Latham Peter E, Nirenberg Sheila
Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR, United Kingdom.
J Neurosci. 2005 May 25;25(21):5195-206. doi: 10.1523/JNEUROSCI.5319-04.2005.
Decoding the activity of a population of neurons is a fundamental problem in neuroscience. A key aspect of this problem is determining whether correlations in the activity, i.e., noise correlations, are important. If they are important, then the decoding problem is high dimensional: decoding algorithms must take the correlational structure in the activity into account. If they are not important, or if they play a minor role, then the decoding problem can be reduced to lower dimension and thus made more tractable. The issue of whether correlations are important has been a subject of heated debate. The debate centers around the validity of the measures used to address it. Here, we evaluate three of the most commonly used ones: synergy, DeltaI(shuffled), and DeltaI. We show that synergy and DeltaI(shuffled) are confounded measures: they can be zero when correlations are clearly important for decoding and positive when they are not. In contrast, DeltaI is not confounded. It is zero only when correlations are not important for decoding and positive only when they are; that is, it is zero only when one can decode exactly as well using a decoder that ignores correlations as one can using a decoder that does not, and it is positive only when one cannot decode as well. Finally, we show that DeltaI has an information theoretic interpretation; it is an upper bound on the information lost when correlations are ignored.
解码神经元群体的活动是神经科学中的一个基本问题。这个问题的一个关键方面是确定活动中的相关性，即噪声相关性，是否重要。如果它们很重要，那么解码问题就是高维的：解码算法必须考虑活动中的相关结构。如果它们不重要，或者只起次要作用，那么解码问题就可以降维，从而变得更容易处理。相关性是否重要的问题一直是激烈辩论的主题。辩论集中在用于解决该问题的测量方法的有效性上。在这里，我们评估三种最常用的方法：协同性、DeltaI（重排）和DeltaI。我们表明，协同性和DeltaI（重排）是混淆的测量方法：当相关性对解码显然很重要时它们可以为零，而当相关性不重要时它们可以为正。相比之下，DeltaI没有混淆。只有当相关性对解码不重要时它才为零，只有当相关性重要时它才为正；也就是说，只有当使用忽略相关性的解码器和解码器都能同样好地解码时它才为零，只有当不能同样好地解码时它才为正。最后，我们表明DeltaI具有信息论解释；它是忽略相关性时信息损失的上限。
