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.
Contents
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
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Backpropagation-decorrelation
Backpropagation-Decorrelation (BPDC)
Liquid-state machine
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See also
- Reservoir Computing using delay systems, Nature Communications 2011
- Optoelectronic Reservoir Computing, Scientific Reports February 2012
- Optoelectronic Reservoir Computing, Optics Express 2012
- All-optical Reservoir Computing, Nature Communications 2013
- Deep Learning
- Extreme Learning Machines
