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Title: Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network
Authors: Zhao, Bo
Ding, Ruoxi
Chen, Shoushun
Linares-Barranco, Bernabe
Tang, Huajin
Keywords: MNIST
Spiking neural network
Event driven
Feedforward categorization
Address event representation (AER)
Issue Date: 2014
Source: Zhao, B., Ding, R., Chen, S., Linares-Barranco, B., & Tang, H. (2015). Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network. IEEE Transactions on Neural Networks and Learning Systems, 26(9), 1963-1978.
Series/Report no.: IEEE Transactions on Neural Networks and Learning Systems
Abstract: This 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 to 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-DVS dataset (in which data is recorded using an AER dynamic vision sensor), with testing accuracy of 88.14%.
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2014.2362542
Schools: School of Electrical and Electronic Engineering 
Rights: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
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