Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis.
Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis.
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DOI：
10.1371/journal.pcbi.1005508
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发表时间：
2017-04
影响因子：
4.3
通讯作者：
Kriegeskorte N
中科院分区：
文献类型：
--
作者：
Diedrichsen J;Kriegeskorte N
表征模型具体说明了神经元群体的活动模式（或者更一般地说，在多变量脑活动测量中）如何与感觉刺激、运动反应或认知过程相关。在实验环境中，表征模型可以被定义为关于跨实验条件下活动剖面分布的假设。目前，有三种不同的方法被用来测试这些假设：编码分析、模式组件建模（PCM）和表示相似性分析（RSA）。在这里，我们开发了一个通用的数学框架来理解这三种方法之间的关系，它们有一个核心共同点：所有三种方法都评估活动概况分布的第二时刻，这决定了代表性几何形状，从而确定了从人口活动中解码任何特征的程度。通过对三种不同实验设计的模拟数据，我们比较了这些方法在相互竞争的表征模型之间进行评判的能力。PCM实现了似然比检验，因此提供了最有力的检验，如果它的假设成立。然而，其他两种方法（如果执行得当）也可以实现类似的效果。在编码分析中，需要对线性模型进行适当的正则化，从而有效地对活动剖面施加先验。有了这样的先验，编码模型指定了活动概要文件的良好定义的分布。在RSA中，需要考虑不相等方差和不相似估计的统计依赖，以达到接近最优的推理能力。这三种方法显示了信息的不同方面（例如编码分析中的单响应调优和RSA中的人口-响应表示不相似性），并且在计算需求、易用性和可扩展性方面具有特定的优势。这三种方法被恰当地解释为单个数据分析工具包的互补组成部分，用于理解基于多变量大脑活动数据的神经表征。现代神经科学可以同时测量许多神经元的活动或大脑许多部位的局部血氧。随着同时测量的数量增加，我们可以更好地研究大脑如何表示和转换信息，以实现感知、认知和行为。最近的研究不仅表明大脑的某个区域参与了某些功能。他们使用表征模型来说明不同的感知、认知和行为是如何在大脑活动模式中编码的。在本文中，我们为这种代表性模型提供了一个一般的数学框架，它澄清了目前在神经科学界使用的三种不同方法之间的关系。这三种方法都评估数据的相同核心特征，但每种方法都有不同的优点和缺点。模式组件建模（PCM）实现了模型之间最强大的测试，并且在分析上易于处理和扩展。代表性相似性分析（RSA）提供了非常有用的汇总统计（不相似性），并支持与较弱的分布假设进行模型比较。最后，编码模型描述了个体反应的特征，并使研究它们在皮层中的布局成为可能。我们认为，这些方法应该被视为一个更大的工具包的组成部分，用于测试关于大脑代表信息的方式的假设。
Representational models specify how activity patterns in populations of neurons (or, more generally, in multivariate brain-activity measurements) relate to sensory stimuli, motor responses, or cognitive processes. In an experimental context, representational models can be defined as hypotheses about the distribution of activity profiles across experimental conditions. Currently, three different methods are being used to test such hypotheses: encoding analysis, pattern component modeling (PCM), and representational similarity analysis (RSA). Here we develop a common mathematical framework for understanding the relationship of these three methods, which share one core commonality: all three evaluate the second moment of the distribution of activity profiles, which determines the representational geometry, and thus how well any feature can be decoded from population activity. Using simulated data for three different experimental designs, we compare the power of the methods to adjudicate between competing representational models. PCM implements a likelihood-ratio test and therefore provides the most powerful test if its assumptions hold. However, the other two approaches—when conducted appropriately—can perform similarly. In encoding analysis, the linear model needs to be appropriately regularized, which effectively imposes a prior on the activity profiles. With such a prior, an encoding model specifies a well-defined distribution of activity profiles. In RSA, the unequal variances and statistical dependencies of the dissimilarity estimates need to be taken into account to reach near-optimal power in inference. The three methods render different aspects of the information explicit (e.g. single-response tuning in encoding analysis and population-response representational dissimilarity in RSA) and have specific advantages in terms of computational demands, ease of use, and extensibility. The three methods are properly construed as complementary components of a single data-analytical toolkit for understanding neural representations on the basis of multivariate brain-activity data. Modern neuroscience can measure activity of many neurons or the local blood oxygenation of many brain locations simultaneously. As the number of simultaneous measurements grows, we can better investigate how the brain represents and transforms information, to enable perception, cognition, and behavior. Recent studies go beyond showing that a brain region is involved in some function. They use representational models that specify how different perceptions, cognitions, and actions are encoded in brain-activity patterns. In this paper, we provide a general mathematical framework for such representational models, which clarifies the relationships between three different methods that are currently used in the neuroscience community. All three methods evaluate the same core feature of the data, but each has distinct advantages and disadvantages. Pattern component modelling (PCM) implements the most powerful test between models, and is analytically tractable and expandable. Representational similarity analysis (RSA) provides a highly useful summary statistic (the dissimilarity) and enables model comparison with weaker distributional assumptions. Finally, encoding models characterize individual responses and enable the study of their layout across cortex. We argue that these methods should be considered components of a larger toolkit for testing hypotheses about the way the brain represents information.