Spike Timing-Based Unsupervised Learning of Orientation, Disparity, and Motion Representations in a Spiking Neural Network

Thomas Barbier, Celine Teuliere, Jochen Triesch; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 1377-1386

Abstract


Neuromorphic vision sensors present unique advantages over their frame based counterparts. However, unsupervised learning of efficient visual representations from their asynchronous output is still a challenge, requiring a rethinking of traditional image and video processing methods. Here we present a network of leaky integrate and fire neurons that learns representations similar to those of simple and complex cells in the primary visual cortex of mammals from the input of two event-based vision sensors. Through the combination of spike timing-dependent plasticity and homeostatic mechanisms, the network learns visual feature detectors for orientation, disparity, and motion in a fully unsupervised fashion. We validate our approach on a mobile robotic platform.

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[bibtex]
@InProceedings{Barbier_2021_CVPR, author = {Barbier, Thomas and Teuliere, Celine and Triesch, Jochen}, title = {Spike Timing-Based Unsupervised Learning of Orientation, Disparity, and Motion Representations in a Spiking Neural Network}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {1377-1386} }