Reservoir Computing Generalized

• URL: http://arxiv.org/abs/2412.12104v1
• Date: Sat, 23 Nov 2024 05:02:47 GMT
• Title: Reservoir Computing Generalized
• Authors: Tomoyuki Kubota, Yusuke Imai, Sumito Tsunegi, Kohei Nakajima,
• Abstract summary: A physical neural network (PNN) has the strong potential to solve machine learning tasks and physical properties, such as high-speed computation and energy efficiency. Reservoir computing (RC) is an excellent framework for implementing an information processing system with a dynamical system. We propose a novel framework called reservoir computing (GRC) by turning this requirement on its head, making conventional RC a special case.
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• Abstract: A physical neural network (PNN) has both the strong potential to solve machine learning tasks and intrinsic physical properties, such as high-speed computation and energy efficiency. Reservoir computing (RC) is an excellent framework for implementing an information processing system with a dynamical system by attaching a trained readout, thus accelerating the wide use of unconventional materials for a PNN. However, RC requires the dynamics to reproducibly respond to input sequence, which limits the type of substance available for building information processors. Here we propose a novel framework called generalized reservoir computing (GRC) by turning this requirement on its head, making conventional RC a special case. Using substances that do not respond the same to identical inputs (e.g., a real spin-torque oscillator), we propose mechanisms aimed at obtaining a reliable output and show that processed inputs in the unconventional substance are retrievable. Finally, we demonstrate that, based on our framework, spatiotemporal chaos, which is thought to be unusable as a computational resource, can be used to emulate complex nonlinear dynamics, including large scale spatiotemporal chaos. Overall, our framework removes the limitation to building an information processing device and opens a path to constructing a computational system using a wider variety of physical dynamics.

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