ERP的神经机制：诱发模型还是相位重排模型?

ERP的神经机制问题一直存在争议。诱发模型认为, ERP产生于触发事件诱发的确定性响应电位, 该诱发电位与背景脑电振荡相互独立。相位重排模型认为, 触发事件并不会诱发出一个独立于脑电信号的诱发电位, ERP产生于背景脑电节律振荡的部分相位重排。已有研究提供了各种实验证据以支持诱发模型或/和相位重排模型, 同时也引发了相关学者的诸多质疑。实验记录方式、数据处理及分析方法等方面的提高有利于进一步澄清ERP的神经机制。     

Neural Mechanisms in Dyslexia

ABSTRACT— Within the last two decades, evidence from many laboratories has converged to indicate the cognitive basis for dyslexia: Dyslexia is a disorder within the language system and, more specifically, within a particular subcomponent of that system, phonological processing. Converging evidence from a number of laboratories using functional brain imaging indicates that there is a disruption of left-hemisphere posterior neural systems in child and adult dyslexic readers when they perform reading tasks. The discovery of a disruption in the neural systems serving reading has significant implications for the acceptance of dyslexia as a valid disorder—a necessary condition for its identification and treatment. Brain-imaging findings provide, for the first time, convincing, irrefutable evidence that what has been considered a hidden disability is "real," and these findings have practical implications for the provision of accommodations, a critical component of management for older children and young adults attending postsecondary and graduate programs. The utilization of advances in neuroscience to inform educational policy and practices provides an exciting example of translational science being used for the public good.     

脑内移植发展的回顾和展望

脑内移植是神经系统疾病的一种新的治疗措施。它适应临床实践的需要而产生,历经脑内组织移植和脑内干细胞移植两个重要发展阶段,其中脑内干细胞移植的研究主要有神经干细胞移植、胚胎干细胞移植、骨髓和脐血间充质干细胞移植。脑内干细胞移植是目前国内外医学研究的热点,具有广阔的临床应用前景,但也存在许多有待解决的问题,还需要经过一个艰难曲折的探索过程,最终使之成为治疗神经系统疾病的有效方法。它的产生和发展遵循着认识论和辩证法的客观规律,体现了科学认识的实践性、深刻性和创造性。     

基于人格特征的即时通讯软件用户分类模型

根据即时通讯(instantmessaging,IM)软件工具使用行为的差异将用户划分为两类,使用“大五人格问卷”获取了该两类IM用户的人格特征测量数据。统计分析结果表明,上述两类用户在“适应性”和“社交性”因子上的得分存在显著差异,而在“开放性”、“利他性”和“道德感”三个因子上的得分差异不显著。据此,以用户在“适应性”与“社交性”因子上的得分为输入和用户的分类类别为输出,构建了IM用户基于人格特征的BP神经网络分类模型。对模型的拟合度检验表明,该模型可利用人格测量数据对IM用户进行有效分类。     

Ontologies and Worlds in Category Theory: Implications for Neural Systems

We propose category theory, the mathematical theory of structure, as a vehicle for defining ontologies in an unambiguous language with analytical and constructive features. Specifically, we apply categorical logic and model theory, based upon viewing an ontology as a sub-category of a category of theories expressed in a formal logic. In addition to providing mathematical rigor, this approach has several advantages. It allows the incremental analysis of ontologies by basing them in an interconnected hierarchy of theories, with an operation on the hierarchy that expresses the formation of complex theories from simple theories that express first principles. Another operation forms abstractions expressing the shared concepts in an array of theories. The use of categorical model theory makes possible the incremental analysis of possible worlds, or instances, for the theories, and the mapping of instances of a theory to instances of its more abstract parts. We describe the theoretical approach by applying it to the semantics of neural networks.     

汉语句子格角色分配的一种神经网络方法

提出了一个基于分布式表征的计算模型,通过并行分布加工方式完成六类汉语句子的格角色分配任务。模型是一个四层的前传网络,包括输入层（词的分布式表征层）,两个隐层,输出层（格角色层）;其中第一隐层的一部分反馈到输入层。模型采用误差反传算法,通过提供学习样本和目标输出,不断调整三个权值矩阵,使得网络稳定时能得到正确的结果。经过训练后的网络具有一定的稳定性和鲁棒性。还对这种方法与传统的符号处理方法作了比较和分析。     

A study of the classification capabilities of neural networks using unsupervised learning: A comparison withK-means clustering

Several neural networks have been proposed in the general literature for pattern recognition and clustering, but little empirical comparison with traditional methods has been done. The results reported here compare neural networks using Kohonen learning with a traditional clustering method (K-means) in an experimental design using simulated data with known cluster solutions. Two types of neural networks were examined, both of which used unsupervised learning to perform the clustering. One used Kohonen learning with a conscience and the other used Kohonen learning without a conscience mechanism. The performance of these nets was examined with respect to changes in the number of attributes, the number of clusters, and the amount of error in the data. Generally, theK-means procedure had fewer points misclassified while the classification accuracy of neural networks worsened as the number of clusters in the data increased from two to five.Acknowledgements: Sara Dickson, Vidya Nair, and Beth Means assisted with the neural network analyses.     

A comparison of the classification capabilities of the 1-dimensional kohonen neural network with two pratitioning and three hierarchical cluster analysis algorithms

Neural Network models are commonly used for cluster analysis in engineering, computational neuroscience, and the biological sciences, although they are rarely used in the social sciences. In this study we compare the classification capabilities of the 1-dimensional Kohonen neural network with two partitioning (Hartigan and Späthk-means) and three hierarchical (Ward's, complete linkage, and average linkage) cluster methods in 2,580 data sets with known cluster structure. Overall, the performance of the Kohonen networks was similar to, or better than, the performance of the other methods.