Spiking neural networks (SNN) are a viable alternative to conventional
artificial neural networks when energy efficiency and computational complexity
are of importance. A major advantage of SNNs is their binary information
transfer through spike trains. The training of SNN has, however, been a
challenge, since neuron models are non-differentiable and traditional
gradient-based backpropagation algorithms cannot be applied directly.
Furthermore, spike-timing-dependent plasticity (STDP), albeit being a
spike-based learning rule, updates weights locally and does not optimize for
the output error of the network. We present desire backpropagation, a method to
derive the desired spike activity of neurons from the output error. The loss
function can then be evaluated locally for every neuron. Incorporating the
desire values into the STDP weight update leads to global error minimization
and increasing classification accuracy. At the same time, the neuron dynamics
and computational efficiency of STDP are maintained, making it a spike-based
supervised learning rule. We trained three-layer networks to classify MNIST and
Fashion-MNIST images and reached an accuracy of 98.41% and 87.56%,
respectively. Furthermore, we show that desire backpropagation is
computationally less complex than backpropagation in traditional neural
networks.

该论文介绍了一种名为 desire backpropagation 的方法，它利用 STDP 的权重更新，实现了全局误差最小化和分类准确率的提高；同时，该方法具有 STDP 的神经动力学和计算效率，是一种基于脉冲的监督学习规则。作者使用 desire backpropagation 对 MNIST 和 Fashion-MNIST 图像进行了分类，实验效果良好。