Reservoir Kernel

Reservoir computing (RC) leverages fixed, recurrent neural networks to process temporal data, focusing on training only a linear readout layer while leaving the reservoir's internal dynamics untouched. Current research explores various reservoir architectures, including simple cycle reservoirs and those based on recurrent neural networks, aiming to optimize their performance and understand their theoretical limits in approximating complex functions and time series. This approach offers advantages in training speed, stability, and potential for efficient hardware implementation, showing promise for applications ranging from time series prediction to more physically-realized computational reservoirs.