Spiking neural networks (SNNs) have received significant attention for their
biological plausibility. SNNs theoretically have at least the same
computational power as traditional artificial neural networks (ANNs). They
possess potential of achieving energy-efficiency while keeping comparable
performance to deep neural networks (DNNs). However, it is still a big
challenge to train a very deep SNN. In this paper, we propose an efficient
approach to build a spiking version of deep residual network (ResNet). ResNet
is considered as a kind of the state-of-the-art convolutional neural networks
(CNNs). We employ the idea of converting a trained ResNet to a network of
spiking neurons, named Spiking ResNet (S-ResNet). We propose a shortcut
conversion model to appropriately scale continuous-valued activations to match
firing rates in SNN, and a compensation mechanism to reduce the error caused by
discretisation. Experimental results demonstrate that, compared with the
state-of-the-art SNN approaches, the proposed Spiking ResNet achieves the best
performance on CIFAR-10, CIFAR-100, and ImageNet 2012. Our work is the first
time to build a SNN deeper than 40, with comparable performance to ANNs on a
large-scale dataset.

提出了一种有效的方法，将 ResNet 转换为名为 S-ResNet 的脉冲神经元网络，该方法采用转换模型和补偿机制来降低离散化引起的误差，并在 CIFAR-10，CIFAR-100 和 ImageNet 2012 数据集上实现了比现有 SNN 途径更好的性能，这是第一次在大规模数据集上构建超过 40 层深度的 SNN，并获得与 ANNs 相当的性能。

脉冲深度残差网络

Spiking Deep Residual Network

We introduce a weight update formula that is expressed only in terms of
firing rates and their derivatives and that results in changes consistent with
those associated with spike-timing dependent plasticity (STDP) rules and
biological observations, even though the explicit timing of spikes is not
needed. The new rule changes a synaptic weight in proportion to the product of
the presynaptic firing rate and the temporal rate of change of activity on the
postsynaptic side. These quantities are interesting for studying theoretical
explanation for synaptic changes from a machine learning perspective. In
particular, if neural dynamics moved neural activity towards reducing some
objective function, then this STDP rule would correspond to stochastic gradient
descent on that objective function.

通过权重更新公式来模拟突触可塑性变化，该公式仅利用神经元的发射率和其导数做出权重更新的改变，该公式可以视为随机梯度下降，用于神经动力学寻求降低目标函数。