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Communication Dans Un Congrès Année : 2021

Spike timing-based unsupervised learning of orientation, disparity, and motion representations in a spiking neural network

Thomas Barbier
Céline Teulière
Jochen Triesch
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Résumé

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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Dates et versions

hal-03614701 , version 1 (21-03-2022)

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Thomas Barbier, Céline Teulière, Jochen Triesch. Spike timing-based unsupervised learning of orientation, disparity, and motion representations in a spiking neural network. EEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Jun 2021, Virtual, France. ⟨10.1109/CVPRW53098.2021.00152⟩. ⟨hal-03614701⟩
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