Spiking Neural Networks (SNNs) have recently emerged as a prominent neural
computing paradigm. However, the typical shallow SNN architectures have limited
capacity for expressing complex representations while training deep SNNs using
input spikes has not been successful so far. Diverse methods have been proposed
to get around this issue such as converting off-the-shelf trained deep
Artificial Neural Networks (ANNs) to SNNs. However, the ANN-SNN conversion
scheme fails to capture the temporal dynamics of a spiking system. On the other
hand, it is still a difficult problem to directly train deep SNNs using input
spike events due to the discontinuous, non-differentiable nature of the spike
generation function. To overcome this problem, we propose an approximate
derivative method that accounts for the leaky behavior of LIF neurons. This
method enables training deep convolutional SNNs directly (with input spike
events) using spike-based backpropagation. Our experiments show the
effectiveness of the proposed spike-based learning on deep networks (VGG and
Residual architectures) by achieving the best classification accuracies in
MNIST, SVHN and CIFAR-10 datasets compared to other SNNs trained with a
spike-based learning. Moreover, we analyze sparse event-based computations to
demonstrate the efficacy of the proposed SNN training method for inference
operation in the spiking domain.

通过引入近似导数方法和基于脉冲的反向传播方法，本文提出一种可以直接训练深度脉冲神经网络的方法，实验结果表明，该方法在 MNIST、SVHN 和 CIFAR-10 数据集上取得了比其他基于脉冲的神经网络更好的分类效果。

为训练深度神经网络架构实现基于脉冲的反向传播

Enabling Spike-based Backpropagation for Training Deep Neural Network  Architectures

We present a spike-based unsupervised regenerative learning scheme to train
Spiking Deep Networks (SpikeCNN) for object recognition problems using
biologically realistic leaky integrate-and-fire neurons. The training
methodology is based on the Auto-Encoder learning model wherein the
hierarchical network is trained layer wise using the encoder-decoder principle.
Regenerative learning uses spike-timing information and inherent latencies to
update the weights and learn representative levels for each convolutional layer
in an unsupervised manner. The features learnt from the final layer in the
hierarchy are then fed to an output layer. The output layer is trained with
supervision by showing a fraction of the labeled training dataset and performs
the overall classification of the input. Our proposed methodology yields
0.92%/29.84% classification error on MNIST/CIFAR10 datasets which is comparable
with state-of-the-art results. The proposed methodology also introduces
sparsity in the hierarchical feature representations on account of event-based
coding resulting in computationally efficient learning.

提出一种基于脉冲的非监督递归学习方案，用于使用生物学上可信的漏积分放电神经元训练 Spiking Deep Networks (SpikeCNN) 进行目标识别问题。训练方法基于自编码器学习模型，其中分层网络使用编码器 - 解码器原理逐层训练。递归学习使用脉冲时序信息和固有延迟以无监督方式更新权重并学习每个卷积层的代表水平。从层次结构中学到的特征然后馈送到一个输出层。所提议的方法在 MNIST/CIFAR10 数据集上产生了与现有技术水平相当的分类错误率，并通过基于事件的编码引入稀疏性来提高计算效率。