Valuations for spike train prediction
Valuations for spike train prediction
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DOI：
10.1162/neco.2007.3179
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发表时间：
2008-03-01
影响因子：
2.9
通讯作者：
Harris, Kenneth D.
中科院分区：
文献类型：
--
作者：
Itskov, Vladimir;Curto, Carina;Harris, Kenneth D.
电生理学实验的最终产品通常是决定哪种生物学假设或模型最能解释观察到的数据。我们概述了一个范例，旨在比较不同的模型，我们称之为穗列车预测。这种模式的一个关键因素是预测质量评估，估计预测的条件强度函数与实际观察到的尖峰序列有多接近。虽然基于对数似然（L）的估值是最自然的，但在这种情况下，它有各种复杂性。我们提出，二次赋值（Q）可以用作L的替代。Q与L具有一些重要的理论性质，包括一致性，并且这两种估值在模拟和实验数据上的表现相似。此外，Q比L更鲁棒，并且使用Q进行优化可以显着提高计算效率。我们说明了Q的效用比较模型的同行预测，它可以直接从交叉相关图计算。虽然Q没有直接的概率解释，但Q本质上是由欧几里得距离给出的。
The ultimate product of an electrophysiology experiment is often a decision on which biological hypothesis or model best explains the observed data. We outline a paradigm designed for comparison of different models, which we refer to as spike train prediction. A key ingredient of this paradigm is a prediction quality valuation that estimates how close a predicted conditional intensity function is to an actual observed spike train. Although a valuation based on log likelihood (L) is most natural, it has various complications in this context. We propose that a quadratic valuation (Q) can be used as an alternative to L. Q shares some important theoretical properties with L, including consistency, and the two valuations perform similarly on simulated and experimental data. Moreover, Q is more robust than L, and optimization with Q can dramatically improve computational efficiency. We illustrate the utility of Q for comparing models of peer prediction, where it can be computed directly from cross-correlograms. Although Q does not have a straightforward probabilistic interpretation, Q is essentially given by Euclidean distance.