title: Delay-based reservoir computing: noise effects in a combined analog and digital implementation.
abstract: Reservoir computing is a paradigm in machine learning whose processing capabilities rely on the dynamical behavior of recurrent neural networks. We present a mixed analog and digital implementation of this concept with a nonlinear analog electronic circuit as a main computational unit. In our approach, the reservoir network can be replaced by a single nonlinear element with delay via time-multiplexing. We analyze the influence of noise on the performance of the system for two benchmark tasks: 1) a classification problem and 2) a chaotic time-series prediction task. Special attention is given to the role of quantization noise, which is studied by varying the resolution in the conversion interface between the analog and digital worlds.
authors.forename: Miguel C
authors.initials: MC
authors.lastname: Soriano
authors.forename: Silvia
authors.initials: S
authors.lastname: Ortín
authors.forename: Lars
authors.initials: L
authors.lastname: Keuninckx
authors.forename: Lennert
authors.initials: L
authors.lastname: Appeltant
authors.forename: Jan
authors.initials: J
authors.lastname: Danckaert
authors.forename: Luis
authors.initials: L
authors.lastname: Pesquera
authors.forename: Guy
authors.initials: G
authors.lastname: van der Sande
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics