  The combination of spiking neural networks and event-based vision sensors
holds the potential of highly efficient and high-bandwidth optical flow
estimation. This paper presents the first hierarchical spiking architecture in
which motion (direction and speed) selectivity emerges in an unsupervised
fashion from the raw stimuli generated with an event-based camera. A novel
adaptive neuron model and stable spike-timing-dependent plasticity formulation
are at the core of this neural network governing its spike-based processing and
learning, respectively. After convergence, the neural architecture exhibits the
main properties of biological visual motion systems, namely feature extraction
and local and global motion perception. Convolutional layers with input
synapses characterized by single and multiple transmission delays are employed
for feature and local motion perception, respectively; while global motion
selectivity emerges in a final fully-connected layer. The proposed solution is
validated using synthetic and real event sequences. Along with this paper, we
provide the cuSNN library, a framework that enables GPU-accelerated simulations
of large-scale spiking neural networks. Source code and samples are available
at https://github.com/tudelft/cuSNN.
