Miri Mohammad-Ali, Menon Vinod
Department of Physics, Queens College of the City University of New York, Queens, NY 11367, USA.
Physics Program, The Graduate Center, City University of New York, New York 10016, USA.
Nanophotonics. 2023 Jan 5;12(5):883-892. doi: 10.1515/nanoph-2022-0367. eCollection 2023 Mar.
We show that coherent laser networks (CLNs) exhibit emergent neural computing capabilities. The proposed scheme is built on harnessing the collective behavior of laser networks for storing a number of 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. It is discussed that despite the large storage capacity of the network, the large overlap between fixed-point patterns effectively limits pattern retrieval to only two images. Next, we show that this restriction can be uplifted by using nonreciprocal coupling between lasers and this allows for utilizing a large storage capacity. This work opens new possibilities for neural computation with coherent laser networks as novel analog processors. In addition, the underlying dynamical model discussed here suggests a novel energy-based recurrent neural network that handles continuous data as opposed to Hopfield networks and Boltzmann machines that are intrinsically binary systems.
我们表明，相干激光网络（CLN）展现出涌现的神经计算能力。所提出的方案基于利用激光网络的集体行为，将多个相位模式存储为控制动力学方程的稳定不动点，并通过适当的激发条件检索这些模式，从而展现出关联记忆特性。讨论了尽管网络具有大容量存储能力，但不动点模式之间的大量重叠有效地将模式检索限制为仅两个图像。接下来，我们表明通过使用激光器之间的非互易耦合可以消除这种限制，这使得能够利用大容量存储。这项工作为将相干激光网络用作新型模拟处理器进行神经计算开辟了新的可能性。此外，这里讨论的基础动力学模型提出了一种新型的基于能量的递归神经网络，它处理连续数据，这与本质上是二元系统的霍普菲尔德网络和玻尔兹曼机不同。
