dc.contributor.authorZhao, Bo
dc.contributor.authorChen, Shoushun
dc.contributor.authorTang, Huajin
dc.identifier.citationZhao, B., Chen, S., & Tang, H. (2014). Bio-inspired categorization using event-driven feature extraction and spike-based learning. 2014 International Joint Conference on Neural Networks (IJCNN), 3845-3852.en_US
dc.description.abstractThis paper presents a fully event-driven feedforward architecture that accounts for rapid categorization. The proposed algorithm processes the address event data generated either from an image or from Address-Event-Representation (AER) temporal contrast vision sensor. Bio-inspired, cortex-like, spikebased features are obtained through event-driven convolution and neural competition. The extracted spike feature patterns are then classified by a network of leaky integrate-and-fire (LIF) spiking neurons, in which the weights are trained using tempotron learning rule. One appealing characteristic of our system is the fully event-driven processing. The input, the features, and the classification are all based on address events (spikes). Experimental results on three datasets have proved the efficacy of the proposed algorithm.en_US
dc.format.extent8 p.en_US
dc.rights© 2014 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: [Article DOI: http://dx.doi.org/10.1109/IJCNN.2014.6889541].en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
dc.titleBio-inspired categorization using event-driven feature extraction and spike-based learningen_US
dc.typeConference Paper
dc.contributor.conference2014 International Joint Conference on Neural Networks (IJCNN)en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.versionAccepted versionen_US

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