rsfs.2018.0007.pdf (3.51 MB)
Spiking neural networks for computer vision
journal contribution
posted on 2023-06-09, 16:20 authored by Michael Hopkins, Garibaldi Pineda Garcia, Petrut A Bogdan, Steve B FuberState-of-the-art computer vision systems use frame-based cameras that sample the visual scene as a series of high-resolution images. These are then processed using convolutional neural networks using neurons with continuous outputs. Biological vision systems use a quite different approach, where the eyes (cameras) sample the visual scene continuously, often with a non-uniform resolution, and generate neural spike events in response to changes in the scene. The resulting spatio-temporal patterns of events are then processed through networks of spiking neurons. Such event-based processing offers advantages in terms of focusing constrained resources on the most salient features of the perceived scene, and those advantages should also accrue to engineered vision systems based upon similar principles. Event-based vision sensors, and event-based processing exemplified by the SpiNNaker (Spiking Neural Network Architecture) machine, can be used to model the biological vision pathway at various levels of detail. Here we use this approach to explore structural synaptic plasticity as a possible mechanism whereby biological vision systems may learn the statistics of their inputs without supervision, pointing the way to engineered vision systems with similar online learning capabilities.
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Publication status
- Published
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- Published version
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Interface FocusISSN
2042-8898Publisher
Royal SocietyExternal DOI
Issue
4Volume
8Page range
20180007Department affiliated with
- Informatics Publications
Research groups affiliated with
- Centre for Computational Neuroscience and Robotics Publications
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- Yes
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- Yes
Legacy Posted Date
2018-12-21First Open Access (FOA) Date
2018-12-21First Compliant Deposit (FCD) Date
2018-12-20Usage metrics
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