  In this paper, we present results of processing Dynamic Vision Sensor (DVS)
recordings of visual patterns with a retinal model based on foveal-pit inspired
Difference of Gaussian (DoG) filters. A DVS sensor was stimulated with varying
number of vertical white and black bars of different spatial frequencies moving
horizontally at a constant velocity. The output spikes generated by the DVS
sensor were applied as input to a set of DoG filters inspired by the receptive
field structure of the primate visual pathway. In particular, these filters
mimic the receptive fields of the midget and parasol ganglion cells (spiking
neurons of the retina) that sub-serve the photo-receptors of the foveal-pit.
The features extracted with the foveal-pit model are used for further
classification using a spiking convolutional neural network trained with a
backpropagation variant adapted for spiking neural networks.
