Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods
Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods
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DOI：
10.1073/pnas.0906705106
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发表时间：
2009-08-18
影响因子：
11.1
通讯作者：
Monasson, Remi
中科院分区：
文献类型：
--
作者：
Cocco, Simona;Leibler, Stanislas;Monasson, Remi
神经系统的复杂性往往使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，从神经元的尖峰活动推断它们之间的有效耦合，迄今为止一直受到其计算复杂性的阻碍。在这里，我们提出了2个互补的，计算效率高的逆算法的基础上的伊辛和“泄漏积分和消防”模型。我们应用这些算法重新分析蝾螈视网膜在黑暗中和随机视觉刺激下的多电极记录。我们发现附近的神经节细胞之间的强正耦合共同的两种刺激，而远程耦合下出现的随机刺激。由于在记录（持续时间，视网膜上覆盖的小面积）的限制推断耦合的不确定性进行了讨论。我们的方法将允许实时评估大型神经元组件的耦合。
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.