Benchmarking Nonlinear Readouts in Linear Reservoir Networks

Recent theoretical advances have demonstrated the universality of linear Reservoir Computing (RC) models equipped with nonlinear readouts, showing their potential to approximate arbitrary input-output mappings. However, practical insights into the selection and performance of nonlinear readouts are limited. This paper addresses this gap by systematically benchmarking a spectrum of nonlinear readouts within linear RC frameworks. Our results reveal the practical trade-offs in accuracy and efficiency across tasks, offering insights on how to train performant RC systems with linear recurrence. These findings provide valuable guidelines for designing efficient recurrent architectures that combine theoretical guarantees with state-of-the-art performance in sequential data processing.