  This paper presents a novel approach leveraging Spiking Neural Networks
(SNNs) to construct a Variational Quantized Autoencoder (VQ-VAE) with a
temporal codebook inspired by hippocampal time cells. This design captures and
utilizes temporal dependencies, significantly enhancing the generative
capabilities of SNNs. Neuroscientific research has identified hippocampal "time
cells" that fire sequentially during temporally structured experiences. Our
temporal codebook emulates this behavior by triggering the activation of time
cell populations based on similarity measures as input stimuli pass through it.
We conducted extensive experiments on standard benchmark datasets, including
MNIST, FashionMNIST, CIFAR10, CelebA, and downsampled LSUN Bedroom, to validate
our model's performance. Furthermore, we evaluated the effectiveness of the
temporal codebook on neuromorphic datasets NMNIST and DVS-CIFAR10, and
demonstrated the model's capability with high-resolution datasets such as
CelebA-HQ, LSUN Bedroom, and LSUN Church. The experimental results indicate
that our method consistently outperforms existing SNN-based generative models
across multiple datasets, achieving state-of-the-art performance. Notably, our
approach excels in generating high-resolution and temporally consistent data,
underscoring the crucial role of temporal information in SNN-based generative
modeling.
