UNIGE document Scientific Article
previous document  unige:43130  next document
add to browser collection
Title

Information-limiting correlations

Authors
Moreno-Bote, Rubén
Beck, Jeffrey
Pitkow, Xaq
Latham, Peter
Published in Nature Neuroscience. 2014, vol. 17, no. 10, p. 1410-7
Abstract 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.
Identifiers
PMID: 25195105
Full text
Article (Published version) (1.1 MB) - document accessible for UNIGE members only Limited access to UNIGE
Structures
Research group Groupe Alexandre Pouget (938)
Project FNS: 31003A_143707
Citation
(ISO format)
MORENO-BOTE, Rubén et al. Information-limiting correlations. In: Nature Neuroscience, 2014, vol. 17, n° 10, p. 1410-7. https://archive-ouverte.unige.ch/unige:43130

179 hits

2 downloads

Update

Deposited on : 2014-12-11

Export document
Format :
Citation style :