We introduce an algorithm to do backpropagation on a spiking network. Our
network is "spiking" in the sense that our neurons accumulate their activation
into a potential over time, and only send out a signal (a "spike") when this
potential crosses a threshold and the neuron is reset. Neurons only update
their states when receiving signals from other neurons. Total computation of
the network thus scales with the number of spikes caused by an input rather
than network size. We show that the spiking Multi-Layer Perceptron behaves
identically, during both prediction and training, to a conventional deep
network of rectified-linear units, in the limiting case where we run the
spiking network for a long time. We apply this architecture to a conventional
classification problem (MNIST) and achieve performance very close to that of a
conventional Multi-Layer Perceptron with the same architecture. Our network is
a natural architecture for learning based on streaming event-based data, and is
a stepping stone towards using spiking neural networks to learn efficiently on
streaming data.

通过介绍一种能够在脉冲网络上进行反向传播的算法，我们表明，在脉冲多层感知机（MLP）长时间运行的极限情况下，该网络在预测和训练期间的行为与带有修正线性单元的传统深度网络行为完全相同。我们将此架构应用于传统分类问题（MNIST），并实现了与具有相同架构的传统 MLP 非常接近的性能。此网络对于基于流事件数据的学习具有自然架构，是使用脉冲神经网络高效学习流式数据的基石。