Neuronal Competition Groups with Supervised STDP for Spike-Based Classification

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.

Paired Competing Neurons Improving STDP Supervised Local Learning In Spiking Neural Networks

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.