  The explosive growth of data and its related energy consumption is pushing
the need to develop energy-efficient brain-inspired schemes and materials for
data processing and storage. Here, we demonstrate experimentally that Co/Pt
films can be used as artificial synapses by manipulating their magnetization
state using circularly-polarized ultrashort optical pulses at room temperature.
We also show an efficient implementation of supervised perceptron learning on
an opto-magnetic neural network, built from such magnetic synapses.
Importantly, we demonstrate that the optimization of synaptic weights can be
achieved using a global feedback mechanism, such that the learning does not
rely on external storage or additional optimization schemes. These results
suggest there is high potential for realizing artificial neural networks using
optically-controlled magnetization in technologically relevant materials, that
can learn not only fast but also energy-efficient.
