Reservoir computing

Reservoir computing is a framework for computation like neural network. Typically input signal is fed into a fixed (random) dynamical system called reservoir and the dynamics of the reservoir maps the input to a higher dimension. Then a simple readout mechanism is trained to read the state of the reservoir and map to the desired output. The main benefit is that the training is performed only at the readout stage and the reservoir is fixed. Liquid-state machines and echo state networks are two major types of reservoir computing.

Reservoir
The reservoir consists of a collection recurrently connected units. The connectivity structure is usually random, and the units are usually non-linear. The over all dynamics of the reservoir is driven by the input, and also affected by the past. A rich collection of dynamical input-output mapping is crucial advantage over simple time delay neural networks.

Backpropagation-decorrelation
Backpropagation-Decorrelation (BPDC)