Spiking neural networks for computer vision

Hopkins, Michael, Pineda-García, Garibaldi, Bogdan, Petruţ A and Fuber, Steve B (2018) Spiking neural networks for computer vision. Interface Focus, 8 (4). p. 20180007. ISSN 2042-8898

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State-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.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Informatics
Research Centres and Groups: Centre for Computational Neuroscience and Robotics
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics > TK7885 Computer engineering. Computer hardware
Depositing User: Garibaldi Pineda Garcia
Date Deposited: 21 Dec 2018 12:26
Last Modified: 02 Jul 2019 13:32
URI: http://sro.sussex.ac.uk/id/eprint/80939

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