2005/12/06 by Elad Schneidman, Michael J. Berry, Michael J. Berry II +2 · 19 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Neuroscience · Physics and Astronomy · Psychology · #Artificial intelligence #Artificial neural network #Computer science #Correlation #Entropy (arrow of time) #Generality #Ising model #Mathematics #Neural Networks and Applications #Neural dynamics and brain function #Pairwise comparison #Physics #Population #Psychology #Statistical physics #q-bio.NC #q-bio.QM #stochastic dynamics and bifurcation
paper · pdf · doi:10.1038/nature04701
Full account of work presented at the conference on Computational and Systems Neuroscience (COSYNE), 17-20 March 2005, in Salt Lake City, Utah (http://cosyne.org)
arxiv created 2005/12/06 · openalex publication_date 2006/04/01 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.