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

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

2005-12-06

arxiv.org/abs/q-bio/0512013v1

Biology

Quantitative Biology

Neurons and Cognition

Full account of work presented at the conference on Computational and Systems Neuroscience (COSYNE), 17-20 March 2005, in Salt

Scientific paper

10.1038/nature04701

Biological networks have so many possible states that exhaustive sampling is impossible. Successful analysis thus depends on simplifying hypotheses, but experiments on many systems hint that complicated, higher order interactions among large groups of elements play an important role. In the vertebrate retina, we show that weak correlations between pairs of neurons coexist with strongly collective behavior in the responses of ten or more neurons. Surprisingly, we find that this collective behavior is described quantitatively by models that capture the observed pairwise correlations but assume no higher order interactions. These maximum entropy models are equivalent to Ising models, and predict that larger networks are completely dominated by correlation effects. This suggests that the neural code has associative or error-correcting properties, and we provide preliminary evidence for such behavior. As a first test for the generality of these ideas, we show that similar results are obtained from networks of cultured cortical neurons.

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