  Spiking Neural Networks (SNNs) are capable of encoding and processing
temporal information in a biologically plausible way. However, most existing
SNN-based methods for image tasks do not fully exploit this feature. Moreover,
they often overlook the role of adaptive threshold in spiking neurons, which
can enhance their dynamic behavior and learning ability. To address these
issues, we propose a novel method for image decoding based on temporal
attention (TAID) and an adaptive Leaky-Integrate-and-Fire (ALIF) neuron model.
Our method leverages the temporal information of SNN outputs to generate
high-quality images that surpass the state-of-the-art (SOTA) in terms of
Inception score, Fr\'echet Inception Distance, and Fr\'echet Autoencoder
Distance. Furthermore, our ALIF neuron model achieves remarkable classification
accuracy on MNIST (99.78\%) and CIFAR-10 (93.89\%) datasets, demonstrating the
effectiveness of learning adaptive thresholds for spiking neurons. The code is
available at https://github.com/bollossom/ICLR_TINY_SNN.
