In recent years, studies on image generation models of spiking neural networks (SNNs) have gained the attention of many researchers. Variational autoencoders (VAEs), as one of the most popular image generation models, have attracted a lot of work exploring their SNN implementation. Due to the constrained binary representation in SNNs, existing SNN VAE methods implicitly construct the latent space by an elaborated autoregressive network and use the network outputs as the sampling variables. However, this unspecified implicit representation of the latent space will increase the difficulty of generating high-quality images and introduces additional network parameters. In this paper, we propose an efficient spiking variational autoencoder (ESVAE) that constructs an interpretable latent space distribution and design a reparameterizable spiking sampling method. Specifically, we construct the prior and posterior of the latent space as a Poisson distribution using the firing rate of the spiking neurons. Subsequently, we propose a reparameterizable Poisson spiking sampling method, which is free from the additional network. Comprehensive experiments have been conducted, and the experimental results show that the proposed ESVAE outperforms previous SNN VAE methods in reconstructed & generated images quality. In addition, experiments demonstrate that ESVAE's encoder is able to retain the original image information more efficiently, and the decoder is more robust. The source code is available at https://github.com/QgZhan/ESVAE.

翻译：近年来，脉冲神经网络（SNN）的图像生成模型研究受到了众多学者的关注。作为最流行的图像生成模型之一，变分自编码器（VAE）吸引了大量探索其SNN实现的工作。由于SNN中受限的二进制表示，现有SNN VAE方法通过精心设计的自回归网络隐式构建潜在空间，并将网络输出作为采样变量。然而，这种未明确指定的潜在空间隐式表示会增加生成高质量图像的难度，并引入额外网络参数。本文提出了一种高效的脉冲变分自编码器（ESVAE），它构建了可解释的潜在空间分布，并设计了可重参数化的脉冲采样方法。具体而言，我们利用脉冲神经元的发放率将潜在空间的先验和后验构建为泊松分布。随后，我们提出了一种无需额外网络的可重参数化泊松脉冲采样方法。通过全面实验，结果表明所提出的ESVAE在重构与生成图像质量上优于现有SNN VAE方法。此外，实验证明ESVAE的编码器能更高效地保留原始图像信息，且解码器具有更强的鲁棒性。源代码见https://github.com/QgZhan/ESVAE。