• DocumentCode
    1902193
  • Title

    Synaptic coding of periodically modulated spike trains

  • Author

    Segundo, J.P. ; Vibert, J.-F. ; Stiber, M. ; Hanneton, S.

  • Author_Institution
    Dept. of Anatomy & Cell Biol., California Univ., CA, USA
  • fYear
    1993
  • fDate
    1993
  • Firstpage
    58
  • Abstract
    The authors describe how periodically modulated pre-synaptic trains influence postsynaptic discharges in pacemaker neurons. Experiments are conducted on inhibitory synapses in crayfish. Descriptions are based upon point-process and dynamical systems theories. Pre- and postsynaptic discharges differ clearly. Local marked distortions are due to similar trends, special patterns, asymmetric sensitivity to change, and saturations. Discharges locally resemble locked, intermittent or messy forms. The pre- vs. post-synaptic contrast is sensitive to all modulation features. Synaptic coding is the operational unit in neural networks. It is asked if and how the performance of a net changes when, rather than including simple units, it has neurons and synapses closer to those examined here. Likewise necessary are a dynamic interpretation of the parallel between modulated and pacemaker drivings, and an understanding of the underlying mechanisms. Commonplace in nature are intercurrent perturbations. These are represented by noisy modulations. Average noisy cycles are distorted less than clean ones, suggesting that faithful synaptic codings may be achieved by averaging simultaneous noisy cycles across converging fibers
  • Keywords
    encoding; neural nets; pacemakers; perturbation techniques; asymmetric sensitivity; crayfish; dynamical systems; inhibitory synapses; intercurrent perturbations; modulation features; neural networks; noisy cycles; pacemaker neurons; periodically modulated spike trains; pre-synaptic trains; Biological cells; Computer science; Computer simulation; Frequency; Low-frequency noise; Modulation coding; Neural networks; Neurons; Noise generators; Pacemakers;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1993., IEEE International Conference on
  • Conference_Location
    San Francisco, CA
  • Print_ISBN
    0-7803-0999-5
  • Type

    conf

  • DOI
    10.1109/ICNN.1993.298531
  • Filename
    298531