  Artificial neural networks are intensively used to perform cognitive tasks
such as image classification on traditional computers. With the end of CMOS
scaling and increasing demand for efficient neural networks, alternative
architectures implementing neural functions efficiently are being studied. This
study leverages the demonstrated frequency tuning capabilities of compact
nano-oscillators and their synchronization dynamics to implement a neuron using
a pair of synchronized oscillators, and which features an unconventional
response curve. We show that this compact neuron can naturally implement
generic logic gates, including XOR. A simulated oscillator-based neural network
is then shown to achieve results equivalent to standard approaches on two
reference classification tasks. Finally, the performance of the system is
evaluated in the presence of oscillator phase noise, an important issue of
oscillating nanodevices. These results open the way for the design of
alternative architectures adapted to efficient neural network execution.
