基于传统心理学和现代认知神经科学的产品情感获取方法研究

文章从传统心理学和现代认知神经科学的双重角度出发,总结多种消费者对产品的情感获取方式,并对其进行对比,预测发展趋势,提出了只有综合运用多种方式才能更加准确有效的获取情感,并为情感量化打下基础,旨在为感性工学在中国的发展提供理论基础,从而指导产品设计的方向。     

脑启发的视觉目标识别模型研究与展望

视觉目标识别是计算机视觉领域中最基本、最具有挑战性的研究课题之一。由于灵长类出色的视觉目标识别能力，对其神经功能机理的研究可能为类脑视觉带来革命性的突破。旨在系统地回顾最近在计算神经科学和计算机视觉交叉领域的工作，研究当前基于脑启发的目标识别模型及其依据的视觉神经机制。从认知功能和皮层动力学方面总结了灵长类视觉目标识别机制的基本特征和主要贡献；根据技术架构和开发方式总结了基于大脑启发的目标识别模型及其实现类脑目标识别的优缺点。进一步对人工神经网络与视觉神经网络进行相似性分析，研究了当前流行的CNN视觉基准模型在生物学上的可信性。总结了当前视觉目标识别常用的实验设计条件和数据分析方法，可以作为一个研究人员进行视觉目标识别研究时权衡时机和条件的使用指南。     

基于D-TIN剖分的三维人脑表面模型重建算法*

为解决人脑表面结构特征建模中CT以及MRI等二维图像显示所带来的信息遗漏、观察不便等缺陷,提出了基于D-TIN（Delaunay TIN）剖分的三维人脑表面模型建模策略。针对实体模型分层切片数据,以离散拟合思想为算法依据,采用Delaunay不规则三角网剖分技术,动态构建模型表面控制点之间的拓扑关系,以此生成三维人脑表面模型;进而结合OpenGL可视化工具,对模型进行可视化渲染,最终得到满足医学需要的三维人脑实体模型。该模型直观、清晰、全面地再现了人脑的表面结构,对脑部病变的认知、手术指导以及认知神经科学     

头皮脑电采集技术研究

脑电能反映人脑的健康及认知活动状况,是脑疾病诊治及认知神经科学的重要参数。脑电监测也是脑机接口的重要手段。其中,头皮脑电采集技术相对于颅内脑电采集技术具备无创的优点,相对于前额脑电采集能提供多通道多脑区的脑电信号,用途最广泛。然而头发遮蔽影响脑电采集性能,从而限制其应用。对头皮脑电采集技术的电极器件及可穿戴系统进行了综述研究,分析该领域国内外科研及产业化进展情况,并从新型材料、先进结构和加工工艺、系统集成三方面对头皮脑电采集技术的发展进行展望。研究对于头皮脑电采集技术的发展具有指导价值与参考意义。     

基于fNIRS与SiPM的脑血氧检测电路设计

脑血氧检测作为大脑功能检测之一,是认知神经科学和生物医学领域必不可少的研究工具。其研究方法之一——功能性近红外光谱技术,利用组织中血液的主要成分对近红外光的良好吸收性和散射性,而获得组织内氧合血红蛋白和脱氧血红蛋白的变化情况,从而实现对脑血氧的检测。硅光电倍增器件是近几年兴起的光电探测器件,具有尺寸小、增益高、工作电压低、对磁场不敏感等特点。为了检测脑血氧的变化,设计一个基于近红外光谱技术与硅光电倍增管的新型脑血氧检测电路,并通过前臂阻断实验验证了电路设计的性能满足需求。     

基于SVM的fMRI数据分类:一种解码思维的方法

使用机器学习分类f MRI数据的方法已逐渐被应用到解码思维状态的研究中.对比了使用血氧含量水平(blood oxygenlevel dependent,BOLD)累计变化和使用BOLD变化时间序列作为特征值训练SVM分类器,并依此来判断人脑正在执行的高级思维类型.在预测4×4 Sudoku问题类型的实验中,使用BOLD时间序列为特征的方法分类正确率较高.通过分析分类正确率较高的voxel的解剖结构,发现很多voxel位于前额、顶叶、前扣带回等与高级思维关系密切的脑区,实验结论与认知神经科学相关结论吻合.该方法可以进一步应用在脑机接口(brain computer interface,BCI)等领域.     

可视分析中的复杂认知活动研究

可视分析领域中有众多技术和工具能很好地帮助用户发现一些关于数据的灼见,然而随着数据日益庞大,需要解决的问题日益复杂,这些灼见对用户来说还远远不够.可视分析工具需要将这些灼见整合起来,支持用户进行复杂的认知活动,如问题解决、意义构建、分析推理等.文中主要是梳理了可视化领域中的认知理论和复杂认知活动的研究工作,并在此基础上,探讨了如何设计可视分析工具来更好地支持复杂认知活动.文中观点是在可视分析工具的设计中可以将复杂认知活动自顶向下逐步分解,打开复杂认知活动的黑盒,可视化表示也应该由复杂的认知活动来驱动.     

认知无线网络的认知能力保障方法研究综述

认知无线网络通过对无线环境的感知获得频谱空洞信息,并以动态频谱接入为关键技术,机会地使用空闲频谱,与传统的无线网络相比,具有更高的网络通信资源利用率。认知能力作为认知无线网络的本质属性,其安全保障直接关系到认知无线网络的实用化进程。由于网络结构的复杂性、节点的移动性等问题,认知能力在各个认知环节中均面临不同程度的安全威胁。首先从认知循环:认知通信、资源感知、推理决策和服务适配4个环节分析认知能力保障所面临的安全问题以及相应的解决方案,然后总结现有方法的不足和缺陷,提出认知能力保障策略基于信任机制的发展趋势;最后指出未来认知能力保障领域所面临的挑战,并对该领域的研究方向进行了展望。     

人工智能在CRN中环境感知与推理阶段的应用

0引言
　　认知无线电技术是无线通信领域与人工智能领域相结合的产物,其核心思想是使无线电设备像人一样具备智能,通过感知无线通信环境,根据一定的学习和决策算法,实时、自适应地改变系统工作参数,动态地检测和有效利用空闲频谱,在时间、频率以及空间上实现多维的频谱复用。因此,将人工智能领域的相关理论和方法引入到通信网络决策中,实现通信系统的“认知”智能化,对发展认知无线电具有重要的科学意义和迫切的现实意义。     

基于认知广度和深度的个性化信息检索模型

受心理学激活-扩散模型的启发,提出了在领域本体基础上的用户认知结构模型。该模型依据用户提供的认知中心,一方面,根据领域本体中概念之间的语义相关性推导出用户的认知范围;另一方面,根据概念之间的语义相关度刻画出用户的认知深度。从认知范围和认知深度两方面,描述用户对某领域知识的认知结构。实验结果表明,该模型与通用本体模型相比,具有较高的查准     

大规模类脑计算系统BiCoSS: 架构、实现及应用

人脑具有协同多种认知功能的能力与极强的自主学习能力, 随着脑与神经科学的快速发展, 亟需计算结构模拟人脑的、性能更强大的计算平台进行人脑智能与认知行为机制的进一步探索. 受人脑神经机制的启发, 本文提出了基于神经认知计算架构的众核类脑计算系统BiCoSS, 该系统以并行计算的现场可编程门阵列(Field-programmable gate array, FPGA)为核心处理器, 以地址事件表达的神经放电作为信息传递载体, 以具有认知计算功能的神经元作为信息处理单元, 实现了四百万神经元数量级大规模神经元网络认知行为的实时计算, 填补了从细胞动力学层面理解人脑认知功能的鸿沟. 实验结果从计算能力、计算效率、功耗、通信效率、可扩展性等方面显示了BiCoSS系统的优越性能. BiCoSS通过人脑信息处理的计算架构以更贴近神经科学本质的模式实现了类脑智能; 同时, BiCoSS为神经认知和类脑计算的研究和应用提供了新的有效手段.     

Computational intelligent brain computer interaction and its applications on driving cognition

Driving is one of the most common attention-demanding tasks in daily life. Driver's fatigue, drowsiness, inattention, and distraction are reported a major causal factor in many traffic accidents. Due to the drivers lost their attention, they had markedly reduced the perception, recognition and vehicle control abilities. In recent years, many computational intelligent technologies were developed for preventing traffic accidents caused by driver's inattention. Driver's drowsiness and distraction related studies had become a major interest research topic in automotive safety engineering. Many researches had investigated the driving cognition in cognitive neuro-engineering, but how to utilize the main findings of driving-related cognitive researches in traditional cognitive neuroscience and integrate with computational intelligence technologies for augmenting driving performance will become a big challenge in the interdisciplinary research area. For this reason, we attempt to integrate the driving cognition for real life application in this study. The implications of the driving cognition in cognitive neuroscience and computational intelligence for daily applications are also demonstrated through two common attention-related driving studies: (1) cognitive-state monitoring of the driver performing the realistic long-term driving tasks in a simulated realistic-driving environment; and (2) to extract the brain dynamic changes of driver's distraction effect during dual-task driving. Experimental results of these studies provide new insights into the understanding of complex brain functions of participants actively performing ordinary tasks in natural body positions and situations within real operational environments.     

Understanding Neural Complexity: A Role For Reduction

Psychoneural reduction is under attack again, only this time from a former ally: cognitive neuroscience. It has become popular to think of the brain as a complex system whose theoretically important properties emerge from dynamic, non-linear interactions between its component parts. ``Emergence' is supposed to replace reduction: the latter is thought to be incapable of explaining the brain qua complex system. Rather than engage this issue at the level of theories of reduction versus theories of emergence, I here emphasize a role that reductionism plays – and will continue to play – even if neuroscience adopts this ``complex systems' view. In detailed investigations into the function of complex neural circuits, certain questions can only be addressed by moving down levels and scales. This role for reduction also finds a place for approaches that dominate mainstream neuroscience, like the widespread use of experimental techniques and theories from molecular biology and biochemistry. These are difficult to reconcile with the anti-reductionist sentiments of the ``complex systems' branch of cognitive neuroscience.     

Cognitive robotics: new insights into robot and human intelligence by reverse engineering brain functions [From the Guest Editors]

Some articles in this special issue on cognitive robotics focus on replicating human and animal behaviors in robots, while others look into using robotic analogs of the brain to provide insight into the neuroscience.     

Brain activity in the resting state: default mode network as an inside story within the brain

As a promising research field after the turn of the new century, a default mode network (DMN) in the brain shows strong potential as a new breakthrough in neuroscience. This approach emphasizes the baseline of the brain’s activities when the brain is awake but is not receiving any external input signal. The study of DMN has recently been highlighted, and is expected to provide a key to understanding mental disorders. This article consists of two sections: (1) a brief tutorial on DMN is presented, together with some necessary fundamental knowledge of neuroscience, and (2) a framework of network informatics for the DMN is proposed based on network dynamics. Models of information networks are discussed to bridge the gap between the level of regions and the level of neurons in the brain, and major issues about analyzing the DMN by brain imaging technologies are also discussed. One of the inspirations arising from the DMN approach is how spontaneous collective behavior emerges within an autonomous system. This is crucial to a systematic understanding of the brain’s function, and to exploring some new design principles of autonomous robotics in order to demonstrate complex life-like behaviors in engineering.     

Integrating neuroscience,psychology, and evolutionary biology through a teleological conception of function

The idea of integrating evolutionary biology and psychology has great promise, but one that will be compromised if psychological functions are conceived too abstractly and neuroscience is not allowed to play a contructive role. We argue that the proper integration of neuroscience, psychology, and evolutionary biology requires a telelogical as opposed to a merely componential analysis of function. A teleological analysis is required in neuroscience itself; we point to traditional and curent research methods in neuroscience, which make critical use of distinctly teleological functional considerations in brain cartography. Only by invoking teleological criteria can researchers distinguish the fruitful ways of identifying brain components from the myriad of possible ways. One likely reason for reluctance to turn to neuroscience is fear of reduction, but we argue that, in the context of a teleological perspective on function, this concern is misplaced. Adducing such theoretical considerations as top-down and bottom-up constraints on neuroscientific and psychological models, as well as existing cases of productive, multidisciplinary cooperation, we argue that integration of neuroscience into psychology and evolutionary biology is likely to be mutually beneficial. We also show how it can be accommodated methodologically within the framework of an interfield theory.     

神经网络的集合运算

计算神经科学是在视觉计算理论影响下最近才产生的一个新的神经科学分支,其目的在于把从事人工智能、工程学和神经科学研究的科学家组织起来,共同开创一条利用各学科交叉研究脑信息处理的新路.本文介绍模型神经网络的集合运算性质,着重论述 Hopfield 提出的研究神经网络计算特性的理论框架及其在视觉中的应用.在这个基础上,我们分析了 Hopfield 模型的意义.     

受限波兹曼机联合稀疏近似的脑功能检测模型

人脑功能连通性检测是神经科学研究的重要技术．使用受限制波兹曼机（RestrictedBoltzmannMachine,RBM）对大量多被试功能磁共振（functionalMagneticResonanceImaging,fMRI）数据进行建模可以检测人脑功能连接,但是不能有效检测单被试数据的功能连接．本文研究一种新颖的融合了稀疏近似与RBM技术的脑功能连通性检测模型,该模型充分利用fMRI数据的稀疏性,采用稀疏近似理论对fMRI数据进行空间域稀疏近似压缩,然后使用RBM建立模型,以检测脑功能连通性．实验结果表明,该融合模型可以有效地提取单被试数据的脑功能时间域混合模型及其相应的脑功能图谱,解决了RBM在单被试数据分析上的瓶颈．     

Mind Reading — Neuroimaging as a gaze into the innermost of the psyche

Neuroscience, broadly speaking the endeavor of understanding nerve system functioning and its relation to perception, feeling, thinking, and behavior, is one of the leading fields in contemporary research. However, in order to determine the legal implications of neuroscience it is necessary in the first place to reflect on what it means to carry out an experiment with human subjects, particularly to transfer a finding from a laboratory setting to the real life world of people. This reflection will be done in the first part of this article. In its second part, this article explains basic methodological and experimental issues of fMRI (functional magnetic resonance imaging) research, particularly the nature of the data that are generated with this technique which is considered the most important to investigate human brain function nowadays. The concluding section relates these issues to legally salient questions of neuroscience research.     

视觉审美体验的类脑计算模型研究进展

目前神经科学与信息科学交叉发展的一个重要方向是面向高级认知功能的神经系统模型的构建,其标志着人类对自身特有的心智活动的研究进入到一个新阶段。简要综述了关于视觉审美体验的生理基础研究的进展,指出大脑神经回路是视觉审美体验的生理基础,其所涉及的大脑皮层和脑组织在视觉情感感知中功能的科学解析是构建类脑计算模型的基础。分别介绍了神经信息学、神经认知科学和神经美学框架下的视觉审美体验的类脑计算模型,并对模型进行了比较分析。对视觉审美体验的类脑计算模型研究的发展趋势进行了展望,阐述了该领域研究的意义。视觉审美体验类脑模型的构建有助于揭示大脑皮层和脑组织在视觉情感感知中的功能,有助于实现视觉情感体验的认知模拟。