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Resonate-and-fire neurons.

E M Izhikevich1

  • 1The Neurosciences Institute, San Diego, CA 92121, USA.

Neural Networks : the Official Journal of the International Neural Network Society
|October 23, 2001
PubMed
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We introduce a resonate-and-fire neuron model, similar to integrate-and-fire but with complex states. This model explains biological neuron phenomena like resonant frequency preference and spike timing sensitivity, offering computational efficiency for large network simulations.

Area of Science:

  • Computational neuroscience
  • Mathematical modeling of neural systems

Background:

  • Biological neurons exhibit complex dynamics, including subthreshold oscillations.
  • Existing models like integrate-and-fire capture some neuronal behaviors but may not fully represent oscillatory phenomena.

Purpose of the Study:

  • To propose a simple spiking neuron model, the resonate-and-fire model, that incorporates complex state variables.
  • To demonstrate how this model can geometrically illustrate phenomena observed in biological neurons with subthreshold oscillations.
  • To highlight the model's utility in exploring neuronal sensitivity to precise spike train timing.

Main Methods:

  • Development of a resonate-and-fire neuron model with a complex state variable.
  • Analysis of model behavior to illustrate phenomena such as resonant frequency preference and doublet-induced excitation/inhibition.

Related Experiment Videos

  • Comparison of model properties with those observed in Hodgkin-Huxley-type models.

    • Main Results:

    • The resonate-and-fire model successfully illustrates subthreshold damped oscillations.
    • The model predicts neuronal preference for input frequencies near their eigenfrequency.
    • It explains how inter-spike intervals of input doublets can determine excitation or inhibition, and firing in response to inhibitory input.

    Conclusions:

    • The resonate-and-fire model offers a computationally efficient alternative to complex biophysical models for simulating large neural networks.
    • It provides geometric insights into neuronal responses to oscillatory inputs and precise spike timing.
    • The model's ability to reproduce phenomena seen in more complex models makes it valuable for studying neural computation.