Design Efficient Exponential Time Differencing method For Hodgkin-Huxley Neural Networks

TL;DR

This paper introduces an adaptive exponential time differencing (AETD2) method that significantly improves the efficiency and accuracy of simulating Hodgkin-Huxley neural networks, especially in stiff and pulse-coupled scenarios.

Contribution

The paper presents a novel second-order adaptive exponential time differencing algorithm (AETD2) that outperforms traditional methods in simulating stiff Hodgkin-Huxley neural networks.

Findings

AETD2 uses larger time steps than RK2, increasing efficiency.

AETD2 captures membrane potential traces accurately across regimes.

Efficiency improves by more than ten times compared to RK2.

Abstract

The exponential time differencing (ETD) method allows using a large time step to efficiently evolve the stiff system such as Hodgkin-Huxley (HH) neural networks. For pulse-coupled HH networks, the synaptic spike times cannot be predetermined and are convoluted with neuron's trajectory itself. This presents a challenging issue for the design of an efficient numerical simulation algorithm. The stiffness in the HH equations are quite different between the spike and non-spike regions. Here, we design a second-order adaptive exponential time differencing algorithm (AETD2) for the numerical evolution of HH neural networks. Compared with the regular second-order Runge-Kutta method (RK2), our AETD2 method can use time steps one order of magnitude larger and improve computational efficiency more than ten times while excellently capturing accurate traces of membrane potentials of HH neurons. This high accuracy and efficiency can be robustly obtained and do not depend on the dynamical regimes, connectivity structure or the network size.