The Parker-Sochacki method is a new technique for the numerical integration of differential equations applicable to many neuronal models. Using this method, the solution order can be adapted according to the local conditions at each time step, enabling adaptive error control without changing the integration timestep. We apply the Parker-Sochacki method to the Izhikevich ‘simple’ model and a Hodgkin-Huxley type neuron, comparing the results with those obtained using the Runge-Kutta and Bulirsch-Stoer methods.
Model Type: Realistic Network
Cell Type(s): Hodgkin-Huxley neuron
Model Concept(s): Simplified Models; Detailed Neuronal Models; Methods
Simulation Environment: C or C++ program; MATLAB
Implementer(s): Stewart, Robert [Robert.Stewart at pharm.ox.ac.uk]
References:
Stewart RD, Bair W. (2009). Spiking neural network simulation: numerical integration with the Parker-Sochacki method. Journal of computational neuroscience 27 [PubMed]