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.

翻译：本文提出了一种新颖方法，利用脉冲神经网络构建受海马时间细胞启发的时序码本变分量化自编码器。该设计捕获并利用时序依赖性，显著增强了脉冲神经网络的生成能力。神经科学研究已识别出海马“时间细胞”，这些细胞在时序结构化的体验中顺序发放。我们的时序码本通过基于相似性度量触发时间细胞群体的激活来模拟此行为，当输入刺激通过码本时实现。我们在标准基准数据集上进行了广泛实验，包括MNIST、FashionMNIST、CIFAR10、CelebA和下采样的LSUN Bedroom，以验证模型性能。此外，我们评估了时序码本在神经形态数据集NMNIST和DVS-CIFAR10上的有效性，并展示了模型在CelebA-HQ、LSUN Bedroom和LSUN Church等高分辨率数据集上的能力。实验结果表明，我们的方法在多个数据集上持续优于现有的基于脉冲神经网络的生成模型，实现了最先进的性能。值得注意的是，我们的方法在生成高分辨率和时序一致的数据方面表现优异，突显了时序信息在基于脉冲神经网络的生成建模中的关键作用。