胎脑提取液对衰老小鼠大脑皮质神经元的影响

目的 观察胎脑提取液对衰老小鼠大脑皮质神经元酸性磷酸酶(ACP)活性和尼氏体含量的影响,探讨胎脑提取液的抗衰老作用。方法 选用健康昆明种小白鼠30只,随机分为3组;采用D-半乳糖制备亚急性衰老模型;组织化学方法显示大脑皮质神经元内的酸性磷酸酶(ACP)和尼氏体;显微图像分析仪进行定量分析。结果 衰老模型组与正常对照组相比,小鼠大脑皮质神经元ACP活性明显升高,尼氏体含量明显减少;给药组与衰老模型组相比,小鼠大脑皮质神经元ACP活性明显降低,尼氏体含量明显增加。结论 胎脑提取液可以稳定大脑皮质神经元的内环境,减少神经元的损伤,促进神经元蛋白质的合成,具有一定的抗衰老作用。     

Symmetries,non-Euclidean metrics,and patterns in a Swift–Hohenberg model of the visual cortex

The aim of this work is to investigate the effect of the shift-twist symmetry on pattern formation processes in the visual cortex. First, we describe a generic set of Riemannian metrics of the feature space of orientation preference that obeys properties of the shift-twist, translation, and reflection symmetries. Second, these metrics are embedded in a modified Swift-Hohenberg model. As a result we get a pattern formation process that resembles the pattern formation process in the visual cortex. We focus on the final stable patterns that are regular and periodic. In a third step we analyze the influences on pattern formation using weakly nonlinear theory and mode analysis. We compare the results of the present approach with earlier models.     

NeuroXidence: reliable and efficient analysis of an excess or deficiency of joint-spike events

We present a non-parametric and computationally efficient method named NeuroXidence that detects coordinated firing of two or more neurons and tests whether the observed level of coordinated firing is significantly different from that expected by chance. The method considers the full auto-structure of the data, including the changes in the rate responses and the history dependencies in the spiking activity. Also, the method accounts for trial-by-trial variability in the dataset, such as the variability of the rate responses and their latencies. NeuroXidence can be applied to short data windows lasting only tens of milliseconds, which enables the tracking of transient neuronal states correlated to information processing. We demonstrate, on both simulated data and single-unit activity recorded in cat visual cortex, that NeuroXidence discriminates reliably between significant and spurious events that occur by chance.     

An improved protocol for primary culture of cardiomyocyte from neonatal mice

The primary culture of neonatal mice cardiomyocyte model enables researchers to study and understand the morphological, biochemical, and electrophysiological characteristics of the heart, besides being a valuable tool for pharmacological and toxicological studies. Because cardiomyocytes do not proliferate after birth, primary myocardial culture is recalcitrant. The present study describes an improved method for rapid isolation of cardiomyocytes from neonatal mice, as well as the maintenance and propagation of such cultures for the long term. Immunocytochemical and gene expression data also confirmed the presence of several cardiac markers in the beating cells during the long-term culture condition used in this protocol. The whole culture process can be effectively shortened by reducing the enzyme digestion period and the cardiomyocyte enrichment step. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

内侧前额叶与社会认知

早期的研究表明杏仁核、前额叶、颞上沟、前扣带回等与人类的社会认知活动有关;随着多种新技术的应用。越来越多的研究发现其它一些脑区结构(如岛叶、基底节、白质等)也与社会认知和行为有关。本文综述了内侧前额叶在社会认知中的作用,重点介绍了内侧前额叶在心灵理论、情绪认知、社会推理与决策、道德判断、自我认知等社会认知活动中的作用。未来研究希望能从整体和动态上认识内侧前额叶在社会认知活动中的作用。     

The universal fuzzy logical framework of neural cir-cuits and its application in modeling primary visual cortex

Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells' dynamical equations. Al- though there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.     

Computational model for perception of objects and motions

Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist 'What' and 'Where' pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives 'where', for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The compu- tational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. Computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.