  Plastic self-adaptation, nonlinear recurrent dynamics and multi-scale memory
are desired features in hardware implementations of neural networks, because
they enable them to learn, adapt and process information similarly to the way
biological brains do. In this work, we experimentally demonstrate these
properties occurring in arrays of photonic neurons. Importantly, this is
realised autonomously in an emergent fashion, without the need for an external
controller setting weights and without explicit feedback of a global reward
signal. Using a hierarchy of such arrays coupled to a backpropagation-free
training algorithm based on simple logistic regression, we are able to achieve
a performance of 98.2% on the MNIST task, a popular benchmark task looking at
classification of written digits. The plastic nodes consist of silicon
photonics microring resonators covered by a patch of phase-change material that
implements nonvolatile memory. The system is compact, robust, and
straightforward to scale up through the use of multiple wavelengths. Moreover,
it constitutes a unique platform to test and efficiently implement biologically
plausible learning schemes at a high processing speed.
