English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/121762
Compartir / Impacto:
Estadísticas
Add this article to your Mendeley library MendeleyBASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Título

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

AutorZhao, Bo; Ding, R.; Chen, Shoushun; Linares-Barranco, Bernabé ; Tang, H.
Fecha de publicación2015
EditorInstitute of Electrical and Electronics Engineers
CitaciónIEEE Transactions on Neural Networks and Learning Systems, 26(9): 1963-1978 (2015)
ResumenThis 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%.
Versión del editorhttp://dx.doi.org/10.1109/TNNLS.2014.2362542
URIhttp://hdl.handle.net/10261/121762
DOI10.1109/TNNLS.2014.2362542
Aparece en las colecciones: (IMSE-CNM) Artículos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
TNNLS-2014-P-3220.R1_final.pdf1,97 MBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo
 

Artículos relacionados:


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.