Boďová Katarína, Paydarfar David, Forger Daniel B
Institute of Science and Technology Austria (IST Austria), Am Campus 1, Klosterneuburg A-3400, Austria.
Department of Neurology, University of Massachusetts Medical School, Worcester, MA, United States; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, United States.
J Theor Biol. 2015 Jan 21;365:40-54. doi: 10.1016/j.jtbi.2014.09.041. Epub 2014 Oct 12.
Understanding the dynamics of noisy neurons remains an important challenge in neuroscience. Here, we describe a simple probabilistic model that accurately describes the firing behavior in a large class (type II) of neurons. To demonstrate the usefulness of this model, we show how it accurately predicts the interspike interval (ISI) distributions, bursting patterns and mean firing rates found by: (1) simulations of the classic Hodgkin-Huxley model with channel noise, (2) experimental data from squid giant axon with a noisy input current and (3) experimental data on noisy firing from a neuron within the suprachiasmatic nucleus (SCN). This simple model has 6 parameters, however, in some cases, two of these parameters are coupled and only 5 parameters account for much of the known behavior. From these parameters, many properties of spiking can be found through simple calculation. Thus, we show how the complex effects of noise can be understood through a simple and general probabilistic model.
理解有噪声神经元的动态特性仍然是神经科学中的一项重要挑战。在此，我们描述了一个简单的概率模型，该模型能准确描述一大类（II型）神经元的放电行为。为证明该模型的实用性，我们展示了它如何准确预测通过以下方式得到的峰峰间隔（ISI）分布、爆发模式和平均放电率：（1）具有通道噪声的经典霍奇金 - 赫胥黎模型的模拟，（2）具有噪声输入电流的乌贼巨轴突的实验数据，以及（3）视交叉上核（SCN）内一个神经元的噪声放电实验数据。这个简单模型有6个参数，然而，在某些情况下，其中两个参数是耦合的，只有5个参数就能解释大部分已知行为。从这些参数中，通过简单计算就能找到许多放电特性。因此，我们展示了如何通过一个简单通用的概率模型来理解噪声的复杂影响。
