Spiking Convolutional Vision Transformer.

This paper introduces the design of a Spiking Convolutional Vision Transformer (SCvT) by using the biological-inspired learning rules including spike-timing-dependent plasticity (STDP) and reward–modulated STDP (R-STDP) for a classification task. We describe the structure of the SCvT simulated with deep spiking convolutional neural networks (DSCNN) and give a detailed description of the classification strategy.