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We propose utilizing coherently coupled laser networks for neural computing. The proposed scheme is built on harnessing the collective behavior of laser networks for storing phase patterns as stable fixed points of the governing dynamical equations and retrieving such patterns through proper excitation conditions, thus exhibiting an associative memory property. We further show that limitations on the number of images can be overcome by using nonreciprocal coupling between lasers, thus allowing for utilizing the large storage capacity inherent to the laser network. This work opens new possibilities for neural computation with coherent laser networks as a novel physical analog processor.
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Mohammad-Ali Miri, Kevin Zelaya, "Energy-based neural network models with coherent laser networks," Proc. SPIE PC12647, Active Photonic Platforms (APP) 2023, PC126470L (4 October 2023); https://doi.org/10.1117/12.2679117