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Simple Reservoir Computing Capitalizing on the Nonlinear Response of Materials: Theory and Physical Implementations

Shaohua Kan, Kohei Nakajima, Yuki Takeshima, Tetsuya Asai, Yuji Kuwahara, Megumi Akai‐Kasaya

2021Physical Review Applied64 citationsDOIOpen Access PDF

Abstract

The potential of nonlinear dynamical systems serving as reservoirs has attracted much attention for the physical realization of reservoir computing (RC). Here, we propose a hardware system working as a reservoir with one simple form of nonlinearity that reflects the intrinsic characteristics of the materials. We show that insufficient dynamics in such physical systems can perform like complex dynamical systems with the assistance of external controls. Based on the idea of spatial multiplexing, this dynamical system is studied under two frameworks. The correlation between structural adjustments of the reservoir and system performance in processing various types of task is proposed. Our results are expected to enable the development of material-based devices for RC.

Topics & Concepts

Reservoir computingRealization (probability)Computer scienceNonlinear systemImplementationSimple (philosophy)Physical systemTask (project management)Dynamical systems theoryMultiplexingDistributed computingComplex systemArtificial intelligenceSystems engineeringPhysicsMathematicsEngineeringTelecommunicationsArtificial neural networkQuantum mechanicsStatisticsPhilosophyProgramming languageRecurrent neural networkEpistemologyNeural Networks and Reservoir ComputingAdvanced Memory and Neural ComputingNeural dynamics and brain function
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