Reservoir computing

Reservoir computing is a framework for computation like a neural network. Typically an input signal is fed into a fixed (random) dynamical system called a reservoir and the dynamics of the reservoir map the input to a higher dimension. Then a simple readout mechanism is trained to read the state of the reservoir and map it 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 of recurrently connected units. The connectivity structure is usually random, and the units are usually non-linear. The overall dynamics of the reservoir is driven by the input, and also affected by the past. A rich collection of dynamical input-output mapping is a crucial advantage over simple time delay neural networks.

Readout

The readout is carried out using a linear transformation of the reservoir output. This transformation is adapted to the task of interest by using a linear regression or a Ridge regression using a teaching signal.

Types

Echo state network

Main article: Echo state network

Backpropagation-decorrelation

Backpropagation-Decorrelation (BPDC)

Liquid-state machine

Main article: Liquid-state machine