Principal and independent components of macaque vocalizations: Constructing stimuli to probe high-level sensory processing
Principal and independent components of macaque vocalizations: Constructing stimuli to probe high-level sensory processing
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DOI：
10.1152/jn.01103.2003
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发表时间：
2004-06-01
影响因子：
2.5
通讯作者：
Romanski, LM
中科院分区：
文献类型：
--
作者：
Averbeck, BB;Romanski, LM
高级感觉皮层区域的神经元对感觉刺激中的复杂特征做出反应。特征消除是研究这些响应的有用技术。在这种方法中，引起神经元反应的复杂刺激被简化，如果细胞对减少的刺激做出反应，则认为对其余特征具有选择性。我们已经开发了一种特征消除技术，该技术使用刺激的主要或独立分量来定义神经元可能敏感的特征子集。可以使用这些组件过滤原始刺激，从而产生仅保留原始中存在的一小部分特征的刺激。我们展示了这种技术在猕猴发声中的应用，这是一类重要的刺激，用于在清醒、有行为的灵长类动物实验中研究听觉功能。我们表明，主成分分析提取的功能，是密切相关的主要傅立叶成分的刺激，通常被称为共振峰在语音感知的研究。相反，独立分量分析提取的特征保留了一组谐波相关频率的相对相位。我们使用了几种统计技术来探索原始和过滤的刺激，以及每种技术提取的成分。这种新的方法提供了一个强大的方法来确定激活高阶感觉神经元的复杂刺激的基本特征。
Neurons in high-level sensory cortical areas respond to complex features in sensory stimuli. Feature elimination is a useful technique for studying these responses. In this approach, a complex stimulus, which evokes a neuronal response, is simplified, and if the cell responds to the reduced stimulus, it is considered selective for the remaining features. We have developed a feature-elimination technique that uses either the principal or the independent components of a stimulus to define a subset of features, to which a neuron might be sensitive. The original stimulus can be filtered using these components, resulting in a stimulus that retains only a fraction of the features present in the original. We demonstrate the use of this technique on macaque vocalizations, an important class of stimuli being used to study auditory function in awake, behaving primate experiments. We show that principal-component analysis extracts features that are closely related to the dominant Fourier components of the stimuli, often called formants in the study of speech perception. Conversely, independent-component analysis extracts features that preserve the relative phase across a set of harmonically related frequencies. We have used several statistical techniques to explore the original and filtered stimuli, as well as the components extracted by each technique. This novel approach provides a powerful method for determining the essential features within complex stimuli that activate higher-order sensory neurons.