Paired Competing Neurons Improving STDP Supervised Local Learning In Spiking Neural Networks
arxiv(2023)
摘要
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.
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