  With the advent of neuromorphic hardware, spiking neural networks can be a
good energy-efficient alternative to artificial neural networks. However, the
use of spiking neural networks to perform computer vision tasks remains
limited, mainly focusing on simple tasks such as digit recognition. It remains
hard to deal with more complex tasks (e.g. segmentation, object detection) due
to the small number of works on deep spiking neural networks for these tasks.
The objective of this paper is to make the first step towards modern computer
vision with supervised spiking neural networks. We propose a deep convolutional
spiking neural network for the localization of a single object in a grayscale
image. We propose a network based on DECOLLE, a spiking model that enables
local surrogate gradient-based learning. The encouraging results reported on
Oxford-IIIT-Pet validates the exploitation of spiking neural networks with a
supervised learning approach for more elaborate vision tasks in the future.
