🔗 Reservoir computing

🔗 Computing

Reservoir computing is a framework for computation that may be viewed as an extension of neural networks. 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 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. One important feature of this system is that it can use the computational power of naturally available systems which is different from the neural networks and it reduces the computational cost.