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

@article{Ganmor2011SparseLI,
  title={Sparse low-order interaction network underlies a highly correlated and learnable neural population code.},
  author={Elad Ganmor and Ronen Segev and Elad Schneidman},
  journal={Proceedings of the National Academy of Sciences of the United States of America},
  year={2011},
  volume={108 23},
  pages={9679-84}
}
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… CONTINUE READING
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