  Spiking Neural Networks (SNNs) have garnered substantial attention in
brain-like computing for their biological fidelity and the capacity to execute
energy-efficient spike-driven operations. As the demand for heightened
performance in SNNs surges, the trend towards training deeper networks becomes
imperative, while residual learning stands as a pivotal method for training
deep neural networks. In our investigation, we identified that the SEW-ResNet,
a prominent representative of deep residual spiking neural networks,
incorporates non-event-driven operations. To rectify this, we propose a novel
training paradigm that first accumulates a large amount of redundant
information through OR Residual Connection (ORRC), and then filters out the
redundant information using the Synergistic Attention (SynA) module, which
promotes feature extraction in the backbone while suppressing the influence of
noise and useless features in the shortcuts. When integrating SynA into the
network, we observed the phenomenon of "natural pruning", where after training,
some or all of the shortcuts in the network naturally drop out without
affecting the model's classification accuracy. This significantly reduces
computational overhead and makes it more suitable for deployment on edge
devices. Experimental results on various public datasets confirmed that the
SynA-ResNet achieved single-sample classification with as little as 0.8 spikes
per neuron. Moreover, when compared to other residual SNN models, it exhibited
higher accuracy and up to a 28-fold reduction in energy consumption.
