  The optical domain is a promising field for physical implementation of neural
networks, due to the speed and parallelism of optics. Extreme Learning Machines
(ELMs) are feed-forward neural networks in which only output weights are
trained, while internal connections are randomly selected and left untrained.
Here we report on a photonic ELM based on a frequency-multiplexed fiber setup.
Multiplication by output weights can be performed either offline on a computer,
or optically by a programmable spectral filter. We present both numerical
simulations and experimental results on classification tasks and a nonlinear
channel equalization task.
