用于认知过程分类的网格学习

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摘要翻译：
认知神经科学的一个相对较新的进展是多体素模式分析(MVPA)，它使研究人员能够在认知操作期间解码大脑状态和/或大脑中表示的信息类型。MVPA方法利用机器学习算法根据神经活动的分布模式来区分大脑中表示的信息或认知状态的类型。在当前的研究中，我们提出了一种新的用于模式分析的神经数据表示方法，即网格学习模型。在该方法中，在每个时刻，在每个体素周围形成一个星形网格，使得中心节点对应的体素被其p-最近邻包围。利用最小二乘法从体素强度值估计每个网格的圆弧权值。所有网格的估计弧权重被称为网格弧描述符(MADs)，然后被用来训练分类器，如神经网络、K-最近邻、Na\\“ive Bayes和支持向量机。在一个使用分类单词表的识别记忆实验中，在通过功能磁共振成像（fMRI）获得的神经成像数据上测试了所提出的网格模型，使用了以前建立的实验范式(Oztekin&Badre,2011）。结果表明，所提出的网格学习方法可以为认知加工过程中的脑活动模式分析提供一种有效的算法。
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英文标题：
《Mesh Learning for Classifying Cognitive Processes》
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作者：
Mete Ozay, Ilke \"Oztekin, Uygar \"Oztekin, Fatos T. Yarman Vural
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最新提交年份：
2015
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  A relatively recent advance in cognitive neuroscience has been multi-voxel pattern analysis (MVPA), which enables researchers to decode brain states and/or the type of information represented in the brain during a cognitive operation. MVPA methods utilize machine learning algorithms to distinguish among types of information or cognitive states represented in the brain, based on distributed patterns of neural activity. In the current investigation, we propose a new approach for representation of neural data for pattern analysis, namely a Mesh Learning Model. In this approach, at each time instant, a star mesh is formed around each voxel, such that the voxel corresponding to the center node is surrounded by its p-nearest neighbors. The arc weights of each mesh are estimated from the voxel intensity values by least squares method. The estimated arc weights of all the meshes, called Mesh Arc Descriptors (MADs), are then used to train a classifier, such as Neural Networks, k-Nearest Neighbor, Na\"ive Bayes and Support Vector Machines. The proposed Mesh Model was tested on neuroimaging data acquired via functional magnetic resonance imaging (fMRI) during a recognition memory experiment using categorized word lists, employing a previously established experimental paradigm (\"Oztekin & Badre, 2011). Results suggest that the proposed Mesh Learning approach can provide an effective algorithm for pattern analysis of brain activity during cognitive processing.
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PDF链接：
https://arxiv.org/pdf/1205.2382

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关键词：Presentation Intelligence Experimental Evolutionary Neuroscience during 大脑 分类器 cognitive represented