Performance of reservoir computing in a random network of single-walled carbon nanotubes complexed with polyoxometalate. Issue 1 (24th January 2022)

• Record Type: Journal Article
• Authors: Akai-Kasaya, Megumi; Takeshima, Yuki; Kan, Shaohua; Nakajima, Kohei; Oya, Takahide; Asai, Tetsuya
• Is Part Of: Neuromorphic computing and engineering. Volume 2:Issue 1(2022)
• Publication Date: 2022-01-24
• Subjects: reservoir computing -- carbon nanotube -- polyoxometalate -- random network; Neural networks (Computer science) -- Periodicals; Neural computers -- Periodicals; Neuromorphics -- Periodicals; 006.3
• DOI: 10.1088/2634-4386/ac4339
• Languages: English
• ISSNs: 2634-4386

Abstract

Abstract: Molecular neuromorphic devices are composed of a random and extremely dense network of single-walled carbon nanotubes (SWNTs) complexed with polyoxometalate (POM). Such devices are expected to have the rudimentary ability of reservoir computing (RC), which utilizes signal response dynamics and a certain degree of network complexity. In this study, we performed RC using multiple signals collected from a SWNT/POM random network. The signals showed a nonlinear response with wide diversity originating from the network complexity. The performance of RC was evaluated for various tasks such as waveform reconstruction, a nonlinear autoregressive model, and memory capacity. The obtained results indicated its high capability as a nonlinear dynamical system, capable of information processing incorporated into edge computing in future technologies.