• DocumentCode
    1503101
  • Title

    Class 1 neural excitability, conventional synapses, weakly connected networks, and mathematical foundations of pulse-coupled models

  • Author

    Izhikevich, Eugene M.

  • Author_Institution
    Center for Syst. Sci. & Eng., Arizona State Univ., Tempe, AZ, USA
  • Volume
    10
  • Issue
    3
  • fYear
    1999
  • fDate
    5/1/1999 12:00:00 AM
  • Firstpage
    499
  • Lastpage
    507
  • Abstract
    Many scientists believe that all pulse-coupled neural networks are toy models that are far away from the biological reality. We show, however, that a huge class of biophysically detailed and biologically plausible neural-network models can be transformed into a canonical pulse-coupled form by a piece-wise continuous, possibly noninvertible, change of variables. Such transformations exist when a network satisfies a number of conditions; e,g., it is weakly connected; the neurons are Class 1 excitable (i.e., they can generate action potentials with an arbitrary small frequency); and the synapses between neurons are conventional (i.e., axo-dendritic and axe-somatic). Thus, the difference between studying the pulse-coupled model and Hodgkin-Huxley-type neural networks is just a matter of a coordinate change. Therefore, any piece of information about the pulse-coupled model is valuable since it tells something about all weakly connected networks of Class 1 neurons. For example, we show that the pulse-coupled network of identical neurons does not synchronize in-phase. This confirms Ermentrout´s (1996) result that weakly connected Class 1 neurons are difficult to synchronize, regardless of the equations that describe dynamics of each cell
  • Keywords
    bifurcation; dynamics; neural nets; neurophysiology; physiological models; Class 1 neural excitability; Hodgkin-Huxley-type neural networks; biologically plausible neural-network models; conventional synapses; pulse-coupled models; pulse-coupled network; pulse-coupled neural networks; weakly connected networks; Bifurcation; Biological neural networks; Biological system modeling; Equations; Frequency synchronization; Limit-cycles; Mathematical model; Neural networks; Neurons; Neurotransmitters;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.761707
  • Filename
    761707