Before revision:  The SNN recognizes the frames more accurately, even if trained on few, still, and untransformed images, with unsupervised and synaptically-local learning, binary spikes, and a single layer of neurons - all in contrast to the deep-learning-trained ANNs .  These results indicate that on-line adaptivity and spike-based computation may optimize natural intelligence for natural environments . Moreover, this expands the goal of exploiting biological neuro-synaptic properties for AI ,  from mere efficiency , to computational supremacy altogether.
After revision:  The SNN recognizes the frames more accurately, even if trained on few, still, and untransformed images, with unsupervised and synaptically-local learning, binary spikes, and a single layer of neurons - all in contrast to the deep-learning-trained ANNs . These results indicate that on-line adaptivity and spike-based computation may optimize natural intelligence for natural environments , from mere efficiency , to computational supremacy altogether.