For a biological agent operating under environmental pressure, energy
consumption and reaction times are of critical importance. Similarly,
engineered systems are optimized for short time-to-solution and low
energy-to-solution characteristics. At the level of neuronal implementation,
this implies achieving the desired results with as few and as early spikes as
possible. With time-to-first-spike coding both of these goals are inherently
emerging features of learning. Here, we describe a rigorous derivation of a
learning rule for such first-spike times in networks of leaky
integrate-and-fire neurons, relying solely on input and output spike times, and
show how this mechanism can implement error backpropagation in hierarchical
spiking networks. Furthermore, we emulate our framework on the BrainScaleS-2
neuromorphic system and demonstrate its capability of harnessing the system's
speed and energy characteristics. Finally, we examine how our approach
generalizes to other neuromorphic platforms by studying how its performance is
affected by typical distortive effects induced by neuromorphic substrates.

本文介绍了如何使用第一个尖峰时间作为学习规则，在网络中实现误差反向传播，同时在 BrainScaleS-2 神经形态系统上验证了该框架。研究了该方法在其他神经形态平台上的性能表现。

基于首次发放时间的快速高效的神经形态深度学习

Fast and energy-efficient neuromorphic deep learning with first-spike  times

Spiking networks that perform probabilistic inference have been proposed both
as models of cortical computation and as candidates for solving problems in
machine learning. However, the evidence for spike-based computation being in
any way superior to non-spiking alternatives remains scarce. We propose that
short-term plasticity can provide spiking networks with distinct computational
advantages compared to their classical counterparts. In this work, we use
networks of leaky integrate-and-fire neurons that are trained to perform both
discriminative and generative tasks in their forward and backward information
processing paths, respectively. During training, the energy landscape
associated with their dynamics becomes highly diverse, with deep attractor
basins separated by high barriers. Classical algorithms solve this problem by
employing various tempering techniques, which are both computationally
demanding and require global state updates. We demonstrate how similar results
can be achieved in spiking networks endowed with local short-term synaptic
plasticity. Additionally, we discuss how these networks can even outperform
tempering-based approaches when the training data is imbalanced. We thereby
show how biologically inspired, local, spike-triggered synaptic dynamics based
simply on a limited pool of synaptic resources can allow spiking networks to
outperform their non-spiking relatives.

本文提出利用短期可塑性为脉冲神经网络提供与非脉冲神经网络不同的计算优势，并使用节约的局部突触可塑性模拟温度相关技术来解决网络计算困难性问题。这些网络在处理生成任务时表现出出色性能，在任务不平衡的情况下，它们甚至可以胜过传统算法。

短期突触可塑性脉冲神经元形成更优秀的生成网络

Spiking neurons with short-term synaptic plasticity form superior  generative networks

Gradient descent training techniques are remarkably successful in training
analog-valued artificial neural networks (ANNs). Such training techniques,
however, do not transfer easily to spiking networks due to the spike generation
hard non-linearity and the discrete nature of spike communication. We show that
in a feedforward spiking network that uses a temporal coding scheme where
information is encoded in spike times instead of spike rates, the network
input-output relation is differentiable almost everywhere. Moreover, this
relation is piece-wise linear after a transformation of variables. Methods for
training ANNs thus carry directly to the training of such spiking networks as
we show when training on the permutation invariant MNIST task. In contrast to
rate-based spiking networks that are often used to approximate the behavior of
ANNs, the networks we present spike much more sparsely and their behavior can
not be directly approximated by conventional ANNs. Our results highlight a new
approach for controlling the behavior of spiking networks with realistic
temporal dynamics, opening up the potential for using these networks to process
spike patterns with complex temporal information.

本研究证明了在采用了时间编码方案的前馈脉冲网络中，网络输入 - 输出关系在几乎任何地方都是可微的，并且在变量转换后这种关系是分段线性的。此外，我们展示了通过该方法对项独立 MNIST 任务进行训练的结果，从而证明了训练人工神经网络的方法可以直接应用于训练这种脉冲网络。与基于速率的脉冲网络不同的是，我们所提出的网络的脉冲更为稀疏，且其行为不能直接由传统的人工神经网络来近似。这些结果强调了控制具有复杂时间信息的脉冲模式的脉冲网络行为的新方法。