  Direct training of Spiking Neural Networks (SNNs) on neuromorphic hardware
has the potential to significantly reduce the energy consumption of artificial
neural network training. SNNs trained with Spike Timing-Dependent Plasticity
(STDP) benefit from gradient-free and unsupervised local learning, which can be
easily implemented on ultra-low-power neuromorphic hardware. However,
classification tasks cannot be performed solely with unsupervised STDP. 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 for feature extraction. 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
toward target or non-target samples through intra-class competition. We
evaluate our methods on image recognition datasets, including MNIST,
Fashion-MNIST, and CIFAR-10. Results show that our methods outperform
state-of-the-art supervised STDP learning rules, for comparable architectures
and numbers of neurons. Further analysis demonstrates that the use of PCN
enhances the performance of S2-STDP, regardless of the hyperparameter set and
without introducing any additional hyperparameters.
