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模式信息分析:柱状敏感度，刺激解码，计算模型测试
Pattern-Information Analysis: Columnar Sensitivity, Stimulus Decoding, and Computational-Model Testing |
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| 课程网址: |
http://videolectures.net/bbci2012_kriegeskorte_information_analys...
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| 主讲教师: |
Nikolaus Kriegeskorte
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| 开课单位: |
剑桥大学
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| 开课时间: |
2012-12-03
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| 课程语种: |
英语
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| 中文简介: |
模式信息核磁共振成像已经成为神经科学中一种流行的方法。这项技术的动机在于功能磁共振成像活动的空间模式反映了感知、认知和行为的神经元群编码。我将回答这个问题，我们可以期望在多大程度上使用2-3毫米体素宽度的传统分辨率，在3T用fMRI研究柱状神经元模式信息。然后，我将把现有的方法与重点放在我们可以从这些方法中学到什么关于大脑理论的问题上进行比较。采用响应模式分类的刺激译码是目前最流行和最普遍的方法。这种方法解决了给定区域的活动模式是否携带有关刺激类别的信息的问题。模式分类使用不模拟大脑信息处理的刺激-反应关系的一般模型，并将刺激空间视为分类空间;这种简化通常是有帮助的，但也限制了可以解决的问题。除了模式分类之外，一个主要的新方向是将大脑信息处理的计算模型集成到模式信息分析中。这种方法使我们能够解决一个问题，即竞争的计算模型在多大程度上与大脑区域的刺激表现相一致。测试计算模型的两种方法是体素接收场建模和表征相似性分析。这些方法对刺激(或心理状态)空间进行更丰富的采样，对每个刺激估计一个单独的响应模式，并能从刺激样本推广到刺激总体。
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| 课程简介: |
Pattern-information fMRI has become a popular method in neuroscience. The technique is motivated by the idea that spatial patterns of fMRI activity reflect the neuronal population codes of perception, cognition, and action. I will address the question to what extent we can expect to investigate columnar-scale neuronal pattern information with fMRI at 3T using conventional resolutions of 2-3 mm voxel width. I will then compare existing approaches with a focus on the question of what we can learn from them in terms of brain theory. The most popular and widespread method is stimulus decoding by response-pattern classification. This approach addresses the question whether activity patterns in a given region carry information about the stimulus category. Pattern classification uses generic models of the stimulus-response relationship that do not mimic brain information processing and treats the stimulus space as categorical – a simplification that is often helpful, but also limiting in terms of the questions that can be addressed. Beyond pattern classification, a major new direction is the integration of computational models of brain information processing into pattern-information analysis. This approach enables us to address the question to what extent competing computational models are consistent with the stimulus representations in a brain region. Two methods that test computational models are voxel receptive-field modeling and representational similarity analysis. These methods sample the stimulus (or mental-state) space more richly, estimate a separate response pattern for each stimulus, and can generalize from the stimulus sample to a stimulus population.
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| 关 键 词: |
核磁共振成像; 神经元群编码; 大脑信息处理计算模型
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-10-22：cwx
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| 阅读次数: |
31
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