Stark Eran, Drori Rotem, Abeles Moshe
Dept. of Physiology, Hadassah Medical School, The Hebrew University of Jerusalem, Jerusalem 91120, Israel.
J Neurophysiol. 2006 Mar;95(3):1966-75. doi: 10.1152/jn.00981.2005. Epub 2005 Nov 30.
A classical question in neuroscience is which features of a stimulus or of an action are represented in brain activity. When several features are interdependent either at a given point in time or at distinct points in time, neural activity related to one feature appears to be correlated with other features. Thus techniques that simultaneously consider multiple features cannot account for delayed interdependencies between features. The result is an ambiguity with respect to the encoded features. Here, we resolve this ambiguity by applying a novel statistical method based on partial cross-correlations. The method yields estimates of linear correlations between neural activity and a given feature that are not affected by linear correlations with other features at multiple time delays. The method also provides a graphical output measured on a scale that allows for comparisons between different features, neurons, and experiments. We use real movement data and neural activity simulated according to a wide range of tuning models to illustrate the method. When applied to real neural activity, the procedure yields results that indicate which of the considered features the neural activity is related to and at what time delays.
神经科学中的一个经典问题是，刺激或动作的哪些特征在大脑活动中得到体现。当几个特征在给定时间点或不同时间点相互依赖时，与一个特征相关的神经活动似乎与其他特征相关。因此，同时考虑多个特征的技术无法解释特征之间的延迟相互依赖关系。结果是在编码特征方面存在模糊性。在这里，我们通过应用一种基于部分互相关的新型统计方法来解决这种模糊性。该方法产生神经活动与给定特征之间线性相关性的估计值，这些估计值不受与其他特征在多个时间延迟下的线性相关性的影响。该方法还提供了一种按比例测量的图形输出，允许对不同特征、神经元和实验进行比较。我们使用根据各种调谐模型模拟的真实运动数据和神经活动来说明该方法。当应用于真实神经活动时，该过程产生的结果表明神经活动与所考虑的哪些特征相关以及在什么时间延迟。
