NDLI: Sparse low-order interaction network underlies a highly correlated and learnable neural population code

Content Provider	PubMed Central
Author	Ganmor, Elad Segev, Ronen Schneidman, Elad
Abstract	Information is carried in the brain by the joint activity patterns of large groups of neurons. Understanding the structure and function of population neural codes is challenging because of the exponential number of possible activity patterns and dependencies among neurons. We report here that for groups of ~100 retinal neurons responding to natural stimuli, pairwise-based models, which were highly accurate for small networks, are no longer sufficient. We show that because of the sparse nature of the neural code, the higher-order interactions can be easily learned using a novel model and that a very sparse low-order interaction network underlies the code of large populations of neurons. Additionally, we show that the interaction network is organized in a hierarchical and modular manner, which hints at scalability. Our results suggest that learnability may be a key feature of the neural code.
Related Links	http://dx.doi.org/10.1073/pnas.1019641108
Ending Page	9684
Page Count	6
Starting Page	9679
File Format	PDF
ISSN	00278424
e-ISSN	10916490
Journal	Proceedings of the National Academy of Sciences of the United States of America
Issue Number	23
Volume Number	108
Language	English
Publisher	National Academy of Sciences
Publisher Date	2011-06-07
Access Restriction	Open
Rights Holder	National Academy of Sciences
Subject Keyword	General Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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Sparse low-order interaction network underlies a highly correlated and learnable neural population code