Parameter Estimation of Two Spiking Neuron Models With Meta-Heuristic Optimization Algorithms

Amr M AbdelAty; Mohammed E Fouda; Ahmed Eltawil

doi:10.3389/fninf.2022.771730

Parameter Estimation of Two Spiking Neuron Models With Meta-Heuristic Optimization Algorithms

Front Neuroinform. 2022 Feb 16:16:771730. doi: 10.3389/fninf.2022.771730. eCollection 2022.

Authors

Amr M AbdelAty^{1

2}, Mohammed E Fouda^{3

4}, Ahmed Eltawil²

Affiliations

¹ Engineering Mathematics and Physics Department, Faculty of Engineering, Fayoum University, Faiyum, Egypt.
² Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
³ Center for Embedded & Cyber-Physical Systems, University of California, Irvine, Irvine, CA, United States.
⁴ Nanoelectronics Integrated Systems Center (NISC), Nile University, Giza, Egypt.

Abstract

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, a new problem formulation is investigated that uses a lower number of search space variables when compared to the ones reported in related literature.

Keywords: adaptive exponential (AdEx) integrate and fire; cuckoo search optimizer; in-vitro data; leaky integrate and fire (LIF); marine predator algorithm; meta-heuristic optimization algorithms; spiking neuron model.