Spiking neural networks (SNNs) possess energy-efficient potential due to
event-based computation. However, supervised training of SNNs remains a
challenge as spike activities are non-differentiable. Previous SNNs training
methods can be generally categorized into two basic classes, i.e.,
backpropagation-like training methods and plasticity-based learning methods.
The former methods are dependent on energy-inefficient real-valued computation
and non-local transmission, as also required in artificial neural networks
(ANNs), whereas the latter are either considered to be biologically implausible
or exhibit poor performance. Hence, biologically plausible (bio-plausible)
high-performance supervised learning (SL) methods for SNNs remain deficient. In
this paper, we proposed a novel bio-plausible SNN model for SL based on the
symmetric spike-timing dependent plasticity (sym-STDP) rule found in
neuroscience. By combining the sym-STDP rule with bio-plausible synaptic
scaling and intrinsic plasticity of the dynamic threshold, our SNN model
implemented SL well and achieved good performance in the benchmark recognition
task (MNIST dataset). To reveal the underlying mechanism of our SL model, we
visualized both layer-based activities and synaptic weights using the
t-distributed stochastic neighbor embedding (t-SNE) method after training and
found that they were well clustered, thereby demonstrating excellent
classification ability. Furthermore, to verify the robustness of our model, we
trained it on another more realistic dataset (Fashion-MNIST), which also showed
good performance. As the learning rules were bio-plausible and based purely on
local spike events, our model could be easily applied to neuromorphic hardware
for online training and may be helpful for understanding SL information
processing at the synaptic level in biological neural systems.

本文提出了一种基于神经科学中对称性脉冲定时相关塑性规则的生物可行的 SNN 模型，该模型结合生物可行的突触缩放和动态阈值的内在可塑性来实现监督学习，并在标准识别任务中实现了良好的性能，其学习规则基于局部脉冲事件，因此可以轻松应用于神经形态硬件，有助于理解生物神经系统内部的监督学习信息处理机制。