Spiking neural network is a kind of neuromorphic computing that is believed to improve the level of intelligence and provide advantages for quantum computing. In this work, we address this issue by designing an optical spiking neural network and find that it can be used to accelerate the speed of computation, especially on combinatorial optimization problems. Here the spiking neural network is constructed by the antisymmetrically coupled degenerate optical parametric oscillator pulses and dissipative pulses. A nonlinear transfer function is chosen to mitigate amplitude inhomogeneities and destabilize the resulting local minima according to the dynamical behavior of spiking neurons. It is numerically shown that the spiking neural network-coherent Ising machines have excellent performance on combinatorial optimization problems, which is expected to offer new applications for neural computing and optical computing.

翻译：脉冲神经网络是一种神经形态计算方法，被认为能提升智能水平并为量子计算带来优势。本研究通过设计光学脉冲神经网络来解决该问题，发现其可加速计算速度，尤其在组合优化问题上效果显著。本文构建的脉冲神经网络采用反对称耦合的简并光学参量振荡器脉冲与耗散脉冲，通过选择非线性传递函数来抑制振幅不均匀性，并根据脉冲神经元的动力学行为破坏局部极小值的稳定性。数值结果表明，基于脉冲神经网络的相干伊辛机在组合优化问题上展现出卓越性能，有望为神经计算和光学计算领域开辟新应用。