Synergy, redundancy, and independence in population codes, revisited
Synergy, redundancy, and independence in population codes, revisited
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DOI：
10.1523/jneurosci.5319-04.2005
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发表时间：
2005-05-25
影响因子：
5.3
通讯作者：
Nirenberg, S
中科院分区：
文献类型：
--
作者：
Latham, PE;Nirenberg, S
解码一群神经元的活动是神经科学的一个基本问题。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：解码算法必须考虑到活动中的相关结构。如果它们不重要，或者作用不大，则可以将解码问题降低到较低的维度，从而使其更易于处理。相关性是否重要的问题一直是一个激烈争论的话题。争论的焦点是用于解决这一问题的措施的有效性。在这里，我们评估了三种最常用的方法：协同作用，δ i - shuffed和δ i - shuffed。我们表明协同作用和δ i - shuffed是混淆的措施：当相关性对解码明显重要时，它们可以为零，当相关性对解码明显重要时，它们可以为正。相比之下，I不会混淆。只有当相关性对解码不重要时，它才为零，只有当相关性对解码重要时，它才为正；也就是说，只有当一个人可以使用忽略相关性的解码器和一个人可以使用不忽略相关性的解码器完全解码时，它才为零，只有当一个人不能解码时，它才为正。最后，我们证明了Delta I具有信息论解释；它是忽略相关性时丢失信息的上界。
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, Delta I-shuffled, and Delta I. We show that synergy and Delta I-shuffled are confounded measures: they can be zero when correlations are clearly important for decoding and positive when they are not. In contrast, Delta I 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 Delta I has an information theoretic interpretation; it is an upper bound on the information lost when correlations are ignored.