Automated Parameter Estimation of the Hodgkin-Huxley Model Using the Differential Evolution Algorithm: Application to Neuromimetic Analog Integrated Circuits

Abstract : We propose a new estimation method for the characterization of the Hodgkin-Huxley formalism. This method is an alternative technique to the classical estimation methods associated with voltage clamp measurements. It uses voltage clamp type recordings, but is based on the differential evolution algorithm. The parameters of an ionic channel are estimated simultaneously, such that the usual approximations of classical methods are avoided and all the parameters of the model, including the time constant, can be correctly optimized. In a second step, this new estimation technique is applied to the automated tuning of neuromimetic analog integrated circuits designed by our research group. We present a tuning example of a fast spiking neuron, which reproduces the frequency-current characteristics of the reference data, as well as the membrane voltage behavior. The final goal of this tuning is to interconnect neuromimetic chips as neural networks, with specific cellular properties, for future theoretical studies in neuroscience.
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Article dans une revue
Neural Computation, Massachusetts Institute of Technology Press (MIT Press), 2011, 23 (10), pp.2599-2625. 〈10.1162/NECO_a_00170.〉
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Contributeur : Chrystel Plumejeau <>
Soumis le : mercredi 21 septembre 2011 - 16:17:39
Dernière modification le : jeudi 11 janvier 2018 - 06:21:06

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Laure Buhry, Filippo Grassia, Audrey Giremus, Eric Grivel, Sylvie Renaud, et al.. Automated Parameter Estimation of the Hodgkin-Huxley Model Using the Differential Evolution Algorithm: Application to Neuromimetic Analog Integrated Circuits. Neural Computation, Massachusetts Institute of Technology Press (MIT Press), 2011, 23 (10), pp.2599-2625. 〈10.1162/NECO_a_00170.〉. 〈hal-00625448〉

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