Neural network computational architectures have been inspired
by the physical structure of the human
brain. There, a large number of nonlinear nodes are connected into
network, and learning is generally
interpreted via modifications to the networks connectivity structure.
Yet, implementations of neural networks are typically based on their
emulation within serial and binary logic computers. Recently, Reservoir
Computing (RC) has been demonstrated with various levels of hardware
implementations inside nonlinear networks implemented in a delay system.
I will demonstrate how the same scheme can be realized in a photonic
networks of 1600 individual nonlinear oscillators. Furthermore, I will
report on results of a full hardware‐based learning scheme for such
networks. Our system therefore presents a significant step to a full
physical implementation of the originally envisioned neuromorphic