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

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.

Bounded-Error Parameter Estimation Using Integro-Differential Equations for Hindmarsh–Rose Model

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The impact of Hodgkin–Huxley models on dendritic research

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Parameter estimation of harmonics arising in electrical instruments of smart grids using cuckoo search heuristics

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