Open AccessPhysical Implementation of Reservoir Computing through Electrochemical Reaction
Author(s) -
Kan Shaohua,
Nakajima Kohei,
Asai Tetsuya,
AkaiKasaya Megumi
Publication year - 2022
Publication title -
advanced science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.388
H-Index - 100
ISSN - 2198-3844
DOI - 10.1002/advs.202104076
Subject(s) - nonlinear system , computer science , reservoir computing , electrochemistry , signal (programming language) , distributed computing , biological system , electrode , chemistry , artificial neural network , artificial intelligence , physics , quantum mechanics , recurrent neural network , biology , programming language
Nonlinear dynamical systems serving reservoir computing enrich the physical implementation of computing systems. A method for building physical reservoirs from electrochemical reactions is provided, and the potential of chemical dynamics as computing resources is shown. The essence of signal processing in such systems includes various degrees of ionic currents which pass through the solution as well as the electrochemical current detected based on a multiway data acquisition system to achieve switchable and parallel testing. The results show that they have respective advantages in periodic signals and temporal dynamic signals. Polyoxometalate molecule in the solution increases the diversity of the response current and thus improves their abilities to predict periodic signals. Conversely, distilled water exhibits great computing power in solving a second‐order nonlinear problem. It is expected that these results will lead to further exploration of ionic conductance as a nonlinear dynamical system and provide more support for novel devices as computing resources.