情绪加工的性别差异及神经机制

已有研究表明, 情绪加工存在显著性别差异。这主要表现为女性人群具有情绪识别优势, 更好的情绪记忆能力与更强的负性情绪易感性。此外, 情绪加工的性别差异也表现为情绪调节过程的不同：相比男性, 女性更善于抑制情绪行为, 却较难通过认知策略调节负性情绪。情绪加工的性别差异有重要生理基础, 与情绪脑结构, 荷尔蒙水平的男女差异有关。同时, 情绪加工的性别差异同女性更高的情绪障碍易感性有着密切联系：不善于调节负性情绪, 及更强的消极情绪易感性可能是女性更易患情绪障碍的重要原因。从情绪加工角度探讨情绪障碍的性别差异及其原因, 对于情绪障碍的预防和治疗具有重要意义。     

从ERP研究看前瞻记忆的神经基础和认知加工机制

关于前瞻记忆的ERP研究主要关注两个方面：神经基础和认知加工机制。神经基础方面的研究发现：前瞻记忆诱发了两种特异性的ERP成分—— N300和前瞻性正波; 前瞻记忆与回溯性记忆的神经基础有相似又有不同; 老年人的前瞻记忆可能存在一种不同于年轻人的神经机制。认知加工机制方面的研究主要为前瞻记忆的预备注意加工和记忆加工理论提供了ERP方面的证据支持。     

老年执行功能的认知可塑性和神经可塑性

执行衰退假说认为执行功能的特异性衰退是引起认知年老化的主要原因, 近年来越来越多的研究表明, 老年人的执行功能及其相关脑区(主要为前额叶)存在可塑性, 通过训练执行功能的衰退可得到缓解, 且相关脑区的激活水平、脑容量或神经递质都可发生改变; 部分研究还发现执行功能训练对其它认知能力有一定的迁移效应。这些发现对于认知年老化理论的继续探索和认知干预研究的实践应用都具有重要意义。     

时间性前瞻记忆的影响因素及机制

时间性前瞻记忆是指个体记得在未来的某一时间或间隔一段时间以后做某事。与事件性前瞻记忆相比, 时间性前瞻记忆需要更多自我启动的注意资源, 并主要受到任务重要性、年龄、进行中任务的性质、时间间隔等因素的影响。时间性前瞻记忆主要与额叶有关, 对于其内部认知机制解释的理论模型主要有测试-等待-测试-退出模型以及注意阀门模型。但是时间性前瞻记忆的影响因素以及机制, 尤其是神经机制方面尚需要进一步的研究。     

学习和记忆的无意识研究

内隐学习和内隐记忆的研究代表了人类学习和记忆的无意识过程。在过去的40年里,内隐学习和内隐记忆的研究经历了：研究对象从人工材料走向真实生活,理论观点从分离走向协同,研究方法从单一走向多样化,以及人工神经网络模型中学习和记忆过程的模拟等。它不仅对学习和记忆本身的心理机制得到了更多的理解,而且还为整个心理学特别是认知心理学的研究开辟了广阔的前景。具体表现为多重记忆的划分、无意识研究的异军突起、研究方法的突破扩展和交叉学科的融会贯通     

工作记忆的认知模型与神经机制

本文评述了工作记忆认知模型的进展,阐述工作记忆神经机制研究的基本问题。研究者在工作记忆认知模型框架下,提出了工作记忆成分结构的脑模型。根据自己的研究探索,展望进一步的研究问题,以及TMS和fMRI/ERP等脑成像技术的结合运用对工作记忆认知神经科学研究的推动。     

注意瞬脱神经机制的研究

在很短的时间间隔内连续呈现两个目标刺激时,被试对第二个目标刺激的正确报告率显著下降,这种现象就是注意瞬脱.该文简述了注意瞬脱的概念、容量有限性等方面,尤其详细介绍了注意瞬脱神经机制研究的新进展,并对今后的研究提出展望.     

An improved learning algorithm for random neural networks based on particle swarm optimization and input-to-output sensitivity

Extreme learning machine (ELM) for random single-hidden-layer feedforward neural networks (RSLFN) has been widely applied in many fields in the past ten years because of its fast learning speed and good generalization performance. But because traditional ELM randomly selects the input weights and hidden biases, it typically requires high number of hidden neurons and thus decreases its convergence performance. It is necessary to select optimal input weights and hidden biases to improve the convergence performance of the traditional ELM. Generally, the single-hidden-layer feedforward neural networks (SLFN) with low input-to-output sensitivity will cause good robustness of the network, which may further lead into good generalization performance. Moreover, particle swarm optimization (PSO) has no complicated evolutionary operators and fewer parameters need to adjust, and is easy to implement. In this study, an improved ELM based on PSO and input-to-output sensitivity information is proposed to improve RSLFN’s convergence performance. In the improved ELM, PSO encoding the input to output sensitivity information of the SLFN is used to optimize the input weights and hidden biases. The improved ELM could obtain better generalization performance as well as improve the conditioning of the SLFN by decreasing the input-to-output sensitivity of the network. Finally, experiment results on the regression and classification problems verify the improved performance the proposed ELM.     

A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images

Computer Aided Decision (CAD) systems, based on 3D tomosynthesis imaging, could support radiologists in classifying different kinds of breast lesions and then improve the diagnosis of breast cancer (BC) with a lower X-ray dose than in Computer Tomography (CT) systems.In previous work, several Convolutional Neural Network (CNN) architectures were evaluated to discriminate four different classes of lesions considering high-resolution images automatically segmented: (a) irregular opacity lesions, (b) regular opacity lesions, (c) stellar opacity lesions and (d) no-lesions. In this paper, instead, we use the same previously extracted relevant Regions of Interest (ROIs) containing the lesions, but we propose and evaluate two different approaches to better discriminate among the four classes.In this work, we evaluate and compare the performance of two different frameworks both considering supervised classifiers topologies. The first framework is feature-based, and consider morphological and textural hand-crafted features, extracted from each ROI, as input to optimised Artificial Neural Network (ANN) classifiers. The second framework, instead, considers non-neural classifiers based on automatically computed features evaluating the classification performance extracting several sets of features using different Convolutional Neural Network models.Final results show that the second framework, based on features computed automatically by CNN architectures performs better than the first approach, in terms of accuracy, specificity, and sensitivity.     

Three-channel convolutional neural networks for vegetable leaf disease recognition

The color information of diseased leaf is the main basis for leaf based plant disease recognition. To make use of color information, a novel three-channel convolutional neural networks (TCCNN) model is constructed by combining three color components for vegetable leaf disease recognition. In the model, each channel of TCCNN is fed by one of three color components of RGB diseased leaf image, the convolutional feature in each CNN is learned and transmitted to the next convolutional layer and pooling layer in turn, then the features are fused through a fully connected fusion layer to get a deep-level disease recognition feature vector. Finally, a softmax layer makes use of the feature vector to classify the input images into the predefined classes. The proposed method can automatically learn the representative features from the complex diseased leaf images, and effectively recognize vegetable diseases. The experimental results validate that the proposed method outperforms the state-of-the-art methods of the vegetable leaf disease recognition.