Compared with rate-based artificial neural networks, Spiking Neural Networks
(SNN) provide a more biological plausible model for the brain. But how they
perform supervised learning remains elusive. Inspired by recent works of Bengio
et al., we propose a supervised learning algorithm based on Spike-Timing
Dependent Plasticity (STDP) for a hierarchical SNN consisting of Leaky
Integrate-and-fire (LIF) neurons. A time window is designed for the presynaptic
neuron and only the spikes in this window take part in the STDP updating
process. The model is trained on the MNIST dataset. The classification accuracy
approach that of a Multilayer Perceptron (MLP) with similar architecture
trained by the standard back-propagation algorithm.

本文提出了一种基于 Spike-Timing Dependent Plasticity (STDP) 的监督学习算法，用于基于 Leaky Integrate-and-fire (LIF) 神经元的分层 Spiking Neural Networks。该算法经 MNIST 数据集训练后，分类精度接近于使用标准反向传播算法训练的类似结构的多层感知器模型。

基于 STDP 的突触可塑性神经网络有监督学习算法

An STDP-Based Supervised Learning Algorithm for Spiking Neural Networks

We train spiking deep networks using leaky integrate-and-fire (LIF) neurons,
and achieve state-of-the-art results for spiking networks on the CIFAR-10 and
MNIST datasets. This demonstrates that biologically-plausible spiking LIF
neurons can be integrated into deep networks can perform as well as other
spiking models (e.g. integrate-and-fire). We achieved this result by softening
the LIF response function, such that its derivative remains bounded, and by
training the network with noise to provide robustness against the variability
introduced by spikes. Our method is general and could be applied to other
neuron types, including those used on modern neuromorphic hardware. Our work
brings more biological realism into modern image classification models, with
the hope that these models can inform how the brain performs this difficult
task. It also provides new methods for training deep networks to run on
neuromorphic hardware, with the aim of fast, power-efficient image
classification for robotics applications.

使用 LIF 神经元训练脉冲深度网络，并在 CIFAR-10 和 MNIST 数据集上实现了最先进的结果。通过软化 LIF 响应函数并添加噪声，我们的方法旨在实现在神经形态硬件上训练深度网络的快速、高效的图像分类。