There is an interest in finding energy efficient alternatives to current
state of the art neural network training algorithms. Spiking neural network are
a promising approach, because they can be simulated energy efficiently on
neuromorphic hardware platforms. However, these platforms come with limitations
on the design of the training algorithm. Most importantly, backpropagation
cannot be implemented on those. We propose a novel neuromorphic algorithm, the
\textit{Spiking Forward Direct Feedback Alignment} (SFDFA) algorithm, an
adaption of \textit{Forward Direct Feedback Alignment} to train SNNs. SFDFA
estimates the weights between output and hidden neurons as feedback
connections. The main contribution of this paper is to describe how exact local
gradients of spikes can be computed in an online manner while taking into
account the intra-neuron dependencies between post-synaptic spikes and derive a
dynamical system for neuromorphic hardware compatibility. We compare the SFDFA
algorithm with a number of competitor algorithms and show that the proposed
algorithm achieves higher performance and convergence rates.

在寻找与当前最先进的神经网络训练算法相比更节能的替代方法方面，有一种对脉冲神经网络的兴趣，因为它们可以在神经形态硬件平台上高效模拟能量。然而，这些平台在训练算法设计方面存在限制，最重要的是不能实现反向传播。本文提出了一种新颖的神经形态算法 —— 脉冲前向直接反馈对齐（SFDFA）算法，这是一种将前向直接反馈对齐（Forward Direct Feedback Alignment）用于训练脉冲神经网络的改进方式。SFDFA 算法通过反馈连接来估计输出和隐藏神经元之间的权重。本文的主要贡献在于描述了如何在线获取脉冲的精确局部梯度，同时考虑到突触后脉冲之间的内部神经元依赖关系，并推导了神经形态硬件兼容的动力系统。我们将 SFDFA 算法与一些竞争算法进行比较，并证明了该算法具有更高的性能和收敛速度。