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首页> 外文期刊>Journal of Computational Neuroscience >Estimating the directed information to infer causal relationships in ensemble neural spike train recordings
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Estimating the directed information to infer causal relationships in ensemble neural spike train recordings

机译：估计有向信息以推断合奏神经峰值火车记录中的因果关系

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Advances in recording technologies have given neuroscience researchers access to large amounts of data, in particular, simultaneous, individual recordings of large groups of neurons in different parts of the brain. A variety of quantitative techniques have been utilized to analyze the spiking activities of the neurons to elucidate the functional connectivity of the recorded neurons. In the past, researchers have used correlative measures. More recently, to better capture the dynamic, complex relationships present in the data, neuroscientists have employed causal measures-most of which are variants of Granger causality-with limited success. This paper motivates the directed information, an information and control theoretic concept, as a modality-independent embodiment of Granger's original notion of causality. Key properties include: (a) it is nonzero if and only if one process causally influences another, and (b) its specific value can be interpreted as the strength of a causal relationship. We next describe how the causally conditioned directed information between two processes given knowledge of others provides a network version of causality: it is nonzero if and only if, in the presence of the present and past of other processes, one process causally influences another. This notion is shown to be able to differentiate between true direct causal influences, common inputs, and cascade effects in more two processes. We next describe a procedure to estimate the directed information on neural spike trains using point process generalized linear models, maximum likelihood estimation and information-theoretic model order selection. We demonstrate that on a simulated network of neurons, it (a) correctly identifies all pairwise causal relationships and (b) correctly identifies network causal relationships. This procedure is then used to analyze ensemble spike train recordings in primary motor cortex of an awake monkey while performing target reaching tasks, uncovering causal relationships whose directionality are consistent with predictions made from the wave propagation of simultaneously recorded local field potentials.
机译：记录技术的进步使神经科学研究者可以访问大量数据，尤其是可以同时对大脑不同部位的大量神经元进行单个记录。已经利用各种定量技术来分析神经元的突刺活性，以阐明所记录的神经元的功能连接性。过去，研究人员使用了相关措施。最近，为了更好地捕获数据中存在的动态，复杂关系，神经科学家采用了因果措施（其中大多数是格兰杰因果关系的变体），但效果有限。本文将有针对性的信息作为一种信息和控制理论的概念，作为格兰杰的因果关系原始概念的形式无关性体现。关键属性包括：（a）当且仅当一个过程对另一个过程有因果影响时，它不为零；并且（b）其特定值可以解释为因果关系的强度。接下来，我们将描述在给定其他过程的知识的情况下两个过程之间的因果条件式有向信息如何提供因果关系的网络版本：当且仅当在存在和存在其他过程的情况下一个过程因果地影响另一个过程时，它才为非零。事实证明，该概念能够在两个以上的过程中区分出真正的直接因果影响，共同的投入和级联效应。接下来，我们描述一种使用点过程广义线性模型，最大似然估计和信息理论模型顺序选择来估计神经穗序列上的定向信息的过程。我们证明，在模拟的神经元网络上，它（a）正确识别所有成对因果关系，并且（b）正确识别网络因果关系。然后，该过程用于在执行目标到达任务时分析清醒猴子的初级运动皮质中的合奏峰值运动记录，从而发现因果关系，这些因果关系的方向性与根据同时记录的局部场电势的波传播所做的预测一致。

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