  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.
