Global parameter estimation of an Hodgkin-Huxley formalism using membrane voltage recordings: Application to neuro-mimetic analog integrated circuits

Laure Buhry; Michele Pace; Sylvain Saïghi

hal-00670411, version 1

Global parameter estimation of an Hodgkin-Huxley formalism using membrane voltage recordings: Application to neuro-mimetic analog integrated circuits

Laure Buhry () ¹, Michele Pace ², Sylvain Saïghi ³

Neurocomputing 81 (2012) 75-85

Abstract: Conductance-based models of biological neurons can accurately reproduce the waveform of the membrane voltage, as well as the spike timing in response to injected currents. Nevertheless, finding the good model parameter set to fit membrane voltage recordings is often a very time-consuming and complex task, difficult to achieve manually. We present a new variant of an optimization algorithm, the differential evolution. We specifically designed this technique for the automated tuning of neuro-mimetic analog integrated circuits based on an Hodgkin-Huxley formalism for a point-neuron model. It indeed enables us to estimate all the parameters of the model, while avoiding local minima. The method is first tested on three types of neuron models (fast spiking, regular spiking, and intrinsically bursting), and then applied to the automated tuning of a neuro-mimetic circuit from the reference membrane voltage of a fast spiking neuron model.

1: CORTEX (INRIA Nancy - Grand Est / LORIA)
INRIA – CNRS : UMR7503 – Université de Lorraine
2: Institut de Recherches interdisciplinaires et de Développements en Intelligence Artificielle [Bruxelles] (IRIDIA)
Université Libre de Bruxelles
3: Laboratoire de l'intégration, du matériau au système (IMS)
CNRS : UMR5218 – Université Sciences et Technologies - Bordeaux I – Institut Polytechnique de Bordeaux

hal-00670411, version 1
http://hal.archives-ouvertes.fr/hal-00670411
oai:hal.archives-ouvertes.fr:hal-00670411
From: Laure Buhry <>
Submitted on: Wednesday, 15 February 2012 13:31:53
Updated on: Tuesday, 13 November 2012 16:08:15

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DOI: 10.1016/j.neucom.2011.11.002

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%% hal-00670411, version 1
%% http://hal.archives-ouvertes.fr/hal-00670411
@article{buhry:hal-00670411,
    hal_id = {hal-00670411},
    url = {http://hal.archives-ouvertes.fr/hal-00670411},
    title = {{Global parameter estimation of an Hodgkin-Huxley formalism using membrane voltage recordings: Application to neuro-mimetic analog integrated circuits}},
    author = {Buhry, Laure and Pace, Michele and Sa{\"\i}ghi, Sylvain},
    abstract = {{Conductance-based models of biological neurons can accurately reproduce the waveform of the membrane voltage, as well as the spike timing in response to injected currents. Nevertheless, finding the good model parameter set to fit membrane voltage recordings is often a very time-consuming and complex task, difficult to achieve manually. We present a new variant of an optimization algorithm, the differential evolution. We specifically designed this technique for the automated tuning of neuro-mimetic analog integrated circuits based on an Hodgkin-Huxley formalism for a point-neuron model. It indeed enables us to estimate all the parameters of the model, while avoiding local minima. The method is first tested on three types of neuron models (fast spiking, regular spiking, and intrinsically bursting), and then applied to the automated tuning of a neuro-mimetic circuit from the reference membrane voltage of a fast spiking neuron model.}},
    keywords = {Hodgkin-Huxley formalism; Parameter estimation; Global optimization; Variant of the differential evolution (DE) algorithm; Membrane voltage},
    language = {English},
    affiliation = {CORTEX - INRIA Nancy - Grand Est / LORIA , Institut de Recherches interdisciplinaires et de D{\'e}veloppements en Intelligence Artificielle [Bruxelles] - IRIDIA , Laboratoire de l'int{\'e}gration, du mat{\'e}riau au syst{\`e}me - IMS},
    pages = {75-85},
    journal = {Neurocomputing},
    volume = {81},
    audience = {international },
    doi = {10.1016/j.neucom.2011.11.002 },
    year = {2012},
    month = Apr,
}

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