  Many modern applications of the artificial neural networks ensue large number
of layers making traditional digital implementations increasingly complex.
Optical neural networks offer parallel processing at high bandwidth, but have
the challenge of noise accumulation. We propose here a new type of neural
networks using stochastic resonances as an inherent part of the architecture
and demonstrate a possibility of significant reduction of the required number
of neurons for a given performance accuracy. We also show that such a neural
network is more robust against the impact of noise.
