Unusual neural connection between injured cingulum and brainstem in a patient with subarachnoid hemorrhage

The human brain is known to have six cholinergic nudei （Selden et al., 1998; Nieuwenhuys et al., 2008）. The cerebral cortex obtains cholinergic innervation mainly from the basalis nucleus of Meynert （Ch 4） in the bas- al forebrain through the medial and lateral cholinergic pathways （Selden et al., 1998; Mesulam et al., 1983）. The cingulum, the neural fiber bundle connecting the basal forebrain and the medial temporal lobe, contains the medial cholinergic pathway （Selden et al., 1998; Hong and Jang, 2010）.

Artificial neural network-based subgrid-scale models for LES of compressible turbulent channel flow

Fully connected neural networks(FCNNs)have been developed for the closure of subgrid-scale(SGS)stress and SGS heat flux in large-eddy simulations of compressible turbulent channel flow.The FCNNbased SGS model trained using data with Mach number Ma=3.0 and Reynolds number Re=3000 was applied to situations with different Mach numbers and Reynolds numbers.The input variables of the neural network model were the filtered velocity gradients and temperature gradients at a single spatial grid point.The a priori test showed that the FCNN model had a correlation coefficient larger than 0.91 and a relative error smaller than 0.43,with much better reconstructions of SGS unclosed terms than the dynamic Smagorinsky model(DSM).In a posteriori test,the behavior of the FCNN model was marginally better than that of the DSM in predicting the mean velocity profiles,mean temperature profiles,turbulent intensities,total Reynolds stress,total Reynolds heat flux,and mean SGS flux of kinetic energy,and outperformed the Smagorinsky model.

Interpretation and characterization of rate of penetration intelligent prediction model

Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations and machine learning algorithms,its lack of interpretability undermines its credibility.This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation function.By leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector operations.The FNN model is linearly characterized through further simplification,enabling its interpretation and analysis.The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield.The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well.The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity.In the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a corresponding linear representation equation.Furthermore,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section.The established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.

Structural neural connectivity of the vestibular nuclei in the human brain:a diffusion tensor imaging study

Many animal studies have reported on the neural connectivity of the vestibular nuclei（VN）.However,little is reported on the structural neural connectivity of the VN in the human brain.In this study,we attempted to investigate the structural neural connectivity of the VN in 37 healthy subjects using diffusion tensor tractography.A seed region of interest was placed on the isolated VN using probabilistic diffusion tensor tractography.Connectivity was defined as the incidence of connection between the VN and each brain region.The VN showed 100% connectivity with the cerebellum,thalamus,oculomotor nucleus,trochlear nucleus,abducens nucleus,and reticular formation,irrespective of thresholds.At the threshold of 5 streamlines,the VN showed connectivity with the primary motor cortex（95.9%）,primary somatosensory cortex（90.5%）,premotor cortex（87.8%）,hypothalamus（86.5%）,posterior parietal cortex（75.7%）,lateral prefrontal cortex（70.3%）,ventromedial prefrontal cortex（51.4%）,and orbitofrontal cortex（40.5%）,respectively.These results suggest that the VN showed high connectivity with the cerebellum,thalamus,oculomotor nucleus,trochlear nucleus,abducens nucleus,and reticular formation,which are the brain regions related to the functions of the VN,including equilibrium,control of eye movements,conscious perception of movement,and spatial orientation.

Fully Connected Feedforward Neural Networks Based CSI Feedback Algorithm

In modern wireless communication systems,the accurate acquisition of channel state information(CSI)is critical to the performance of beamforming,non-orthogonal multiple access(NOMA),etc.However,with the application of massive MIMO in 5G,the number of antennas increases by hundreds or even thousands times,which leads to excessive feedback overhead and poses a huge challenge to the conventional channel state information feedback scheme.In this paper,by using deep learning technology,we develop a system framework for CSI feedback based on fully connected feedforward neural networks(FCFNN),named CF-FCFNN.Through learning the training set composed of CSI,CF-FCFNN is able to recover the original CSI from the compressed CSI more accurately compared with the existing method based on deep learning without increasing the algorithm complexity.

Central projections and connections of lumbar primary afferent fibers in adult rats:effectively revealed using Texas red-dextran amine tracing

Signals from lumbar primary afferent fibers are important for modulating locomotion of the hind-limbs.However,silver impregnation techniques,autoradiography,wheat germ agglutinin-horseradish peroxidase and cholera toxin B subunit-horseradish peroxidase cannot image the central projections and connections of the dorsal root in detail.Thus,we injected 3-k Da Texas red-dextran amine into the proximal trunks of L4 dorsal roots in adult rats.Confocal microscopy results revealed that numerous labeled arborizations and varicosities extended to the dorsal horn from T12–S4,to Clarke＇s column from T10–L2,and to the ventral horn from L1–5.The labeled varicosities at the L4 cord level were very dense,particularly in laminae I–Ⅲ,and the density decreased gradually in more rostral and caudal segments.In addition,they were predominately distributed in laminae I–IV,moderately in laminae V–VⅡ and sparsely in laminae VⅢ–X.Furthermore,direct contacts of lumbar afferent fibers with propriospinal neurons were widespread in gray matter.In conclusion,the projection and connection patterns of L4 afferents were illustrated in detail by Texas red-dextran amine-dorsal root tracing.

Perspectives on the neural connectivity of the fornix in the human brain

The fornix is involved in the transfer of information on episodic memory as a part of the Papez circuit. Diffusion tensor imaging enables to estimate the neural connectivity of the fornix. The anterior fornical body has high connectivity with the anterior commissure, and brain areas rele- vant to cholinergic nuclei （septal forebrain region and brainstem） and memory function （medial temporal lobe）. In the normal subjects, by contrast, the posterior fornical body has connectivity with the cerebral cortex and brainstem through the splenium of the corpus callosum. We believe that knowledge of the neural connectivity of the fornix would be helpful in investigation of the neural network associated with memory and recovery mechanisms following injury of the fornix.

联接主义智能控制综述

综述了近年来联接主义智能控制的理论和应用上的研究进展 ,覆盖了神经网络的逼近和泛化能力、神经网络与混沌、监督学习算法等基本性质 ,以及神经网络建模、预测、优化和控制等联接主义智能控制系统的各个部分 ,并对今后的研究发展提出了展望 .

认知主义与联结主义之比较

认知主义的研究定向和联结主义的研究定向是广义的现代认知心理学的两种主要研究范式。这两种研究范式都各有自己的研究内容和方法论,也取得了各自不同的成就,同时也存在各自不同的问题。通过对两种研究范式的比较,我们可以清楚地认识到二者的相互沟通和融合才是认知心理学未来发展的必然趋势。

人工神经网研究的剖析与探讨

本文概述了人工神经网与生物神经网研究的关系．给出了确立人工神经网中的基本原理和模式的生物神经网的依据。分析了人工神经网在各种意义下的分类及研究的基本内容。指出了目前若干研究动向和前沿性课题。

连接论:对基于计算机隐喻的认知模型的质疑

本文比较了基于计算机隐喻的传统认知处理模型的内容与特点和新兴起的连接论的内容与特点，认为，连接论将取代传统的基于计算机隐喻的认知处理模型成为今后指导人类认知研究的主流思想；语言学等学科要取得突破必须抛弃计算机隐喻的影响。

Connectivity differences between adult male and female patients with attention deficit hyperactivity disorder according to resting-state functional MRI

Attention deficit hyperactivity disorder（ADHD） is a pervasive psychiatric disorder that affects both children and adults. Adult male and female patients with ADHD are differentially affected, but few studies have explored the differences. The purpose of this study was to quantify differences between adult male and female patients with ADHD based on neuroimaging and connectivity analysis. Resting-state functional magnetic resonance imaging scans were obtained and preprocessed in 82 patients. Group-wise differences between male and female patients were quantified using degree centrality for different brain regions. The medial-, middle-, and inferior-frontal gyrus, superior parietal lobule, precuneus, supramarginal gyrus, superior- and middle-temporal gyrus, middle occipital gyrus, and cuneus were identified as regions with significant group-wise differences. The identified regions were correlated with clinical scores reflecting depression and anxiety and significant correlations were found. Adult ADHD patients exhibit different levels of depression and anxiety depending on sex, and our study provides insight into how changes in brain circuitry might differentially impact male and female ADHD patients.

神经元网络动态认知过程与第二语言习得

连接论的神经元网络动态认知过程相对于其它理论比较清晰地解释了二语习得中的一些问题及现象,如母语迁移,二语习得而非学得,以及二语被大脑习得的整个较为清晰的动态过程。除此之外,连接论还解答了两个重要问题,那就是可理解性输入到底输入的应该是什么;为什么输入必须是可理解或被理解的。本文将就此进行分析和探讨。

AN^2 APPLICATION IN THE METRODS OF POPULATION GEOGRAPHY

This paper tries to present another theoretical view in the study of population geography by applying the principle of artificial neural network.It is our view that the approach to population geography study is of two kinds so far: the synthetic analysis and An2 synthetic analysis.

语义联想省略的认知模式

省略是一种常见的语言现象,分类不一,本文的语义联想省略是建立在语义场基础之上的一种语意从缺,省略的内容无法从上下文中回找,必须依托一条合理的认知途径来理解语义省略,这条认知途径就是联想。人类的神经网络为联想提供了生理基础,联结主义认知模式为联想提供了认知基础,语义联想场本身也是一个让人产生丰富联想的词汇网络。为了解决语义联想省略的理解问题,本文提出了"语义联想场—联结主义—神经网络"的循环认知模式,并对此进行论证。

一个基于连通主义的二语习得认知过程模型

连通主义自二十世纪八十年代后期以来是认知心理学的主导理论 ,它被广泛应用于包括语言学在内的各个领域。该理论不强调规则的习得 ,认为语言学习作为技能训练过程与其他技能无甚区别 ,网络在不断接收输入的过程中通过自适应、自组织性的学习实现发展与提升。目前连通主义在语言学中一般被用来进行母语研究。本文试图在深入介绍连通主义理论与网络工作特性的基础上建立一个二语习得认知过程的连通主义网络模型 ,分析图中体现的二语学习特点与连通主义具体运作特征 ,验证若干二语习得理论的合理性。

机器翻译与人工翻译相辅相成

本文介绍了基于规则的机器翻译、统计机器翻译和神经机器翻译的发展历程,阐释机器翻译研究需要有语言学知识和常识的支持,主张不要过分地迷信目前广为流行的基于语言大数据的连接主义方法,不要轻易地忽视目前受到冷落的基于语言规则与常识的符号主义方法,应当把基于语言大数据的连接主义方法和基于语言规则与常识的符号主义方法巧妙、精准地结合起来,把机器翻译研究推向深入。本文指出,机器翻译将成为人工翻译的好朋友和得力助手,机器翻译和人工翻译应当和谐共生,相得益彰。

Applying Big Data Based Deep Learning System to Intrusion Detection

With vast amounts of data being generated daily and the ever increasing interconnectivity of the world’s internet infrastructures,a machine learning based Intrusion Detection Systems(IDS)has become a vital component to protect our economic and national security.Previous shallow learning and deep learning strategies adopt the single learning model approach for intrusion detection.The single learning model approach may experience problems to understand increasingly complicated data distribution of intrusion patterns.Particularly,the single deep learning model may not be effective to capture unique patterns from intrusive attacks having a small number of samples.In order to further enhance the performance of machine learning based IDS,we propose the Big Data based Hierarchical Deep Learning System(BDHDLS).BDHDLS utilizes behavioral features and content features to understand both network traffic characteristics and information stored in the payload.Each deep learning model in the BDHDLS concentrates its efforts to learn the unique data distribution in one cluster.This strategy can increase the detection rate of intrusive attacks as compared to the previous single learning model approaches.Based on parallel training strategy and big data techniques,the model construction time of BDHDLS is reduced substantially when multiple machines are deployed.