• DocumentCode
    2152133
  • Title

    Remembering Key Features of Visual Images Based on Spike Timing Dependent Plasticity of Spiking Neurons

  • Author

    Wu, QingXiang ; Cai, Rongtai ; McGinnity, T.M. ; Maguire, Liam ; Harkin, Jim

  • Author_Institution
    Sch. of Phys. & Optoelectron. Technol., Fujian Normal Univ., Fuzhou, China
  • fYear
    2009
  • fDate
    17-19 Oct. 2009
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    The brain has the powerful capability of remembering key features of images. Based on the principle of spike timing dependent plasticity of spiking neurons and the ON/OFF pathways in the visual system, a spiking neural network is proposed to remember key features of visual images. The simulation results show that the network is capable of remembering key features according to a learning rule based on spike timing dependent plasticity. The principle of the network can be used to explain how a spiking neuron-based system can store the key features of visual images. Furthermore, the network can be applied to spiking neuron based artificial intelligent systems to support the processing visual images.
  • Keywords
    artificial intelligence; brain; neural nets; neurophysiology; visual perception; artificial intelligent systems; learning rule; spike timing dependent plasticity; spiking neural network; spiking neurons; visual images; Artificial intelligence; Biological neural networks; Brain modeling; Equations; Humans; Intelligent systems; Neurons; Retina; Timing; Visual system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image and Signal Processing, 2009. CISP '09. 2nd International Congress on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-1-4244-4129-7
  • Electronic_ISBN
    978-1-4244-4131-0
  • Type

    conf

  • DOI
    10.1109/CISP.2009.5303978
  • Filename
    5303978