Reservoir computing is a promising machine learning-based approach for the analysis of data that changes over time, such as weather patterns, recorded speech or stock market trends. Classical reservoir computing techniques are known to perform best at the "edge of chaos," or in simpler terms, at a "sweet spot" in which the behavior of systems is neither entirely predictable (i.e., order) nor completely unpredictable (i.e., chaos).