"Stochastic reservoir computers" (2025) <a href="https://www.nature.com/articles/s41467-025-58349-6" rel="nofollow">https://www.nature.com/articles/s41467-025-58349-6</a> :<p>> Abstract: [...] <i>This allows the number of readouts to scale exponentially with the size of the reservoir hardware, offering the advantage of compact device size.</i> We prove that classes of stochastic echo state networks form universal approximating classes. <i>We also investigate the performance of two practical examples in classification and chaotic time series prediction. While shot noise is a limiting factor, we show significantly improved performance compared to a deterministic reservoir computer with similar hardware when noise effects are small.</i>