Weak pairwise correlations imply strongly correlated network states in a neural population
Weak pairwise correlations imply strongly correlated network states in a neural population
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DOI：
10.1038/nature04701
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发表时间：
2006-04-20
期刊：
影响因子：
64.8
通讯作者：
Bialek, W
中科院分区：
文献类型：
--
作者：
Schneidman, E;Berry, MJ;Bialek, W
生物网络有如此多的可能状态，以至于不可能进行详尽的采样。因此，成功的分析依赖于简化假设，但许多系统的实验表明，大组元素之间复杂的高阶相互作用具有重要作用。在这里，我们表明，在脊椎动物的视网膜中，神经元对之间的弱相关性与十个或更多神经元的反应中的强烈集体行为共存。我们发现，这种集体行为是定量描述的模型，捕捉观察到的成对相关性，但假设没有高阶相互作用。这些最大熵模型等价于伊辛模型，并预测较大的网络完全由相关效应主导。这表明神经代码具有关联或纠错特性，我们为这种行为提供了初步证据。作为这些想法的一般性的第一个测试，我们表明，从培养的皮层神经元网络得到了类似的结果。
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 have an important role. Here we show, in the vertebrate retina, that weak correlations between pairs of neurons coexist with strongly collective behaviour in the responses of ten or more neurons. We find that this collective behaviour 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 behaviour. As a first test for the generality of these ideas, we show that similar results are obtained from networks of cultured cortical neurons.