Spike Timing-Dependent Plasticity (STDP) is a promising substitute to backpropagation for local training of Spiking Neural Networks (SNNs) on neuromorphic hardware. STDP allows SNNs to address classification tasks by combining unsupervised STDP for feature extraction and supervised STDP for classification. Unsupervised STDP is usually employed with Winner-Takes-All (WTA) competition to learn distinct patterns. However, WTA for supervised STDP classification faces unbalanced competition challenges. In this paper, we propose a method to effectively implement WTA competition in a spiking classification layer employing first-spike coding and supervised STDP training. We introduce the Neuronal Competition Group (NCG), an architecture that improves classification capabilities by promoting the learning of various patterns per class. An NCG is a group of neurons mapped to a specific class, implementing intra-class WTA and a novel competition regulation mechanism based on two-compartment thresholds. We incorporate our proposed architecture into spiking classification layers trained with state-of-the-art supervised STDP rules. On top of two different unsupervised feature extractors, we obtain significant accuracy improvements on image recognition datasets such as CIFAR-10 and CIFAR-100. We show that our competition regulation mechanism is crucial for ensuring balanced competition and improved class separation.

翻译：脉冲时序依赖可塑性（STDP）是神经形态硬件上局部训练脉冲神经网络（SNN）时一种有前景的反向传播替代方案。STDP通过结合无监督STDP进行特征提取和监督STDP进行分类，使SNN能够处理分类任务。无监督STDP通常与赢者通吃（WTA）竞争机制结合以学习不同模式。然而，在监督STDP分类中采用WTA机制面临竞争不平衡的挑战。本文提出一种方法，可在采用首脉冲编码和监督STDP训练的脉冲分类层中有效实现WTA竞争。我们引入神经元竞争组（NCG）架构，该架构通过促进每个类别学习多种模式来提升分类能力。NCG是一组映射到特定类别的神经元，实现了类内WTA竞争以及基于双隔室阈值的新型竞争调节机制。我们将所提出的架构集成到采用先进监督STDP规则训练的脉冲分类层中。在两种不同的无监督特征提取器基础上，我们在CIFAR-10和CIFAR-100等图像识别数据集上取得了显著的准确率提升。实验表明，我们的竞争调节机制对于确保平衡竞争和改善类别分离至关重要。