A Comparison of Descriptive Models of a Single Spike Train by Information-Geometric Measure
A Comparison of Descriptive Models of a Single Spike Train by Information-Geometric Measure
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DOI：
10.1162/neco.2006.18.3.545
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发表时间：
2006-03
影响因子：
2.9
通讯作者：
H. Nakahara;S. Amari;B. Richmond
中科院分区：
文献类型：
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作者：
H. Nakahara;S. Amari;B. Richmond
在研究锋电位序列时，不同的模型被用来描述它们的结构。不同的模型往往看起来非常相似，但由于它们采用不同的形式主义，通常很难比较它们的预测。在这里，我们使用信息几何测度，一个正交坐标表示的点过程，在一个共同的坐标系中表示随机点过程的不同模型。在这样的框架内，可以直观地显示不同模型的高阶相关性，从而评估模型之间的差异。我们应用信息几何测度比较两个相似但不相同的神经元发放序列模型：非齐次马尔可夫模型和混合泊松模型。结果表明，它们在二阶和高阶相互作用项上是不同的。在泊松混合模型中，二阶和高阶相互作用在每个阶次内具有可比的量级，而在非齐次马尔可夫模型中，它们在不同阶次上具有交替的符号。这提供了关于什么测量将有效地分离两个模型的指导。随着新模型的提出，它们也可以使用信息几何与这些模型进行比较。
In examining spike trains, different models are used to describe their structure. The different models often seem quite similar, but because they are cast in different formalisms, it is often difficult to compare their predictions. Here we use the information-geometric measure, an orthogonal coordinate representation of point processes, to express different models of stochastic point processes in a common coordinate system. Within such a framework, it becomes straightforward to visualize higher-order correlations of different models and thereby assess the differences between models. We apply the information-geometric measure to compare two similar but not identical models of neuronal spike trains: the inhomogeneous Markov and the mixture of Poisson models. It is shown that they differ in the secondand higher-order interaction terms. In the mixture of Poisson model, the second- and higher-order interactions are of comparable magnitude within each order, whereas in the inhomogeneous Markov model, they have alternating signs over different orders. This provides guidance about what measurements would effectively separate the two models. As newer models are proposed, they also can be compared to these models using information geometry.