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

arXiv:2308.02194v2 Announce Type: replace
Abstract: 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 …

abstract artificial arxiv benefit consumption cs.cv energy free gradient hardware improving network networks network training neural network neural networks neuromorphic neurons reduce spiking neural networks training type unsupervised