RCbench: a unified framework for benchmarking reservoir computing systems

Reservoir computing (RC) is a computational framework where a fixed dynamical reservoir projects an input into a higher-dimensional state that is then analyzed by a readout, which is trained to map the reservoir state into the desired output. While the conventional RC paradigm is based on dynamics of in-silico implemented recurrent neural networks, this computing paradigm can be efficiently implemented in hardware by exploiting dynamics of a wide range of physical systems in a paradigm denoted as Physical RC (PRC), attracting interest from a broader research community spanning from computer scientists to physicists, and material scientists. Here, we present RCbench, an open-source RC benchmark toolkit that implements a standardized and comprehensive suite for benchmarking computational reservoir models and physical implementations of RC. RCbench integrates widely recognized metrics such as Memory capacity, Nonlinear autoregressive moving average of order N, Kernel rank , and generalization rank, along with nonlinear transformation tasks. It also allows testing and comparing different readout algorithms, the evaluation of computational capabilities with diverse accuracy metrics, and includes feature selection methods to unravel the effect of specific reservoir outputs on computational performances. In particular, the toolkit enables easy benchmarking of PRC systems, providing a comprehensive benchmark tool that can be easily integrated with experimental data acquisition processes. By standardizing performance assessments, RCbench aims to facilitate inter-study comparisons and to accelerate the exploration, characterization and optimization of RC systems.