Mitsis Georgios D, French Andrew S, Höger Ulli, Courellis Spiros, Marmarelis Vasilis Z
Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
Biol Cybern. 2007 Jan;96(1):113-27. doi: 10.1007/s00422-006-0108-2. Epub 2006 Oct 5.
The encoding of mechanical stimuli into action potentials in two types of spider mechanoreceptor neurons is modeled by use of the principal dynamic modes (PDM) methodology. The PDM model is equivalent to the general Wiener-Bose model and consists of a minimum set of linear dynamic filters (PDMs), followed by a multivariate static nonlinearity and a threshold function. The PDMs are obtained by performing eigen-decomposition of a matrix constructed using the first-order and second-order Volterra kernels of the system, which are estimated by means of the Laguerre expansion technique, utilizing measurements of pseudorandom mechanical stimulation (input signal) and the resulting action potentials (output signal). The static nonlinearity, which can be viewed as a measure of the probability of action potential firing as a function of the PDM output values, is computed as the locus of points of the latter that correspond to output action potentials. The performance of the model is assessed by computing receiver operating characteristic (ROC) curves, akin to the ones used in decision theory and quantified by computing the area under the ROC curve. Three PDMs are revealed by the analysis. The first PDM exhibits a high-pass characteristic, illustrating the importance of the velocity of slit displacement in the generation of action potentials at the mechanoreceptor output, while the second and third PDMs exhibit band-pass and low-pass characteristics, respectively. The corresponding three-input nonlinearity exhibits asymmetric behavior with respect to its arguments, suggesting directional dependence of the mechanoreceptor response on the mechanical stimulation and the PDM outputs, in agreement to our findings from a previous study (Ann Biomed Eng 27:391-402, 1999). Differences between the Type A and B neurons are observed in the zeroth-order Volterra kernels (related to the average firing), as well as in the magnitudes of the second and third PDMs that perform band-pass and low-pass processing of the input signal, respectively.
利用主动态模式(PDM)方法对两类蜘蛛机械感受器神经元中机械刺激编码为动作电位的过程进行了建模。PDM模型等同于一般的维纳 - 玻色模型,由一组最小的线性动态滤波器(PDM)、一个多元静态非线性函数和一个阈值函数组成。通过对使用系统的一阶和二阶沃尔泰拉核构建的矩阵进行特征分解来获得PDM,这些核通过拉盖尔展开技术进行估计,利用伪随机机械刺激(输入信号)的测量值和由此产生的动作电位(输出信号)。静态非线性函数可被视为动作电位发放概率作为PDM输出值的函数的一种度量,它被计算为与输出动作电位相对应的后者的点的轨迹。通过计算类似于决策理论中使用的接收器操作特征(ROC)曲线并计算ROC曲线下的面积来量化,评估模型的性能。分析揭示了三个PDM。第一个PDM呈现高通特性,说明了狭缝位移速度在机械感受器输出端动作电位产生中的重要性,而第二个和第三个PDM分别呈现带通和低通特性。相应的三输入非线性函数相对于其自变量表现出不对称行为,表明机械感受器响应在机械刺激和PDM输出方面存在方向依赖性,这与我们先前研究(《生物医学工程年报》27:391 - 402, 1999)的发现一致。在零阶沃尔泰拉核(与平均发放有关)以及分别对输入信号进行带通和低通处理的第二和第三个PDM的幅度方面,观察到了A类和B类神经元之间的差异。