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.     

从人脑到计算机─—鸿沟能否逾越

人类对人脑及其精神活动过程的彻底了解,意味着哲学上的根本性事件的发生。所谓彻底的了解,标志就是人类对这种精神活动的成功模拟和再现。作为神经科学的分支和广延的人工智能的兴起,使人类有理由希望：存在于人脑与计算机之间的鸿沟终将被人类逾越。     

冲突控制的神经机制

人类认知系统的一个重要特征是它能根据知觉选择、反应偏向以及实时更新的情境信息调节自身以适应特定的任务,使这种适应性得以实现的认知过程就是认知控制。该文简述了冲突控制的界定、实验范式、冲突的种类等方面,尤其详细介绍了冲突控制神经机制研究的最新进展,并对进一步的研究提出展望。     

Understanding Cognition Through Large-Scale Cortical Networks

An emerging body of evidence from a number of fields is beginning to reveal general neural principles underlying cognition. The characteristic adaptability of cognitive function is seen to derive from large-scale networks in the cerebral cortex that are able to repeatedly change the state of coordination among their constituent areas on a subsecond time scale. Experimental and theoretical studies suggest that large-scale network dynamics operate in a metastable regime in which the interdependence of cortical areas is balanced between integrating and segregating activities. Cortical areas, through their coordination dynamics, are thought to rapidly resolve a large number of mutually imposed constraints, leading to consistent local states and a globally coherent state of cognition.