Abstract
The identification of unconventional physical systems for neuroinspired information processing comes with much-desired capabilities for powerful data analysis. Moreover, it offers the possibility to replicate complex dynamical behavior under closed-loop operation. Robust, reliable autonomous replication of the structure of complex dynamics has remained elusive, though. The authors experimentally demonstrate the advantage of pretraining a system with additive input noise, achieving robust operation over an extended parameter range, and introduce quantifiers for the quality of replication.
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