# Spiking Generative Adversarial Network with Attention Scoring Decoding

arXiv: https://arxiv.org/abs/2305.10246 · HTML: https://mlsanity.com/paper/2305.10246/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2023.

## Built on by (most cited first, 7 of 7)

- [Spiking Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2306.17046/) (2023)
- [Enhancing Audio-Visual Spiking Neural Networks through Semantic-Alignment and Cross-Modal Residual Learning](https://mlsanity.com/paper/2502.12488/) (2025)
- [SDiT: Spiking Diffusion Model with Transformer](https://mlsanity.com/paper/2402.11588/) (2024)
- [Spiking Diffusion Models](https://mlsanity.com/paper/2408.16467/) (2024)
- [ESVAE: An Efficient Spiking Variational Autoencoder with Reparameterizable Poisson Spiking Sampling](https://mlsanity.com/paper/2310.14839/) (2023)
- [Negative Binomial Variational Autoencoders for Overdispersed Latent Modeling](https://mlsanity.com/paper/2508.05423/) (2025)
- [Biologically Inspired Spiking Diffusion Model with Adaptive Lateral Selection Mechanism](https://mlsanity.com/paper/2503.23767/) (2025)

## Abstract (arXiv)

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.