no code implementations • 29 Nov 2022 • Joseph D. Hart, Francesco Sorrentino, Thomas L. Carroll
Reservoir computing is a recurrent neural network paradigm in which only the output layer is trained.
no code implementations • 3 May 2022 • Thomas L. Carroll, Joseph D. Hart
Additionally, the need to create and connect large numbers of nonlinear nodes makes it difficult to design and build analog reservoir computers that can be faster and consume less power than digital reservoir computers.
no code implementations • 29 Oct 2020 • Amitava Banerjee, Joseph D. Hart, Rajarshi Roy, Edward Ott
To achieve this, we first train a type of machine learning system known as reservoir computing to mimic the dynamics of the unknown network.
1 code implementation • 18 Nov 2019 • Yuanzhao Zhang, Zachary G. Nicolaou, Joseph D. Hart, Rajarshi Roy, Adilson E. Motter
We report on a new type of chimera state that attracts almost all initial conditions and exhibits power-law switching behavior in networks of coupled oscillators.
Disordered Systems and Neural Networks Dynamical Systems Adaptation and Self-Organizing Systems Chaotic Dynamics Pattern Formation and Solitons
1 code implementation • 8 Feb 2019 • Joseph D. Hart, Yuanzhao Zhang, Rajarshi Roy, Adilson E. Motter
Symmetries are ubiquitous in network systems and have profound impacts on the observable dynamics.
Adaptation and Self-Organizing Systems Disordered Systems and Neural Networks Chaotic Dynamics Pattern Formation and Solitons