Cellar automata models for reservoir computing in single-walled carbon nanotube network complexed with polyoxometalate

Abstract

Molecular neuromorphic devices are composed of random and extremely dense networks of single-walled carbon nanotubes (SWNTs) complexed with polyoxometalate (POM). Such devices are expected to possess the rudimentary ability of reservoir computing (RC), which utilizes the signal-response dynamics and a certain degree of network complexity. The potential of non-linear dynamic systems to serve as reservoirs has attracted considerable attention for the physical realization of RC. In this study, three theoretical approaches are introduced for the physical component of a reservoir with dynamic response and nonlinearity. The first is the cellar automata model for the random network of the model, second is a hardware system working as a reservoir with one simple form of nonlinearity that reflects the intrinsic characteristics of the materials, and third is a large simulation model that includes the negative differential resistance of the POM. Although the two cell-ligation models incorporated different molecular properties, they both exhibited excellent reservoir properties. Furthermore, a simple nonlinear system was driven as a reservoir and demonstrated excellent performance in speech recognition and other functions. Our results are expected to facilitate the development of material-based RC devices.