English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/121762
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

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

Title

Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network

AuthorsZhao, Bo; Ding, R.; Chen, Shoushun; Linares-Barranco, Bernabé ; Tang, H.
Issue Date2015
PublisherInstitute of Electrical and Electronics Engineers
CitationIEEE Transactions on Neural Networks and Learning Systems, 26(9): 1963-1978 (2015)
AbstractThis paper introduces an event-driven feedforward categorization system, which takes data from a temporal contrast address event representation (AER) sensor. The proposed system extracts bio-inspired cortex-like features and discriminates different patterns using an AER based tempotron classifier (a network of leaky integrate-and-fire spiking neurons). One of the system’s most appealing characteristics is its event-driven processing, with both input and features taking the form of address events (spikes). The system was evaluated on an AER posture dataset and compared with two recently developed bio-inspired models. Experimental results have shown that it consumes much less simulation time while still maintaining comparable performance. In addition, experiments on the Mixed National Institute of Standards and Technology (MNIST) image dataset have demonstrated that the proposed system can work not only on raw AER data but also on images (with a preprocessing step to convert images into AER events) and that it can maintain competitive accuracy even when noise is added. The system was further evaluated on the MNIST dynamic vision sensor dataset (in which data is recorded using an AER dynamic vision sensor), with testing accuracy of 88.14%.
Publisher version (URL)http://dx.doi.org/10.1109/TNNLS.2014.2362542
URIhttp://hdl.handle.net/10261/121762
DOI10.1109/TNNLS.2014.2362542
Appears in Collections:(IMSE-CNM) Artículos
Files in This Item:
File Description SizeFormat 
TNNLS-2014-P-3220.R1_final.pdf1,97 MBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 

Related articles:


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