Robustly optimal dynamics for active matter reservoir computing
Mario U. Gaimann, Miriam Klopotek

TL;DR
This paper identifies a robustly optimal dynamical regime in active matter systems for reservoir computing, characterized by intrinsic relaxation abilities near a critical damping point, enhancing information processing across various conditions.
Contribution
It uncovers a previously overlooked dynamical regime in active matter that is optimally suited for reservoir computing, providing new insights into physical computation mechanisms.
Findings
Optimal computational performance occurs just below a critical damping threshold.
The regime exhibits diverse dynamic mechanisms under fluctuating forces.
Correlations in agent dynamics relate closely to driving forces.
Abstract
Information processing abilities of active matter are studied in the reservoir computing (RC) paradigm to infer the future state of a chaotic signal. We uncover an exceptional regime of agent dynamics that has been overlooked previously. It appears robustly optimal for performance under many conditions, thus providing valuable insights into computation with physical systems more generally. The key to forming effective mechanisms for information processing appears in the system's intrinsic relaxation abilities. These are probed without actually enforcing a specific inference goal. The dynamical regime that achieves optimal computation is located just below a critical damping threshold, involving a relaxation with multiple stages, and is readable at the single-particle level. At the many-body level, it yields substrates robustly optimal for RC across varying physical parameters and…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Quantum many-body systems · Mechanical and Optical Resonators
