Adjusting neuron models in neuromimetic ICs using the Differential Evolution algorithm

This paper presents an original method to adjust parameters for a neuromimetic IC based on neuron conductance-based models (Hodgkin-Huxley formalism). To adjust the chip, we use a Metaheuristic, the Differential Evolution algorithm (DE). We detail the DE for its implementation in our hardware neural simulator. The DE estimates in the same time all the parameters of one ionic channel. We discuss about the DE performance for each channel. We conclude by mentioning the future applications of this technique in chip design and neuron modeling.