Kiessling Lilli, Lindner Benjamin
Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, Haus 2, 10115, Berlin, Germany.
Physics Department of Technische, Universit Berlin, Hardenbergstr. 36, 10623, Berlin, Germany.
Biol Cybern. 2024 Dec 30;119(1):2. doi: 10.1007/s00422-024-01000-2.
Integrate-and-fire models are an important class of phenomenological neuronal models that are frequently used in computational studies of single neural activity, population activity, and recurrent neural networks. If these models are used to understand and interpret electrophysiological data, it is important to reliably estimate the values of the model's parameters. However, there are no standard methods for the parameter estimation of Integrate-and-fire models. Here, we identify the model parameters of an adaptive integrate-and-fire neuron with temporally correlated noise by analyzing membrane potential and spike trains in response to a current step. Explicit formulas for the parameters are analytically derived by stationary and time-dependent ensemble averaging of the model dynamics. Specifically, we give mathematical expressions for the adaptation time constant, the adaptation strength, the membrane time constant, and the mean constant input current. These theoretical predictions are validated by numerical simulations for a broad range of system parameters. Importantly, we demonstrate that parameters can be extracted by using only a modest number of trials. This is particularly encouraging, as the number of trials in experimental settings is often limited. Hence, our formulas may be useful for the extraction of effective parameters from neurophysiological data obtained from standard current-step experiments.
积分发放模型是一类重要的现象学神经元模型，常用于单个神经活动、群体活动和递归神经网络的计算研究。如果使用这些模型来理解和解释电生理数据，可靠地估计模型参数的值很重要。然而，对于积分发放模型的参数估计没有标准方法。在这里，我们通过分析响应电流阶跃的膜电位和脉冲序列，确定了具有时间相关噪声的自适应积分发放神经元的模型参数。通过对模型动力学进行稳态和时间相关的系综平均，解析推导了参数的显式公式。具体来说，我们给出了适应时间常数、适应强度、膜时间常数和平均恒定输入电流的数学表达式。这些理论预测通过对广泛的系统参数进行数值模拟得到了验证。重要的是，我们证明仅使用少量试验就可以提取参数。这尤其令人鼓舞，因为实验设置中的试验次数通常是有限的。因此，我们的公式可能有助于从标准电流阶跃实验获得的神经生理数据中提取有效参数。
