Information-limiting correlations.
Information-limiting correlations.
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DOI：
10.1038/nn.3807
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发表时间：
2014-10
影响因子：
25
通讯作者：
Pouget, Alexandre
中科院分区：
文献类型：
--
作者：
Moreno-Bote, Ruben;Beck, Jeffrey;Kanitscheider, Ingmar;Pitkow, Xaq;Latham, Peter;Pouget, Alexandre
大脑使用的计算策略在很大程度上取决于可以存储在人群活动中的信息量，而这些信息量又在很大程度上取决于噪声相关性的模式。在体内，噪声相关性趋向于正的，并且与调谐特性的相似性成正比。这种相关性被认为限制了信息，这导致了去相关性增加信息的建议。相反，通过分析和数值分析，我们发现去相关并不意味着信息的增加。相反，唯一限制信息的相关性是我们所说的微分相关性：与调谐曲线导数的乘积成比例的相关性。不幸的是，微分相关性可能非常小，并且隐藏在不限制信息的相关性之下，这使得它们特别难以检测。然而，我们发现，差分相关性对信息的影响可以用相对简单的解码器来检测。
Computational strategies used by the brain strongly depend on the amount of information that can be stored in population activity, which in turn strongly depends on the pattern of noise correlations. In vivo, noise correlations tend to be positive and proportional to the similarity in tuning properties. Such correlations are thought to limit information, which has led to the suggestion that decorrelation increases information. In contrast, we found, analytically and numerically, that decorrelation does not imply an increase in information. Instead, the only information-limiting correlations are what we refer to as differential correlations: correlations proportional to the product of the derivatives of the tuning curves. Unfortunately, differential correlations are likely to be very small and buried under correlations that do not limit information, making them particularly difficult to detect. We found, however, that the effect of differential correlations on information can be detected with relatively simple decoders.
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影响因子：
16.2
作者：
Beck JM;Ma WJ;Pitkow X;Latham PE;Pouget A
通讯作者：
Pouget A
影响因子：
25
作者：
Goris, Robbe L. T.;Movshon, J. Anthony;Simoncelli, Eero P.
通讯作者：
Simoncelli, Eero P.
影响因子：
16.2
作者：
Ecker AS;Berens P;Cotton RJ;Subramaniyan M;Denfield GH;Cadwell CR;Smirnakis SM;Bethge M;Tolias AS
通讯作者：
Tolias AS
影响因子：
2.5
作者：
Huang, Xin;Lisberger, Stephen G.
通讯作者：
Lisberger, Stephen G.
影响因子：
64.8
作者：
NEWSOME, WT;BRITTEN, KH;MOVSHON, JA
通讯作者：
MOVSHON, JA