Neural Comput - Automated parameter estimation of the Hodgkin-Huxley model using the differential evolution algorithm: application to neuromimetic analog integrated circuits.

Tópicos

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Resumo

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.

Resumo Limpo

propos new estim method character hodgkinhuxley formal method altern techniqu classic estim method associ voltag clamp measur use voltag clamp type record base differenti evolut algorithm paramet ionic channel estim simultan usual approxim classic method avoid paramet model includ time constant can correct optim second step new estim techniqu appli autom tune neuromimet analog integr circuit design research group present tune exampl fast spike neuron reproduc frequencycurr characterist refer data well membran voltag behavior final goal tune interconnect neuromimet chip neural network specif cellular properti futur theoret studi neurosci

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