  The search for energy-efficient alternatives for computer data processing
urges us to look at the neural architecture of our brain, as it indeed uses
much less energy than a computer. While working on one such alternative, we
built a neural network in which the magnetization of a multilayered CoPt sample
as an artificial synapse adapts to ultrashort optical pulses. As an underlying
mechanism behind such magnetization adaptation, we exposed a new phenomenon in
the form of surface currents stem out from the interactions of these
helicity-dependent circular pulses with the sample. To supervise this network
for a pattern recognition functionality, 90% of patterns from a data set of
twenty-seven, constituting each pattern by nine binary inputs are utilized. In
the network training, a consistent number of picosecond pump pulses, in their
normal incidence to the sample, sequentially adjust each synaptic weight
assigned with every input of such patterns. These, instead Gaussian, flat-top
shaped pulses are used for the first time in this research field and here they
resolve the sample inhomogeneity and the efficiency issues of the network. For
the same efficiency reason, weight readout was also accomplished by never used
picosecond probe pulses in the magneto-optic imaging. Further, on the training,
the network keeps adjusting the weights until all the weight-dependent patterns
are categorized into one of the twofold classes around a pre-set threshold.
Convincingly, this supervised network identifies 10% rest of the untrained
patterns from the given data set and thus motivates us to develop other
complexes yet energy-efficient magneto-optical networks with more advanced
functionalities.
