  In real world scenarios, objects are often partially occluded. This requires
a robustness for object recognition against these perturbations. Convolutional
networks have shown good performances in classification tasks. The learned
convolutional filters seem similar to receptive fields of simple cells found in
the primary visual cortex. Alternatively, spiking neural networks are more
biological plausible. We developed a two layer spiking network, trained on
natural scenes with a biologically plausible learning rule. It is compared to
two deep convolutional neural networks using a classification task of stepwise
pixel erasement on MNIST. In comparison to these networks the spiking approach
achieves good accuracy and robustness.
