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Related Experiment Video

Updated: Jun 28, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Accelerating event-driven simulation of spiking neurons with multiple synaptic time constants.

Michiel D'Haene¹, Benjamin Schrauwen, Jan Van Campenhout

¹Ghent University, Electronics and Information Systems Department, 9000 Ghent, Belgium. michiel.dhaene@ugent.be

Neural Computation

|October 22, 2008

Summary

Simulating complex spiking neural networks (SNNs) is faster with a new event-driven algorithm. This method avoids exact firing time calculations, enabling efficient simulation of detailed neuron models.

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Area of Science:

Computational Neuroscience
Artificial Intelligence

Background:

Simulating spiking neural networks (SNNs) is computationally intensive, limiting model size and complexity.
Existing event-driven simulation strategies struggle with complex neuron models due to the high cost of evaluating firing times.

Purpose of the Study:

To develop a more efficient simulation strategy for SNNs, particularly for complex neuron models.
To overcome the limitations of current event-driven methods in handling multiple state variables like synaptic time constants.

Main Methods:

Proposed an event-driven simulation algorithm that does not require exact firing time prediction.
Developed techniques to minimize the computational effort in predicting firing times.
Introduced an algorithm for simulating leaky integrate-and-fire (LIF) neurons with multiple synaptic time constants.

Main Results:

Achieved very high simulation speeds for LIF neurons with arbitrary numbers of synaptic time constants.
Demonstrated that exact firing time prediction is not necessary for accurate simulation results.
The algorithm's performance is independent of the neuron model's complexity.

Conclusions:

The novel algorithm significantly enhances the efficiency of SNN simulations.
This approach enables the simulation of larger and more complex SNNs in reasonable timeframes.
The method provides a scalable solution for simulating biologically realistic neural models.