Reservoir computing from collective dynamics of active colloidal oscillators

Abstract Physical reservoir computing is a computational framework that offers an energy- and computation-efficient alternative to conventional training of neural networks. In reservoir computing, input signals are mapped into the high-dimensional dynamics of a nonlinear system, and only a simple readout layer is trained. In most physical implementations, the interactions that give rise to the dynamics cannot be tuned directly and high dimensionality is typically achieved through time-multiplexing, which can limit flexibility and efficiency. Here we introduce a reservoir composed of hundreds of hydrodynamically coupled active colloidal oscillators forming a fully parallel physical reservoir whose coupling strength and memory can be tuned in situ. The collective dynamics of the active oscillators allow accurate predictions of chaotic time series without time-multiplexing. We further demonstrate real-time detection of subtle hidden anomalies that preserve all instantaneous statistical properties of the signal. These results establish interacting active colloids as a reconfigurable platform for physical computation and model-free detection of irregularities in complex time signals.