Before revision:  For the first time, a largely biologically modelled spiking neural network (SNN) surpasses state-of-the-art artificial neural networks (ANNs) in all relevant aspects, in an example taskof recognizing video frames transformed by moving occlusions. 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:  For the first time, a largely biologically modelled spiking neural network (SNN) surpasses state-of-the-art artificial neural networks in a visual task . These results indicate that on-line adaptivity and spike-based computation may optimize natural intelligence for natural environments .