  Generative models based on neural networks present a substantial challenge
within deep learning. As it stands, such models are primarily limited to the
domain of artificial neural networks. Spiking neural networks, as the third
generation of neural networks, offer a closer approximation to brain-like
processing due to their rich spatiotemporal dynamics. However, generative
models based on spiking neural networks are not well studied. In this work, we
pioneer constructing a spiking generative adversarial network capable of
handling complex images. Our first task was to identify the problems of
out-of-domain inconsistency and temporal inconsistency inherent in spiking
generative adversarial networks. We addressed these issues by incorporating the
Earth-Mover distance and an attention-based weighted decoding method,
significantly enhancing the performance of our algorithm across several
datasets. Experimental results reveal that our approach outperforms existing
methods on the MNIST, FashionMNIST, CIFAR10, and CelebA datasets. Moreover,
compared with hybrid spiking generative adversarial networks, where the
discriminator is an artificial analog neural network, our methodology
demonstrates closer alignment with the information processing patterns observed
in the mouse.

Neural and Evolutionary Computing