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 .
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 link short-term plasticity to high-level cortical function, suggest optimality of natural intelligence for natural environments .