title: 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.
authors.affiliation: University of Bordeaux, IMS, IPB, CNRS UMR 33405 Talence, France. laureb93@gmail.com
authors.forename: Laure
authors.initials: L
authors.lastname: Buhry
authors.forename: Filippo
authors.initials: F
authors.lastname: Grassia
authors.forename: Audrey
authors.initials: A
authors.lastname: Giremus
authors.forename: Eric
authors.initials: E
authors.lastname: Grivel
authors.forename: Sylvie
authors.initials: S
authors.lastname: Renaud
authors.forename: Sylvain
authors.initials: S
authors.lastname: Saïghi
mesh_terms: D000465:Algorithms; D000818:Animals; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*