Fishberg Department of Neuroscience, Mount Sinai School of Medicine, Box 1065, One Gustave L. Levy Place, New York, NY 10029, United States.
J Neurosci Methods. 2009 Nov 15;184(2):337-56. doi: 10.1016/j.jneumeth.2009.07.034. Epub 2009 Aug 18.
Many physiological responses elicited by neuronal spikes-intracellular calcium transients, synaptic potentials, muscle contractions-are built up of discrete, elementary responses to each spike. However, the spikes occur in trains of arbitrary temporal complexity, and each elementary response not only sums with previous ones, but can itself be modified by the previous history of the activity. A basic goal in system identification is to characterize the spike-response transform in terms of a small number of functions-the elementary response kernel and additional kernels or functions that describe the dependence on previous history-that will predict the response to any arbitrary spike train. Here we do this by developing further and generalizing the "synaptic decoding" approach of Sen et al. (1996). Given the spike times in a train and the observed overall response, we use least-squares minimization to construct the best estimated response and at the same time best estimates of the elementary response kernel and the other functions that characterize the spike-response transform. We avoid the need for any specific initial assumptions about these functions by using techniques of mathematical analysis and linear algebra that allow us to solve simultaneously for all of the numerical function values treated as independent parameters. The functions are such that they may be interpreted mechanistically. We examine the performance of the method as applied to synthetic data. We then use the method to decode real synaptic and muscle contraction transforms.
许多由神经元尖峰引起的生理反应——细胞内钙瞬变、突触电位、肌肉收缩——都是由每个尖峰引起的离散的基本反应构成的。然而，尖峰以任意时间复杂度的序列形式出现，每个基本反应不仅与前一个反应相加，而且自身也可以被活动的先前历史所改变。在系统识别中，一个基本目标是根据少量的函数来描述尖峰反应变换——基本反应核和描述先前历史依赖性的附加核或函数——这些函数将预测对任何任意尖峰序列的反应。在这里，我们通过进一步发展和推广 Sen 等人的“突触解码”方法来实现这一点。（1996）。给定序列中的尖峰时间和观察到的整体反应，我们使用最小二乘法来构建最佳估计反应，同时最佳估计基本反应核和其他特征尖峰反应变换的函数。我们通过使用数学分析和线性代数技术来避免对这些函数进行任何特定的初始假设，这些技术允许我们同时求解所有被视为独立参数的数值函数值。这些函数可以从机械的角度进行解释。我们检查该方法应用于合成数据的性能。然后，我们使用该方法对真实的突触和肌肉收缩变换进行解码。
