Parameter Estimation of Two Spiking Neuron Models With Meta-Heuristic Optimization Algorithms - AbdelAty, Amr M.; Fouda, Mohammed E.; Eltawil, Ahmed (Frontiers in Neuroinformatics, 2022)
The automatic fitting of spiking neuron models to experimental data is a challenging problem. The integrate and fire model and Hodgkin--Huxley (HH) models represent the two complexity extremes of spiking neural models. Between these two extremes lies two and three differential-equation-based models. In this work, we investigate the problem of parameter estimation of two simple neuron models with a sharp reset in order to fit the spike timing of electro-physiological recordings based on two problem formulations. Five optimization algorithms are investigated; three of them have not been used to tackle this problem before. The new algorithms show improved fitting when compared with the old ones in both problems under investigation. The improvement in fitness function is between $5%$ and $8%$, which is achieved by using the new algorithms while also being more consistent between independent trials. Furthermore, the proposed problem formulation, referred to as Problem 1, achieves higher mean objective function values than the formulation reported in the literature, referred to as Problem 2, while using a lower number of search space variables.