IEEE Task Force on Reservoir Computing

Welcome to the Task Force on Reservoir Computing (TFRC)

This task force has been established in 2018 under the Data Mining and Big Data Analytics Technical Committee (DMTC) of the Computational Intelligence Society (CIS) of the Institute of Electrical and Electronic Engineers, Inc. (IEEE).

Reservoir Computing is a common denomination for a class of dynamical Recurrent Neural Networks featured by untrained dynamics, including (among the others) Echo State Networks, Liquid State Machines, and Fractal Prediction Machines. As such, Reservoir Computing represents a de facto state-of-the-art paradigm for efficient learning in time series domains.

The goal of this task force is to promote and stimulate the development of Reservoir Computing research under both theoretical and application perspectives.

Current Chairs

(Chair)

University of Tokyo, Japan

k-nakajima [at] isi.imi.i.u-tokyo.ac.jp 

(Vice-Chair)

University of Tokyo, Japan

gouhei [at] sat.t.u-tokyo.ac.jp

(Vice-Chair)

INRIA, Bordeaux, France 

xavier.hinaut [at] inria.fr

 (Vice-Chair)

German Research Center for Artificial Intelligence (DFKI)

benjamin.paassen [at] hu-berlin.de

Founders and Previous Chairs (2018-2022)

Department of Computer Science, University of Pisa

gallicch [at] di.unipi.it

Department of Computer Science, University of Pisa

micheli [at] di.unipi.it