Reservoir computing is a framework for computation like a neural network

Neural network

The term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes...

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Typically an input signal is fed into a fixed (random) dynamical system

Dynamical system

A dynamical system is a concept in mathematics where a fixed rule describes the time dependence of a point in a geometrical space. Examples include the mathematical models that describe the swinging of a clock pendulum, the flow of water in a pipe, and the number of fish each springtime in a...

 called 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 network

Echo state network

The echo state network is a recurrent neural network with a sparsely connected hidden layer . The connectivity and weights of hidden neurons are randomly assigned and are fixed...

s 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 network

Time delay neural network

Time delay neural network is an alternative neural network architecture whose primary purpose is to work on continuous data. The advantage of this architecture is to adapt the network online and hence helpful in many real time applications, like time series prediction, online spell check,...

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