In‐Materio Reservoir Computing in a Sulfonated Polyaniline Network

Abstract

A sulfonated polyaniline (SPAN) organic electrochemical network device (OEND) is fabricated using a simple drop‐casting method on multiple Au electrodes for use in reservoir computing (RC). The SPAN network has humidity‐dependent electrical properties. Under high humidity, the SPAN OEND exhibits mainly ionic conduction, including charging of an electric double layer and ionic diffusion. The nonlinearity and hysteresis of the current–voltage characteristics progressively increase with increasing humidity. The rich dynamic output behavior indicates wide variations for each electrode, which improves the RC performance because of the disordered network. For RC, waveform generation and short‐term memory tasks are realized by a linear combination of outputs. The waveform task accuracy and memory capacity calculated from a short‐term memory task reach 90% and 33.9, respectively. Improved spoken‐digit classification is realized with 60% accuracy by only 12 outputs, demonstrating that the SPAN OEND can manage time series dynamic data operation in RC owing to a combination of rich dynamic and nonlinear electronic properties. The results suggest that SPAN‐based electrochemical systems can be applied for material‐based computing, by exploiting their intrinsic physicochemical behavior.

In-materio computing in random networks of carbon nanotubes complexed with chemically dynamic molecules: a review

“…Although many such hardware NN models [50] have been studied, RC is novel and has only recently attracted significant attention (figure 16(b)) [51][52][53][54] owing to its straightforward framework for processing time-series data. The execution of RC learning for time-series prediction tasks has been applied to atomic switch networks (ASNs) [55][56][57], memristor networks [58], CNT/polymer composites [59,60], NP aggregation [57], polymer network systems [61], [36] with permission from the Royal Society of Chemistry.) optoelectronic systems [62,63], soft bodies [64,65], spintronics [4,66], and water-tank systems [67].…”

In Materia Should Be Used Instead of In Materio

“…More than 10 years later, Miller recalled and updated the in materio expression, as connected to the implementation of computational paradigms on physical systems as black boxes (Miller et al, 2014). Soon after, in materio started being associated especially with physical reservoir computing implemented on designless nanonetworks (van Damme et al, 2016;Dale et al, 2017aDale et al, , 2017bPrzyczyna et al, 2020;Banerjee et al, 2021;Boon et al, 2021;Kotooka et al, 2021;Lilak et al, 2021;Usami et al, 2021). Very recently, the same meaning was attributed to a slightly different Latin expression: in materia (Milano et al, 2021).…”

Emergence of In‐Materio Intelligence from an Incidental Structure of a Single‐Walled Carbon Nanotube–Porphyrin Polyoxometalate Random Network

“…Many such hardware-implemented NN models are being researched [6,7] ; however, the one that has recently been attracting attention is directed towards the emulation of reservoir computing (RC) [8][9][10][11] (Figure S1, Supporting Information) because of its bio-inspired and straightforward framework for processing time-series data. Execution of such temporal RC machine has been accomplished in atomic switch networks (ASNs), [12] conductive polymer network, [13] carbon nanotube (CNT)/polymer composites, [14,15] optoelectrical systems, [16,17] soft bodies, [18,19] spintronics, [20,21] and water tank systems. [22] The productivity of each of these materials towards RC relies on their intrinsic reservoir property [10,11] of recurrent nonlinear high-dimensional dynamics analogous to the human brain.…”