Reservoir Dynamic

Reservoir computing (RC) is a machine learning framework using recurrent neural networks with a fixed, randomly connected "reservoir" layer to process temporal data, simplifying training by only adjusting a linear readout layer. Current research focuses on optimizing reservoir architectures (e.g., simple cycle reservoirs, echo state networks with feedback, and graph neural networks) and improving their performance through techniques like generalized readouts, self-modulation, and hyperparameter tuning, including exploration of noise impact and efficient hardware implementations. These advancements enhance RC's capabilities for time series prediction, system identification, and various applications across diverse fields, from reservoir simulation and rhythm prediction to chaotic system modeling and even robotic control.