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A general and efficient method for incorporating precise spike times in globally time-driven simulations.

Alexander Hanuschkin1, Susanne Kunkel, Moritz Helias

  • 1Functional Neural Circuits Group, Faculty of Biology, Albert-Ludwig University of Freiburg Freiburg im Breisgau, Germany.

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Summary

Globally time-driven simulations accurately calculate neuronal firing times, outperforming event-driven methods in computational efficiency. This approach simplifies spike prediction, making it broadly applicable to integrate-and-fire neuron models.

Keywords:
accuracyevent drivennon-linear neuron modelsprecise spike timestime driven

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Area of Science:

  • Computational neuroscience
  • Neuronal modeling
  • Simulation techniques

Background:

  • Event-driven simulations traditionally require closed-form spike timing prediction.
  • Recent advances extended event-driven methods to some integrate-and-fire models.
  • Time-driven simulations are often perceived as imprecise.

Purpose of the Study:

  • To demonstrate the precision and efficiency of a globally time-driven simulation scheme.
  • To compare time-driven and event-driven simulation methods for neuronal models.
  • To show the broad applicability of the proposed time-driven method.

Main Methods:

  • Developed a globally time-driven simulation scheme.
  • Implemented retrospective detection of threshold crossings.
  • Compared results with event-driven implementations for various integrate-and-fire models.
  • Utilized a standard adaptive solver for a non-linear model.

Main Results:

  • The time-driven scheme achieved firing times indistinguishable from event-driven methods.
  • The time-driven approach demonstrated lower computational costs.
  • The method is applicable to commonly used integrate-and-fire neuronal models.
  • A non-linear model reproduced a reference spike train with high precision.

Conclusions:

  • Globally time-driven simulations offer a precise and computationally efficient alternative to event-driven methods.
  • Retrospective detection simplifies algorithms compared to future spike prediction.
  • The proposed time-driven method is versatile for various neuronal models.