Spiking neural networks (SNNs) hold promise for demonstrating superior learning and representation capabilities in deep models. Given the tremendous success of ResNet in deep learning, it would naturally follow to train deep SNNs with residual learning. However, existing residual structures for constructing deep SNNs still present challenges of spike redundancy or information loss, as well as redundant learning. In the present study, we first aim to address issues of relative spike redundancy in identity mapping and information loss in non-identity mapping. To this end, we propose an OR-ADD (OA) shortcut connection to merge output spikes/currents from two branches in the residual structure. Furthermore, to mitigate redundant learning in the backbone branch of the residual structure, we introduce the concept of XOR meta-residuals, i.e., selecting pre-learning residuals using the Exclusive-OR (XOR) operation for the backbone branch. Finally, by integrating the OA shortcut and XOR meta-residuals, we devise the XOR residual block and further construct XOResNet with varying depths based on this block. Extensive experiments on four datasets, Fashion-MNIST, CIFAR-10, CIFAR-100, and miniImageNet, show that the proposed XOResNet outperforms existing state-of-the-art deep SNNs optimized via gradient descent. These results validate the effectiveness of our OA shortcut and XOR meta-residual components in overcoming fundamental limitations of residual learning in SNNs, providing new architectural insights for building high-performance neuromorphic systems.

翻译：脉冲神经网络（SNNs）有望在深度模型中展现出卓越的学习与表征能力。鉴于ResNet在深度学习中的巨大成功，自然地会考虑通过残差学习来训练深度SNNs。然而，现有用于构建深度SNNs的残差结构仍面临脉冲冗余或信息损失以及冗余学习的挑战。在本研究中，我们首先旨在解决恒等映射中相对脉冲冗余和非恒等映射中信息损失的问题。为此，我们提出一种OR-ADD（OA）捷径连接，用于融合残差结构中两个分支的输出脉冲/电流。此外，为缓解残差结构主干分支中的冗余学习，我们引入了异或元残差（XOR meta-residuals）概念，即利用异或（XOR）运算为主干分支选择预学习残差。最后，通过整合OA捷径与异或元残差，我们设计了异或残差块，并基于该模块构建了不同深度的XOResNet。在Fashion-MNIST、CIFAR-10、CIFAR-100和miniImageNet四个数据集上的大量实验表明，所提出的XOResNet优于现有通过梯度下降优化的最先进深度SNNs。这些结果验证了我们的OA捷径与异或元残差组件在克服SNNs中残差学习基本局限性方面的有效性，为构建高性能神经形态系统提供了新的架构见解。