• DocumentCode
    1749175
  • Title

    Neural computations in the vertebrate retina and an analysis of GABA action from horizontal cells

  • Author

    Yang, Simon X. ; Ogmen, Haluk ; Maguire, Greg

  • Author_Institution
    Sch. of Eng., Guelph Univ., Ont., Canada
  • Volume
    2
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    826
  • Abstract
    A neural network architecture, based on the neural anatomy and function of retinal neurons in tiger salamander and mudpuppy retinae, is proposed to study some basic aspects of early visual information processing. All the main types of retinal neurons are modeled, and their response characteristics are studied. The model predictions on the main characteristics of retinal neurons are in agreement with the neurophysiological data, including the antagonistic role of horizontal cells in the outer plexiform layer, and the sustained and transient responses of amacrine cells in the inner plexiform layer. The examination of possible γ-aminobutyric acid (GABA) action from horizontal cells suggests that GABAA alone, GABAB alone, or their weighted combination can generate the response characteristics observed in bipolar cells
  • Keywords
    electroretinography; neural nets; neurophysiology; physiological models; γ-aminobutyric acid action; GABA action; GABAA; GABAB; amacrine cells; antagonistic role; bipolar cells; early visual information processing; horizontal cells; model predictions; mudpuppy; neural anatomy; neural computations; neural network architecture; plexiform layer; response characteristics; retinal neurons; tiger salamander; transient responses; vertebrate retina; weighted combination; Anatomy; Character generation; Computer architecture; Neural networks; Neurons; Photoreceptors; Predictive models; Retina; Transmitters; Visual system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7044-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2001.939466
  • Filename
    939466