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首页> 外文期刊>Journal of Computational Neuroscience >A new method to infer higher-order spike correlations from membrane potentials
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A new method to infer higher-order spike correlations from membrane potentials

机译：从膜电位推断高阶尖峰相关性的新方法

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摘要

What is the role of higher-order spike correlations for neuronal information processing? Common data analysis methods to address this question are devised for the application to spike recordings from multiple single neurons. Here, we present a new method which evaluates the subthreshold membrane potential fluctuations of one neuron, and infers higher-order correlations among the neurons that constitute its presynaptic population. This has two important advantages: Very large populations of up to several thousands of neurons can be studied, and the spike sorting is obsolete. Moreover, this new approach truly emphasizes the functional aspects of higher-order statistics, since we infer exactly those correlations which are seen by a neuron. Our approach is to represent the subthreshold membrane potential fluctuations as presynaptic activity filtered with a fixed kernel, as it would be the case for a leaky integrator neuron model. This allows us to adapt the recently proposed method CuBIC (cumulant based inference of higher-order correlations from the population spike count; Staude et al., J Comput Neurosci 29(l-2):327-350, 2010c) with which the maximal order of correlation can be inferred. By numerical simulation we show that our new method is reasonably sensitive to weak higher-order correlations, and that only short stretches of membrane potential are required for their reliable inference. Finally, we demonstrate its remarkable robustness against violations of the simplifying assumptions made for its construction, and discuss how it can be employed to analyze in vivo intracellular recordings of membrane potentials.
机译：高阶尖峰相关对神经元信息处理的作用是什么？针对该问题设计了通用的数据分析方法，以用于从多个单个神经元中加峰记录的应用。在这里，我们提出了一种新的方法，该方法可以评估一个神经元的阈下膜电位波动，并推断构成其突触前群体的神经元之间的高阶相关性。这具有两个重要的优点：可以研究多达数千个神经元的非常大的种群，并且尖峰排序已过时。而且，这种新方法真正地强调了高阶统计的功能方面，因为我们精确地推断出神经元所见的那些相关性。我们的方法是将阈下膜电位波动表示为用固定核过滤的突触前活动，就像渗漏积分神经元模型的情况一样。这使我们能够适应最近提出的方法CuBIC（从人口峰值计数基于累积量的高阶相关性推断； Staude等人，J Comput Neurosci 29（1-2）：327-350，2010c）可以推断出相关的顺序。通过数值模拟，我们证明了我们的新方法对弱的高阶相关性相当敏感，并且仅需要短时间的膜电位来进行可靠的推断即可。最后，我们证明了其出色的鲁棒性，可抵制对其构造所做的简化假设，并讨论了如何将其用于分析膜电位的体内细胞内记录。

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