Using ODE waveform-relaxation methods to efficiently include gap junctions in distributed neural network simulations

TL;DR

This paper introduces a waveform-relaxation method for efficiently simulating gap junctions in distributed neural network models, improving accuracy and performance over traditional non-iterative approaches.

Contribution

It presents a novel waveform-relaxation approach tailored for gap junctions in neural simulations, enabling better parallelization and accuracy.

Findings

Improved simulation accuracy with the waveform-relaxation method.

Enhanced computational performance compared to non-iterative solutions.

Successful implementation in the NEST neural network simulator.

Abstract

Waveform-relaxation methods divide systems of differential equations into subsystems and therefore allow for parallelization across the system. Here we present an application for ODE waveform-relaxation methods in the context of spiking neural network simulators. Parallel spiking neural network simulators make use of the fact that the dynamics of neurons with chemical synapses is decoupled for the duration of the minimal network delay and thus can be solved independently for this duration. The inclusion of electrical synapses, so-called gap junctions, requires continuous interaction between neurons and therefore constitutes a conceptional problem for those simulators. We present a suitable waveform-relaxation method for an efficient integration of gap junctions and demonstrate that the use of the waveform-relaxation method improves both, accuracy and performance, compared to a non-iterative solution of the problem. We investigate the employed method in a reference implementation in the parallel spiking neural network simulator NEST.