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Content Provider | IEEE Xplore Digital Library |
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Author | Orchard, G. Lagorce, X. Posch, C. Furber, S. Benosman, R. Galluppi, F. |
Copyright Year | 2015 |
Description | Author affiliation: Adv. Processor Technol. Group, Univ. of Manchester, Manchester, UK (Furber, S.) || Singapore Inst. for Neurotechnology (SINAPSE), Nat. Univ. of Singapore, Singapore, Singapore (Orchard, G.) || Inst. de la Vision, UPMC Univ. Paris 06, Paris, France (Lagorce, X.; Posch, C.; Benosman, R.; Galluppi, F.) |
Abstract | This live demonstration shows real-time visual object recognition based on a spiking neural network adaptation of the HMAX model running on a purely event-based computational hardware platform. Visual input to the system is provided by an ATIS spiking silicon retina sensor. A SpiNNaker board processes the event-encoded visual information from the scene. Using a Leaky Integrate-and-Fire (LIF) neuron model implemented on SpiNNaker, an event-driven, multi-layer network is created that performs real-time orientation extraction and recombination. In this demonstration, the network will be tuned to recognize complex objects such as printed characters. |
Starting Page | 1903 |
Ending Page | 1903 |
File Size | 685529 |
Page Count | 1 |
File Format | |
ISBN | 9781479983919 |
DOI | 10.1109/ISCAS.2015.7169036 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2015-05-24 |
Publisher Place | Portugal |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Visualization Neurons Real-time systems Object recognition Adaptation models Computational modeling Data visualization |
Content Type | Text |
Resource Type | Article |
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