English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/84414
logo share SHARE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:

DC FieldValueLanguage
dc.contributor.authorPerez-Carrasco, J. A.-
dc.contributor.authorSerrano-Gotarredona, Teresa-
dc.contributor.authorLinares-Barranco, Bernabé-
dc.identifierdoi: 10.1109/ICPR.2010.756-
dc.identifierisbn: 978-1-4244-7542-1-
dc.identifier.citation20th International Conference on Pattern Recognition (ICPR): 3085-3088 (2010)-
dc.description.abstractIn this paper we propose the first bio-inspired six layer convolutional network (ConvNet) non-frame based that can be implemented with already physically available spike-based electronic devices. The system was designed to recognize people in three different positions: standing, lying or up-side down. The inputs were spikes obtained with a motion retina chip. We provide simulation results showing recognition delays of 16 milliseconds from stimulus onset (time-to-first spike) with a recognition rate of 94%. The weight sharing property in ConvNets and the use of AER protocol allow a great reduction in the number of both trainable parameters and connections (only 748 trainable parameters and 123 connections in our AER system (out of 506998 connections that would be required in a frame-based implementation).-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.titleSpike-based convolutional network for real-time processing-
dc.typecomunicación de congreso-
dc.description.versionPeer Reviewed-
Appears in Collections:(IMSE-CNM) Libros y partes de libros
Files in This Item:
File Description SizeFormat 
accesoRestringido.pdf15,38 kBAdobe PDFThumbnail
Show simple item record

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.