• 08660483

    Accepted author manuscript, 2 MB, PDF-document


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 cuSNNlibrary, a framework that enables GPU-accelerated simulations of large-scale spiking neural networks. Source code and samples are available at
Original languageEnglish
Number of pages19
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Publication statusPublished - Mar 2019

    Research areas

  • Event-based vision, Feature extraction, Motion detection, Neural nets, Neuromorphic computing, Unsupervised learning

ID: 67552176