1 citations · 1 across the 4 of their papers we have counts for
6 papers
Confabulation dynamics in a reservoir computer: Filling in the gaps with untrained attractors
Jack O'Hagan, Andrew Keane, Andrew Flynn
Artificial Intelligence has advanced significantly in recent years thanks to innovations in the design and training of artificial neural networks (ANNs). Despite these advancements…
Exploring the origins of switching dynamics in a multifunctional reservoir computer
Andrew Flynn, Andreas Amann
The concept of multifunctionality has enabled reservoir computers (RCs), a type of dynamical system that is typically realised as an artificial neural network, to reconstruct multi…
Multifunctionality in a Connectome-Based Reservoir Computer
Jacob Morra, Andrew Flynn, Andreas Amann +1
Multifunctionality describes the capacity for a neural network to perform multiple mutually exclusive tasks without altering its network connections; and is an emerging area of int…
Seeing double with a multifunctional reservoir computer
Andrew Flynn, Vassilios A. Tsachouridis, Andreas Amann
Multifunctional biological neural networks exploit multistability in order to perform multiple tasks without changing any network properties. Enabling artificial neural networks (A…
Exploring the limits of multifunctionality across different reservoir computers
Andrew Flynn, Oliver Heilmann, Daniel Köglmayr +3
Multifunctional neural networks are capable of performing more than one task without changing any network connections. In this paper we explore the performance of a continuous-time…
Multifunctionality in a Reservoir Computer
Andrew Flynn, Vassilios A. Tsachouridis, Andreas Amann
Multifunctionality is a well observed phenomenological feature of biological neural networks and considered to be of fundamental importance to the survival of certain species over…