Cocco Simona, Leibler Stanislas, Monasson Rémi
Laboratoire de Physique Statistique de l'Ecole Normale Supérieure, Université Pierre et Marie Curie, Université Denis Diderot, Centre National de la Recherche Scientifique, 24 Rue Lhomond, 75005 Paris, France.
Proc Natl Acad Sci U S A. 2009 Aug 18;106(33):14058-62. doi: 10.1073/pnas.0906705106. Epub 2009 Jul 31.
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.
神经系统的复杂性常常使得明确测量其组成部分之间的所有相互作用变得不切实际。逆统计物理方法从神经元的放电活动推断其有效耦合，但迄今为止一直受到计算复杂性的阻碍。在此，我们提出了基于伊辛模型和“泄漏积分发放”模型的两种互补且计算高效的逆算法。我们应用这些算法重新分析了蝾螈视网膜在黑暗中和随机视觉刺激下的多电极记录。我们发现，两种刺激下附近神经节细胞之间都存在强正耦合，而长程耦合仅在随机刺激下出现。文中还讨论了由于记录限制（持续时间、视网膜覆盖的小面积）导致的推断耦合的不确定性。我们的方法将允许对大量神经元集合的耦合进行实时评估。
