir computers

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.

Grantor dc:publisher

University College Cork

Year dc:date.issued

2023

Author dc:creator

• Flynn, Andrew

Advisor dc:contributor.advisor

• Amann, Andreas

• Nonlinear dynamics
• Reservoir computing
• Machine learning
• Multifunctionality
• Computational neuroscience

Statement dc:rights

• © 2023, Andrew Flynn.

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10468/14980

OAI identifier oai:identifier

oai:cora.ucc.ie:10468/14980