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.     

Exploratory study of the effect of binaural beat stimulation on the EEG activity pattern in resting state using artificial neural networks

Anxiety disorders afflict almost 7.3 percent of the world’s population. One in 14 people will experience anxiety disorder at the given year. When associated with mood disorders, anxiety can also trigger or increase other diseases’ symptoms and effects, like depression and suicidal behavior. Binaural beats are a low-frequency type of acoustic stimulation perceived when the individual is subjected to two slightly different wave frequencies, from 200 to 900 Hz. Binaural beats can contribute to anxiety reduction and modification of other psychological conditions and states, modifying cognitive processes and mood states. In this work, we applied a 5 Hz binaural beat to 6 different subjects, to detect a relevant change in their brainwaves before and after the stimuli. We applied 20 min stimuli in 10 separated sessions. We assessed the differences using a Multi-Layer Perceptron classifier in comparison with non-parametric tests and Low-Resolution Brain Electromagnetic Tomography (eLORETA). eLORETA showed remarkable changes in High Alpha. Both eLORETA and MLP approaches revealed outstanding modifications in high Beta. MLP evinced significant changes in Theta brainwaves. Our study evidenced high Alpha modulation at the limbic lobe, implicating in a possible reduction of sympathetic system activation in the studied sample. Our main results on eLORETA suggest a strong increase in the current distribution, mostly in Alpha 2, at the Anterior Cingulate, which is related to the monitoring of mistakes regarding social conduct, recognition and expression of emotions. We also found that MLPs are able of evincing the main differences with high separability in Delta and Theta.     

Brainwave classification without the help of limb movement and any stimulus for character-writing application

Recently, Brain-Computer Interfaces (BCIs) have been extensively popular for employing Electroencephalography (EEG) signals to control devices with different applications. The use of BCIs currently involves for lots of applications to help the disabilities who cannot communicate with other people, as it is an alternative way for communication by passing the need of speech. Although the applications to spell the character with BCI systems (e.g., P300-speller, SSVEP-speller, Hex-O-spell) have been already developed, but these techniques are not flexible in the real scenarios because they require the stimulus all the time or stopping the activity to focus on the limb movement in order to provide the accuracy of brain responses. In this paper, the feasibility of brainwave classification for the applications of character-writing by considering only the EEG signals without the need of stimulus unlike the literature is newly introduced. This paper adopts a classification technique named Artificial Neural Network (ANN) and focuses on two different characters; straight line and circle. From the experimental results, the suitable position of electrodes are the pair of electrodes (F3 and F4) at the frontal lobe, which provide the best result as compared to other areas due to its important role in perception, maintenance and revival of the information. The experimental results indicate that the classification accuracy of the proposed technique is about 70%, which in turn leads to a significant achievement for the development of character-writing applications.     

书写对阅读的影响——来自传统书写与电脑打字的证据

书写和阅读是在文字的基础上发展起来的, 二者存在共享的神经网络。书写能影响阅读, 阅读不仅依靠视觉加工, 还依赖书写运动表征。不同书写方式对阅读的影响不同, 传统书写对阅读有促进作用, 电脑打字对阅读产生了一定的消极影响。对汉字来说, 传统书写能加强汉字的正字法表征, 促进汉字阅读; 电脑打字对汉字阅读的影响尚无一致结论。未来研究可以考察传统书写与电脑打字神经机制的差异, 以及电脑打字对汉字认知加工的影响及神经机制。     

预先承诺的理论解释及其神经机制

预先承诺是指个体提前做出一个有长远目标的决定来限制对即时诱惑的选择,从而避免在未来决策中由于意志力缺乏而出现的自我控制失败。最后期限法和限制选择法是开展预先承诺研究的常用范式。本研究在系统回顾预先承诺的三种理论解释（自我控制资源模型、神经网络模型和时间差强化学习模型）的基础上,尝试提出一个整合性的认知神经机制模型来解释预先承诺的作用过程。未来研究可进一步丰富预先承诺的研究范式和应用范围,并探究预先承诺调控非理性决策的心理与神经机制。     

客体相似性对不同认知方式个体视觉工作记忆表征的影响

采用整体检测的变化察觉范式,探讨客体的相似性对客体—空间型认知方式个体视觉工作记忆表征的影响。行为结果显示,在不相似客体的视觉工作记忆表征任务中,客体型者在长延时条件的成绩优于空间型者,而在相似客体的视觉工作记忆表征任务中,两者无差异;ERP结果显示,在不相似客体视觉工作记忆表征任务中,与客体型者相比,空间型者在大脑皮层诱发出更大的负SP。以上结果表明,客体型者比空间型者对不相似客体视觉工作记忆加工具有优势,其潜在机制可能在于,客体型者对不相似客体视觉工作记忆表征时的神经活动效能更高。