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"Weak pairwise correlations imply strongly correlated network states in a neural population"

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If you have a question about this talk, please contact Oliver Stegle .

This paper combines our interest in computational neuroscience with our recent interest in Ising models. In contrast to the traditional Hopfield model the Ising approach is used here to make sense of real neural data taken primarily from the salamander retina. Perhaps surprisingly this simple pairwise model can account for most of the variability in the data and makes interesting predictions about the existence of a phase transition that could occur as the number of neurons increases above about 200.

http://www.nature.com/nature/journal/v440/n7087/full/nature04701.html

This talk is part of the Machine Learning Journal Club series.

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