Direct training of Spiking Neural Networks (SNNs) on neuromorphic hardware
has the potential to significantly reduce the high energy consumption of
Artificial Neural Networks (ANNs) training on modern computers. The biological
plausibility of SNNs allows them to benefit from bio-inspired plasticity rules,
such as Spike Timing-Dependent Plasticity (STDP). STDP offers gradient-free and
unsupervised local learning, which can be easily implemented on neuromorphic
hardware. However, relying solely on unsupervised STDP to perform
classification tasks is not enough. In this paper, we propose Stabilized
Supervised STDP (S2-STDP), a supervised STDP learning rule to train the
classification layer of an SNN equipped with unsupervised STDP. S2-STDP
integrates error-modulated weight updates that align neuron spikes with desired
timestamps derived from the average firing time within the layer. Then, we
introduce a training architecture called Paired Competing Neurons (PCN) to
further enhance the learning capabilities of our classification layer trained
with S2-STDP. PCN associates each class with paired neurons and encourages
neuron specialization through intra-class competition. We evaluated our
proposed methods on image recognition datasets, including MNIST, Fashion-MNIST,
and CIFAR-10. Results showed that our methods outperform current supervised
STDP-based state of the art, for comparable architectures and numbers of
neurons. Also, the use of PCN enhances the performance of S2-STDP, regardless
of the configuration, and without introducing any hyperparameters.Further
analysis demonstrated that our methods exhibited improved hyperparameter
robustness, which reduces the need for tuning.

通过将监督性 Spike Timing-Dependent Plasticity（STDP）与自监督 STDP 相结合，并引入 Paired Competing Neurons（PCN）训练架构，我们提出了一种能在神经形态硬件上进行分类任务训练的 Spiking Neural Networks（SNNs）方法，其方法在 MNIST、Fashion-MNIST 和 CIFAR-10 数据集上表现出比现有的基于 STDP 的方法更优越的性能，同时具备改进的超参数鲁棒性。