Parameter estimation of the Hodgkin-Huxley model using metaheuristics: application to neuromimetic analog integrated circuits

In 1952 Hodgkin and Huxley introduced the voltage-clamp technique to extract the parameters of the ionic channel model of a neuron. Although this method is widely used today, it has a lot of disadvantages. In this paper, we propose an alternative approach to the estimation method of the voltage-clamp technique using metaheuristics such as Simulated Annealing, Genetic Algorithms and Differential Evolution. This method avoids approximations of the original technique by simultaneously estimating all the parameters of a single ionic channel with a single fitness function. To compare the different methods, we apply them on measurements from a neuromimetic integrated circuit. This circuit, due to its analog behavior, provides us noisy data like a biological system. Therefore we can validate the efficiency of our method on experimental-like data.