Sharpee T, Sugihara H, Kurgansky A V, Rebrik S, Stryker M P, Miller K D
Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, CA 94143-0444.
Proc SPIE Int Soc Opt Eng. 2004(5467):212-222. doi: 10.1117/12.548513.
One way to characterize neural feature selectivity is to model the response probability as a nonlinear function of the output of a set of linear filters applied to incoming signals. Traditionally these linear filters are measured by probing neurons with correlated Gaussian noise ensembles and calculating correlation functions between incoming signals and neural responses. It is also important to derive these filters in response to natural stimuli, which have been shown to have strongly non-Gaussian spatiotemporal correlations. An information-theoretic method has been proposed recently for reconstructing neural filters using natural stimuli in which one looks for filters whose convolution with the stimulus ensemble accounts for the maximal possible part of the overall information carried the sequence of neural responses. Here we give a first-time demonstration of this method on real neural data, and compare responses of neurons in cat primary visual cortex driven with natural stimuli, noise ensembles, and moving gratings. We show that the information-theoretic method achieves the same quality of filter reconstruction for natural stimuli as that of well-established white-noise methods. Major parameters of neural filters derived from noise ensembles and natural stimuli, as well as from moving gratings are consistent with one another. We find that application of the reverse correlation method to natural stimuli ensembles leads to significant distortions in filters for a majority of studied cells with non-zero reverse-correlation filter.
表征神经特征选择性的一种方法是将响应概率建模为应用于输入信号的一组线性滤波器输出的非线性函数。传统上，这些线性滤波器是通过用相关高斯噪声集合探测神经元并计算输入信号与神经响应之间的相关函数来测量的。同样重要的是，要根据自然刺激来推导这些滤波器，自然刺激已被证明具有强烈的非高斯时空相关性。最近有人提出了一种信息论方法，用于使用自然刺激来重建神经滤波器，即寻找与刺激集合卷积后能解释神经响应序列所携带的整体信息中最大可能部分的滤波器。在这里，我们首次在真实神经数据上演示了这种方法，并比较了猫初级视觉皮层中神经元在自然刺激、噪声集合和移动光栅驱动下的响应。我们表明，信息论方法在自然刺激下实现的滤波器重建质量与成熟的白噪声方法相同。从噪声集合、自然刺激以及移动光栅中推导出来的神经滤波器的主要参数彼此一致。我们发现，将反向相关方法应用于自然刺激集合会导致大多数具有非零反向相关滤波器的研究细胞的滤波器出现显著失真。
