Foveal-pit inspired filtering of DVS spike response

TL;DR

This paper introduces a foveal-pit inspired filtering approach for processing DVS sensor data, combining biologically inspired receptive fields with spiking neural networks for visual pattern classification.

Contribution

It presents a novel retinal model based on foveal-pit inspired DoG filters applied to DVS data, integrated with a spiking CNN for classification.

Findings

Effective extraction of visual features from DVS data

Successful classification using spiking neural networks

Biologically inspired model enhances processing of dynamic visual stimuli

Abstract

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.