Detection of Straight Lines Using a Spiking Neural Network Model

Qingxiang Wu, TM McGinnity, Liam Maguire, German Valderrama, Jianyong Cai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)
40 Downloads (Pure)

Abstract

Receptive fields of neurons play various rolesin biological neural networks. Based on a receptive field withthe function of Hough transform, a spiking neural networkmodel is proposed to detect straight lines in a visual image.Through the network, straight lines transform tocorresponding neurons with high firing rates in the outputneuron array. Simulation results show that straight lines canbe detected by the network and firing rates of thecorresponding neurons are referred to lengths of the lines. Thismodel can be used to explain how a spiking neuron-basednetwork can detect straight lines, and furthermore the modelcan be used in an artificial intelligent system.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE Xplore
Pages385-389
Number of pages5
Volume2
ISBN (Print)978-0-7695-3736-8
DOIs
Publication statusPublished - 28 Dec 2009
Event2009 Fifth International Conference on Natural Computation - Tianjian, China
Duration: 28 Dec 2009 → …

Conference

Conference2009 Fifth International Conference on Natural Computation
Period28/12/09 → …

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  • Cite this

    Wu, Q., McGinnity, TM., Maguire, L., Valderrama, G., & Cai, J. (2009). Detection of Straight Lines Using a Spiking Neural Network Model. In Unknown Host Publication (Vol. 2, pp. 385-389). IEEE Xplore. https://doi.org/10.1109/ICNC.2009.484