We show that many delay-based reservoir computers considered in the literature can be characterized by a universal master memory function (MMF). Once computed for two independent parameters, this function provides linear memory capacity for any delay-based single-variable reservoir with small inputs. Moreover, we propose an analytical description of the MMF that enables its efficient and fast computation. Our approach can be applied not only to single-variable delay-based reservoirs governed by known dynamical rules, such as the Mackey–Glass or Stuart–Landau-like systems, but also to reservoirs whose dynamical model is not available
We study the role of the system response time in the computational capacity of delay-based reservoir...
Delayed feedback systems are known to exhibit a rich dynamical behavior, showing a wide variety of d...
International audienceReservoir computing is a recently introduced machine learning paradigm that ha...
Reservoir computing is a recently introduced brain-inspired machine learning paradigm. We focus on t...
Reservoir computing is a machine learning method that uses the response of a dynamical system to a c...
Reservoir computing is a machine learning method that solves tasks using the response of a dynamical...
Delays are ubiquitous in biological systems, ranging from genetic regulatory networks and synaptic c...
Delays are ubiquitous in biological systems, ranging from genetic regulatory networks and synaptic c...
© 2015 Massachusetts Institute of Technology. Supplementing a differential equation with delays resu...
2021 International Joint Conference on Neural Networks (IJCNN, 18-22 July 2021).Delay-based reservoi...
Reservoir computing (RC) has attracted a lot of attention in the field of machine learning because o...
The reservoir computing scheme is a machine learning mechanism which utilizes the naturally occurrin...
The recent progress in artificial intelligence has spurred renewed interest in hardware implementati...
Physical dynamical systems are able to process information in a nontrivial manner. The machine learn...
© 2015 Soriano, Brunner, Escalona-Morán, Mirasso and Fischer. To learn and mimic how the brain proce...
We study the role of the system response time in the computational capacity of delay-based reservoir...
Delayed feedback systems are known to exhibit a rich dynamical behavior, showing a wide variety of d...
International audienceReservoir computing is a recently introduced machine learning paradigm that ha...
Reservoir computing is a recently introduced brain-inspired machine learning paradigm. We focus on t...
Reservoir computing is a machine learning method that uses the response of a dynamical system to a c...
Reservoir computing is a machine learning method that solves tasks using the response of a dynamical...
Delays are ubiquitous in biological systems, ranging from genetic regulatory networks and synaptic c...
Delays are ubiquitous in biological systems, ranging from genetic regulatory networks and synaptic c...
© 2015 Massachusetts Institute of Technology. Supplementing a differential equation with delays resu...
2021 International Joint Conference on Neural Networks (IJCNN, 18-22 July 2021).Delay-based reservoi...
Reservoir computing (RC) has attracted a lot of attention in the field of machine learning because o...
The reservoir computing scheme is a machine learning mechanism which utilizes the naturally occurrin...
The recent progress in artificial intelligence has spurred renewed interest in hardware implementati...
Physical dynamical systems are able to process information in a nontrivial manner. The machine learn...
© 2015 Soriano, Brunner, Escalona-Morán, Mirasso and Fischer. To learn and mimic how the brain proce...
We study the role of the system response time in the computational capacity of delay-based reservoir...
Delayed feedback systems are known to exhibit a rich dynamical behavior, showing a wide variety of d...
International audienceReservoir computing is a recently introduced machine learning paradigm that ha...