Norm-based face encoding by single neurons in the monkey inferotemporal cortex

The rich and immediate perception of a familiar face, including its identity, expression and even intent, is one of the most impressive shared faculties of human and non-human primate brains. Many visually responsive neurons in the inferotemporal cortex of macaque monkeys respond selectively to faces, sometimes to only one or a few individuals, while showing little sensitivity to scale and other details of the retinal image. Here we show that face-responsive neurons in the macaque monkey anterior inferotemporal cortex are tuned to a fundamental dimension of face perception. Using a norm-based caricaturization framework previously developed for human psychophysics, we varied the identity information present in photo-realistic human faces, and found that neurons of the anterior inferotemporal cortex were most often tuned around the average, identity-ambiguous face. These observations are consistent with face-selective responses in this area being shaped by a figural comparison, reflecting structural differences between an incoming face and an internal reference or norm. As such, these findings link the tuning of neurons in the inferotemporal cortex to psychological models of face identity perception.     

Columns for visual features of objects in monkey inferotemporal cortex.

At early stages of the mammalian visual cortex, neurons with similar stimulus selectivities are vertically arrayed through the thickness of the cortical sheet and clustered in patches or bands across the surface. This organization, referred to as a 'column', has been found with respect to one-dimensional stimulus parameters such as orientation of stimulus contours, eye dominance of visual inputs, and direction of stimulus motion. It is unclear, however, whether information with extremely high dimensions, such as visual shape, is organized in a similar columnar fashion or in a different manner in the brain. Here we report that the anterior inferotemporal area of the monkey cortex, the final station of the visual cortical stream crucial for object recognition, consists of columns, each containing cells responsive to similar visual features of objects.     

Visual categorization shapes feature selectivity in the primate temporal cortex.

The way that we perceive and interact with objects depends on our previous experience with them. For example, a bird expert is more likely to recognize a bird as a sparrow, a sandpiper or a cockatiel than a non-expert. Neurons in the inferior temporal cortex have been shown to be important in the representation of visual objects; however, it is unknown which object features are represented and how these representations are affected by categorization training. Here we show that feature selectivity in the macaque inferior temporal cortex is shaped by categorization of objects on the basis of their visual features. Specifically, we recorded from single neurons while monkeys performed a categorization task with two sets of parametric stimuli. Each stimulus set consisted of four varying features, but only two of the four were important for the categorization task (diagnostic features). We found enhanced neuronal representation of the diagnostic features relative to the non-diagnostic ones. These findings demonstrate that stimulus features important for categorization are instantiated in the activity of single units (neurons) in the primate inferior temporal cortex.     

Early experience of tactile stimulation influences organization of somatic sensory cortex

Visual experience is essential for the establishment of the cerebral cortical circuitry that allows normal binocular vision. For example, the pattern of right-eye, left-eye dominance columns is permanently altered by simply closing an eye of a young primate. A critical issue is whether environmental factors also influence the development of other cortical sensory areas. In the present experiments we manipulated the tactile experience of young rats by depriving them of the sensory information that is normally provided by their large facial whiskers. Electrophysiological analyses showed that simply trimming the whiskers from the day of birth results in pronounced abnormalities in the response properties of single neurons in the adult somatic sensory cortex. Thus functional plasticity in response to early experience appears to be a fundamental aspect of cortical development.     

Feature-based attention influences motion processing gain in macaque visual cortex.

Changes in neural responses based on spatial attention have been demonstrated in many areas of visual cortex, indicating that the neural correlate of attention is an enhanced response to stimuli at an attended location and reduced responses to stimuli elsewhere. Here we demonstrate non-spatial, feature-based attentional modulation of visual motion processing, and show that attention increases the gain of direction-selective neurons in visual cortical area MT without narrowing the direction-tuning curves. These findings place important constraints on the neural mechanisms of attention and we propose to unify the effects of spatial location, direction of motion and other features of the attended stimuli in a 'feature similarity gain model' of attention.     

Comparison of text categorization algorithms

This paper summarizes several automatic text categorization algorithms in common use recently, analyzes and compares their advantages and disadvantages. It provides clues for making use of appropriate automatic classifying algorithms in different fields. Finally some evaluations and summaries of these algorithms are discussed, and directions to further research have been pointed out.     

中文文本分类器的评价

对目前比较流行的4种中文文本分类器(Rocchio、KNN、Naive Bayes、最大熵)进行评价，其中，Naive Bayes和最大熵是基于概率统计的方法，而Rocchio和KNN是基于向量的相似度计算的方法。选用X2作为文本特征选取方法，对一个中文文本分类语料库进行分类评测。实验结果表明，最大熵和Naive Bayes的分类性能十分接近，处于较好水平，而KNN和Rocchio分类性能稍差一些。     

中文文本分类器的评价

对目前比较流行的4种中文文本分类器(Rocchio、KNN、NaiveBayes、最大熵)进行评价,其中,NaiveBayes和最大熵是基于概率统计的方法,而Rocchio和KNN是基于向量的相似度计算的方法。选用χ2作为文本特征选取方法,对一个中文文本分类语料库进行分类评测。实验结果表明,最大熵和NaiveBayes的分类性能十分接近,处于较好水平,而KNN和Rocchio分类性能稍差一些。     

中文文本分类器的评价

对目前比较流行的4种中文文本分类器(Rocchio、KNN、Naive Bayes、最大熵)进行评价,其中,Naive Bayes和最大熵是基于概率统计的方法,而Rocchio和KNN是基于向量的相似度计算的方法.选用χ2作为文本特征选取方法,对一个中文文本分类语料库进行分类评测.实验结果表明,最大熵和Naive Bayes的分类性能十分接近,处于较好水平,而KNN和Rocchio分类性能稍差一些.     

电刺激大鼠mPFC对听皮层神经元听反应的影响

实验在40只成年SD大鼠上进行,使用常规电生理学方法,观察了电刺激大鼠内侧额叶前皮质(medial prefrontal cortex, mPFC)对听皮层神经元听反应的影响.在122个神经元上观察了电刺激mPFC对听反应的影响.对其中93个神经元作了详细分析发现,有73个神经元的听反应受到易化（39个,41.9%）或抑制（34个,36.6%）.刺激mPFC对听反应的影响存在最佳刺激间隔,大多数神经元（51个,69%）在10~15 ms之间.结果提示,大鼠mPFC可对听皮层神经元的听反应调制,这种调制可能是通过多级神经元环路实现的.     

基于最大熵的文本分类算法的改进

基于最大熵模型的文本分类算法对不同测试文档的训练结果相差较大.利用Boosting机制改进基于最大熵模型的分类算法,以提高该分类算法的稳定性.实验结果表明,该改进方法可以有效改善基于最大熵模型分类算法的稳定性,且分类精度也有一定的提高.     

The specific contribution of object''''s origin on artifacts categorization

Gelman and Bloom found that adults and children's object naming was sensitive to how an object was created (man-made or not), but they did not reveal on which specific level of conceptual system this effect was. Using a free-naming task and a force-choice task, two experiments were conducted to test a hypothesis that this effect was specifically on domain level (artifact/non-artifact distinction). In Experiment 1, participants were asked to name shortly-depicted objects, rate their confidence, and report their reasons for each naming response. Results showed that most of the naming responses in man-made condition were in artifact domain, and most in natural condition were in non-artifact domain, although in both conditions names were very divergent on basic level. In Experiment 2, another group of participants were asked to choose one from two names (one in artifact domain and the other in non-artifact domain) to match the same shortly-depicted objects presented in the first experiment. Re-sults of Experiment 1 on domain level were replicated in Experiment 2. These convergent findings supported the hypothesis that the effect of object's origin is specifically on domain level of conceptual system of objects. Reasons explicitly reported for naming responses in Experiment 1 suggested that participants might automatically infer objects' functions in man-made condition but not in natural condition. Here the function-based hypothesis of artifacts classification is discussed.     

A New Approach of Feature Selection for Text Categorization

This paper proposes a new approach of feature selection based on the independent measure between features for text categorization. A fundamental hypothesis that occurrence of the terms in documents is independent of each other, widely used in the probabilistic models for text categorization （TC）, is discussed. However, the basic hypothesis is incom plete for independence of feature set. From the view of feature selection, a new independent measure between features is designed, by which a feature selection algorithm is given to ob rain a feature subset. The selected subset is high in relevance with category and strong in independence between features, satisfies the basic hypothesis at maximum degree. Compared with other traditional feature selection method in TC （which is only taken into the relevance account）, the performance of feature subset selected by our method is prior to others with experiments on the benchmark dataset of 20 Newsgroups.     

基于命名实体的Web新闻文本分类方法

文章对Web新闻领域的文本自动分类问题进行了研究,提出一种基于新闻实体要素的分类方法;在应用空间向量模型的基础上,充分考虑命名实体对Web新闻文本分类的特殊作用,并进行了实验.实验结果表明,以新闻实体要素为特征的文本分类系统可得到较高的分类精度,该方法具有一定的实用价值.     

深矿井综采工作面端面控制

通过对深矿井开采矿山压力与开采关系的研究,分析了深矿井条件下综采工作面矿压显现特征,提出了工作面端面控制的方法和措施     

训练集类别分布对文本分类的影响

为了减小训练集中各类别资源分布不均衡对分类性能造成的影响,该文对原始训练集使用类别均衡法,即对原始训练集以类为单位进行重新组合,使得重组后的训练集类别分布尽可能均衡,从而可以在均衡的类别上进行训练和分类,以降低在训练过程中对小类别的不公平待遇.在复旦大学语料库上使用类别均衡法,分别用Naive Bayes和Rocchio方法分类,前者的宏平均F1从48.62%提高到了80.99%,后者的宏平均F1从64.58%提高到80.26%,微平均F1从73.99%提高到80.47%.实验结果显示,类别均衡法显著提高了分类性能.     

The time course of visual categorization： Electrophysiological evidence from ERP

Categorization is the process to ascertain the identity of an object by a specific standard[1,2]. Categorization isa fundamental cognitive process; in fact, the raw per- ception would be of little use without the effective clas- sification of original inf…     

基于LSA降维的KNN文本分类算法

针对文本自动分类问题,提出了一种基于LSA降维的KNN改进算法.通过对文本特征向量运用LSA理论进行降维处理,可以有效提高KNN算法的运行效率,提高分类精度.实验证明,改进的KNN算法具有很好的性能.