Over the past decade, artificial neural networks (ANNs) have made tremendous
advances, in part due to the increased availability of annotated data. However,
ANNs typically require significant power and memory consumptions to reach their
full potential. Spiking neural networks (SNNs) have recently emerged as a
low-power alternative to ANNs due to their sparsity nature. SNN, however, are
not as easy to train as ANNs. In this work, we propose a hybrid SNN training
scheme and apply it to segment human hippocampi from magnetic resonance images.
Our approach takes ANN-SNN conversion as an initialization step and relies on
spike-based backpropagation to fine-tune the network. Compared with the
conversion and direct training solutions, our method has advantages in both
segmentation accuracy and training efficiency. Experiments demonstrate the
effectiveness of our model in achieving the design goals.

本文提出了一种基于 ANN-SNN 转换和基于脉冲的反向传播的混合 SNN 训练方案，并将其应用于磁共振图像中人类海马的分割。与转换和直接训练方案相比，我们的方法在分割准确度和训练效率方面具有优势。实验证明了我们模型的有效性。