Global parameter estimation of an Hodgkin-Huxley formalism using membrane voltage recordings: Application to neuro-mimetic analog integrated circuits
Global parameter estimation of an Hodgkin-Huxley formalism using membrane voltage recordings: Application to neuro-mimetic analog integrated circuits
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DOI：
10.1016/j.neucom.2011.11.002
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发表时间：
2012-04-01
期刊：
影响因子：
6
通讯作者：
Saighi, Sylvain
中科院分区：
文献类型：
--
作者：
Buhry, Laure;Pace, Michele;Saighi, Sylvain
基于电导的生物神经元模型可以准确地再现膜电压的波形，以及响应注入电流的尖峰时序。然而，找到适合膜电压记录的良好模型参数集通常是一项非常耗时且复杂的任务，很难手动实现。我们提出了优化算法的一种新变体，即差分进化。我们专门设计了这种技术，用于基于点神经元模型的霍奇金-赫胥黎形式主义的神经模拟模拟集成电路的自动调谐。它确实使我们能够估计模型的所有参数，同时避免局部最小值。该方法首先在三种类型的神经元模型（快速尖峰、常规尖峰和本质爆发）上进行测试，然后应用于根据快速尖峰神经元模型的参考膜电压自动调整神经模拟电路。 (C) 2011 Elsevier B.V. 保留所有权利。
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. (C) 2011 Elsevier B.V. All rights reserved.