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首页> 外文期刊>Journal of Computational Neuroscience >Parameter estimation for a leaky integrate-and-fire neuronal model from ISI data
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Parameter estimation for a leaky integrate-and-fire neuronal model from ISI data

机译：基于ISI数据的泄漏集成点火神经元模型的参数估计

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摘要

The Ornstein-Uhlenbeck process has been proposed as a model for the spontaneous activity of a neuron. In this model, the firing of the neuron corresponds to the first passage of the process to a constant boundary, or threshold. While the Laplace transform of the first-passage time distribution is available, the probability distribution function has not been obtained in any tractable form. We address the problem of estimating the parameters of the process when the only available data from a neuron are the interspike intervals, or the times between firings. In particular, we give an algorithm for computing maximum likelihood estimates and their corresponding confidence regions for the three identifiable (of the five model) parameters by numerically inverting the Laplace transform. A comparison of the two-parameter algorithm (where the time constant x is known a priori) to the three-parameter algorithm shows that significantly more data is required in the latter case to achieve comparable parameter resolution as measured by 95% confidence intervals widths. The computational methods described here are a efficient alternative to other well known estimation techniques for leaky integrate-and-fire models. Moreover, it could serve as a template for performing parameter inference on more complex integrate-and-fire neuronal models.
机译：已经提出了Ornstein-Uhlenbeck过程作为神经元自发活动的模型。在此模型中，神经元的激发对应于过程的第一阶段，即恒定的边界或阈值。虽然可以使用第一遍时间分布的拉普拉斯变换，但尚未以任何易于处理的形式获得概率分布函数。当来自神经元的唯一可用数据是尖峰间隔或触发之间的时间时，我们解决了估计过程参数的问题。特别是，我们给出了一种算法，可通过对Laplace变换进行数值求逆来计算（五个模型中的）三个可识别参数的最大似然估计及其对应的置信区域。将两参数算法（时间常数x先验已知）与三参数算法的比较表明，在后一种情况下，要获得可比较的参数分辨率（由95％置信区间宽度测量），需要大量数据。此处描述的计算方法是其他已知的泄漏集成点火模型估算技术的有效替代方案。而且，它可以作为在更复杂的集成和发射神经元模型上执行参数推断的模板。

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