Simulation of Visual Attention using Hierarchical Spiking Neural Networks

Qingxiang Wu, TM McGinnity, LP Maguire, Rongtai Cai, Meigui Chen

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

2 Citations (Scopus)

Abstract

Based on the information processing functionalities of spiking neurons, a hierarchical spiking neural network model is proposed to simulate visual attention. The network is constructed with a conductance-based integrate-and-fire neuron model and a set of specific receptive fields in different levels. The simulation algorithm and properties of the network are detailed in this paper. Simulation results show that the network is able to perform visual attention to extract objects based on specific image features. Using extraction of horizontal and vertical lines, a demonstration shows how the network can detect a house in a visual image. Using this visual attention principle, many other objects can be extracted by analogy.
Original languageEnglish
Title of host publicationUnknown Host Publication
Place of PublicationBerlin Heidelberg
PublisherSpringer
Pages26-31
Number of pages6
Volume6840
ISBN (Print)978-3-642-24552-7
Publication statusPublished - 4 Oct 2011
EventInternational Conference on Interlligent Computing (ICIC 2011) -
Duration: 4 Oct 2011 → …

Conference

ConferenceInternational Conference on Interlligent Computing (ICIC 2011)
Period4/10/11 → …

Keywords

  • Visual attention
  • spiking neural network
  • receptive field
  • visual system.

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  • Cite this

    Wu, Q., McGinnity, TM., Maguire, LP., Cai, R., & Chen, M. (2011). Simulation of Visual Attention using Hierarchical Spiking Neural Networks. In Unknown Host Publication (Vol. 6840, pp. 26-31). Berlin Heidelberg: Springer.