文章基本信息

标题：Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods
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作者：Simona Cocco ; Stanislas Leibler ; Rémi Monasson 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2009
卷号：106
期号：33
页码：14058-14062
DOI：10.1073/pnas.0906705106
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Complexity of neural systems often makes impracticable explicit measurements of all interactions between their constituents. Inverse statistical physics approaches, which infer effective couplings between neurons from their spiking activity, have been so far hindered by their computational complexity. Here, we present 2 complementary, computationally efficient inverse algorithms based on the Ising and "leaky integrate-and-fire" models. We apply those algorithms to reanalyze multielectrode recordings in the salamander retina in darkness and under random visual stimulus. We find strong positive couplings between nearby ganglion cells common to both stimuli, whereas long-range couplings appear under random stimulus only. The uncertainty on the inferred couplings due to limitations in the recordings (duration, small area covered on the retina) is discussed. Our methods will allow real-time evaluation of couplings for large assemblies of neurons.
关键词：inference and inverse problems ; multielectrode recordings ; neural couplings