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Neural Dissimilarity Indices That Predict Oddball Detection in Behaviour

机译：预测行为中的奇数球检测的神经相似性指数

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Neuroscientists have recently shown that images that are difficult to find in visual search elicit similar patterns of firing across a population of recorded neurons. The L1 distance between firing rate vectors associated with two images was strongly correlated with the inverse of decision time in behavior. But why should decision times be correlated with L1 distance? What is the decision-theoretic basis? In our decision theoretic formulation, we model visual search as an active sequential hypothesis testing problem with switching costs. Our analysis suggests an appropriate neuronal dissimilarity index, which correlates equally strongly with the inverse of decision time as the L1 distance. We also consider a number of other possibilities, such as the relative entropy (Kullback–Leibler divergence) and the Chernoff entropy of the firing rate distributions. A more stringent test of equality of means, which would have provided a strong backing for our modeling, fails for our proposed as well as the other already discussed dissimilarity indices. However, test statistics from the equality of means test, when used to rank the indices in terms of their ability to explain the observed results, places our proposed dissimilarity index at the top followed by relative entropy, Chernoff entropy, and the L1 indices. Computations of the different indices require an estimate of the relative entropy between two Poisson point processes. An estimator is developed and is shown to have near unbiased performance for almost all operating regions.
机译：神经科学家最近表明，在视觉搜索中难以找到的图像会在记录的神经元群体中引发类似的放电模式。与两个图像关联的发射速率向量之间的L1距离与行为决策时间的倒数密切相关。但是为什么决策时间应该与L1距离相关？决策理论基础是什么？在我们的决策理论表述中，我们将视觉搜索建模为具有转换成本的主动序贯假设检验问题。我们的分析提出了一个合适的神经元相似性指数，该指数与L1距离与决策时间的倒数同等重要。我们还考虑了许多其他可能性，例如点火速率分布的相对熵（Kullback-Leibler散度）和切尔诺夫熵。对均等性进行更严格的测试，这将为我们的建模提供强大的支持，但对于我们的建议以及其他已经讨论过的相异性指标均失败了。但是，根据均值检验的检验统计量，当根据其解释观测结果的能力对指数进行排名时，我们建议的相异性指数位于顶部，其后是相对熵，切尔诺夫熵和L1指数。不同指数的计算需要估计两个泊松点过程之间的相对熵。已开发出一种估计器，并显示出几乎所有操作区域的性能几乎没有偏差。

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