• DocumentCode
    3059700
  • Title

    Effect of synaptic weight assignment on spiking neuron response

  • Author

    Fujii, Robert H. ; Ichishita, Taiki

  • Author_Institution
    Univ. of Aizu, Aizu-Wakamatsu
  • fYear
    2007
  • fDate
    13-15 Dec. 2007
  • Firstpage
    217
  • Lastpage
    222
  • Abstract
    Three synaptic weight assignment schemes were proposed and their effect on the behavior of a spiking neuron was analyzed. To evaluate the proposed synaptic assignment schemes, a feed-forward Spiking Neural Network that can learn to recognize temporal sequences was proposed. This spiking neural network uses two of the proposed synaptic weight assignment schemes in conjunction with a spiking neuron model that uses a simplified linear soma potential function. The robustness and reliability of the proposed spiking neural network system were shown by the high (approximately 99%) temporal sequence recognition rate achieved during testing. Practical hardware implementation issues were also discussed.
  • Keywords
    feedforward neural nets; feedforward spiking neural network; linear soma potential function; spiking neuron response; synaptic weight assignment; Artificial neural networks; Biological information theory; Biological neural networks; Biological system modeling; Intersymbol interference; Nerve fibers; Neural networks; Neurofeedback; Neurons; Recurrent neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Applications, 2007. ICMLA 2007. Sixth International Conference on
  • Conference_Location
    Cincinnati, OH
  • Print_ISBN
    978-0-7695-3069-7
  • Type

    conf

  • DOI
    10.1109/ICMLA.2007.16
  • Filename
    4457234