Orbitofrontal dysfunction in patients with obsessive-compulsive disorder and their unaffected relatives

Obsessive-compulsive disorder (OCD) is characterized by repetitive thoughts and behaviors associated with underlying dysregulation of frontostriatal circuitry. Central to neurobiological models of OCD is the orbitofrontal cortex, a neural region that facilitates behavioral flexibility after negative feedback (reversal learning). We identified abnormally reduced activation of several cortical regions, including the lateral orbitofrontal cortex, during reversal learning in OCD patients and their clinically unaffected close relatives, supporting the existence of an underlying previously undiscovered endophenotype for this disorder.     

Social decision-making: insights from game theory and neuroscience

By combining the models and tasks of Game Theory with modern psychological and neuroscientific methods, the neuroeconomic approach to the study of social decision-making has the potential to extend our knowledge of brain mechanisms involved in social decisions and to advance theoretical models of how we make decisions in a rich, interactive environment. Research has already begun to illustrate how social exchange can act directly on the brain's reward system, how affective factors play an important role in bargaining and competitive games, and how the ability to assess another's intentions is related to strategic play. These findings provide a fruitful starting point for improved models of social decision-making, informed by the formal mathematical approach of economics and constrained by known neural mechanisms.     

稻米品质综合评价的人工神经网络方法

基于人工神经网络 (ANN)理论 ,面向稻米品质的综合评价问题 ,分别开发了 SOM和 BP神经网模型 ;研究了模型的设计、利用观测数据建立网络结构训练样本集以及网络学习等问题 ;从不同角度分别对杂交籼稻雄性不育保持系和恢复系两组供试亲本的品质样本进行聚类和综合评价 .实际仿真结果表明 ,ANN用于米质评价是科学、有效的 ,而且方法简便、速度快     

Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication

We present a method for learning nonlinear systems, echo state networks (ESNs). ESNs employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains. The learning method is computationally efficient and easy to use. On a benchmark task of predicting a chaotic time series, accuracy is improved by a factor of 2400 over previous techniques. The potential for engineering applications is illustrated by equalizing a communication channel, where the signal error rate is improved by two orders of magnitude.     

The neurobiology of learning and memory

Study of the neurobiology of learning and memory is in a most exciting phase. Behavioral studies in animals are characterizing the categories and properties of learning and memory; essential memory trace circuits in the brain are being defined and localized in mammalian models; work on human memory and the brain is identifying neuronal systems involved in memory; the neuronal, neurochemical, molecular, and biophysical substrates of memory are beginning to be understood in both invertebrate and vertebrate systems; and theoretical and mathematical analysis of basic associative learning and of neuronal networks in proceeding apace. Likely applications of this new understanding of the neural bases of learning and memory range from education to the treatment of learning disabilities to the design of new artificial intelligence systems.     

Intelligent diagnosis of northern corn leaf blight with deep learning model

Maize (Zea mays L.), also known as corn, is the third most cultivated crop in the world. Northern corn leaf blight (NCLB) is a globally devastating maize foliar disease caused by Setosphaeria turcica (Luttrell) Leonard and Suggs. Early intelligent diagnosis and warning is an effective and economical strategy to control this disease. Today, deep learning is beginning to play an essential role in agriculture. Notably, deep convolutional neural networks (DCNN) are amongst the most successful machine learning techniques in plant disease detection and diagnosis. Our study aims to identify NCLB in the maize-producing area in Jilin Province based on several DCNN models. We established a database of 985 leaf images of healthy and infected maize and applied data augmentation techniques including image segmentation, image resizing, image cropping, and image transformation, to expand to 30 655 images. Several proven convolutional neural networks, such as AlexNet, GoogleNet, VGG16, and VGG19, were then used to identify diseases. Based on the best performance of the DCNN pre-trained model GoogleNet, some of the recent loss functions developed for deep facial recognition tasks such as ArcFace, CosFace, and A-Softmax were applied to detect NCLB. We found that a pre-trained GoogleNet architecture with the Softmax loss function can achieve an excellent accuracy of 99.94% on NCLB diagnosis. The analysis was implemented in Python with two deep learning frameworks, Pytorch and Keras. The techniques, training, validation, and test results are presented in this paper. Overall, our study explores intelligent identification technology for NCLB and effectively diagnoses NCLB from images of maize.     

基于组合式神经网络的柴油机性能评估预测模型

在分析发动机结构参数和运转参数对发动机性能影响的基础上,提出了一种基于组合式神经网络的柴油机性能状态评估预测模型.该模型首先运用动态聚类法将大样本分成若干小组,然后分别用于子网络训练.性能评估时,运用模糊识别法选择相关的子网络进行评估分析.实例验证表明,这种模型能有效解决大样本下神经网络训练速度慢和难以收敛的问题,提高柴油机性能评估预测精度.     

Regularization algorithms for learning that are equivalent to multilayer networks

Learning an input-output mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multidimensional function (that is, solving the problem of hypersurface reconstruction). From this point of view, this form of learning is closely related to classical approximation techniques, such as generalized splines and regularization theory. A theory is reported that shows the equivalence between regularization and a class of three-layer networks called regularization networks or hyper basis functions. These networks are not only equivalent to generalized splines but are also closely related to the classical radial basis functions used for interpolation tasks and to several pattern recognition and neural network algorithms. They also have an interesting interpretation in terms of prototypes that are synthesized and optimally combined during the learning stage.     

基于残差网络与迁移学习的柑橘类型识别

传统的柑橘分类依靠人工进行辨识再手动完成分拣,整个过程耗费时间且成本高昂。对此提出了一种基于迁移学习与残差网络的柑橘图像分类方法。对Kaggle获取的20738张共8类柑橘的图像按7：3比例进行划分得到数据集。在此数据集上不同网络对于柑橘分类性能差异以及迁移学习对经典卷积模型在图像分类任务中的性能提升进行探究,实验以损失值、精准率、召回率等为性能评价指标。实验结果表明,在多种模型中,残差神经网络能获得比其他网络更高的准确率,使用迁移学习初始化网络参数能显著提高柑橘分类的准确度,降低模型过拟合的概率,实现对8类柑橘的准确识别分类,最终分类准确率达到99.9%,对柑橘分类具有指导意义。     

神经网络技术在节水灌溉专家系统中的应用

针对传统专家系统开发和应用过程中存在的问题,利用神经网络的学习功能、记忆功能和并行处理的优势,提出了用神经网络建立专家系统的方法,构造了神经网络专家系统的基本框架,并介绍了神经网络专家系统在节水灌溉上的应用。     

Predicting pragmatic reasoning in language games

One of the most astonishing features of human language is its capacity to convey information efficiently in context. Many theories provide informal accounts of communicative inference, yet there have been few successes in making precise, quantitative predictions about pragmatic reasoning. We examined judgments about simple referential communication games, modeling behavior in these games by assuming that speakers attempt to be informative and that listeners use Bayesian inference to recover speakers' intended referents. Our model provides a close, parameter-free fit to human judgments, suggesting that the use of information-theoretic tools to predict pragmatic reasoning may lead to more effective formal models of communication.     

一种模糊神经网络自校正控制器设计与应用

本文提出了一种模糊神经网络智能控制方法，并介绍了采用多层神经网络表达模糊控制和知识规则，模糊推理和学习算法，实验仿真结果表明，这种控制方案可改善具有时变及大纯滞后系统的控制品质，其性质优于一般模糊控制。     

Methods of predicting milk yield in dairy cows—Predictive capabilities of Wood's lactation curve and artificial neural networks (ANNs)

The study is focused on the capability of artificial neural networks to forecast milk yield for both full and standardised lactations. We used a dataset of 108,931 daily milk yields (dataset A) collected from three lactations of dairy cows managed in a production farm. Using the actual data on daily milk yields and the data recorded on official milk recording test days, a number of neural networks were designed and parameters of Wood's model were estimated. The quality of each network and regression model was measured using coefficients of determination, relative approximation errors (RAE), and root mean square errors (RMS). In order to test the prognostic parameters of the models, we randomly selected a subset of cows from the studied population, which produced in a dataset of 28,576 daily yields (dataset B). For those cows, daily and lactation yield forecasts were generated, which were next compared with their actual (observed) yield records and with the yields calculated by SYMLEK (ZETO Olsztyn Sp. z o.o., www.zeto.olsztyn.pl). The results have shown that the quality parameters of the designed neural networks were better than those of the regression model, for both the daily yields and test-day data (higher coefficients of determination and lower RAE and RMS). The prognostic parameters estimated for the forecasts of the neural networks were characterised by lower errors of prediction for both the daily yields and test-day data and exhibited higher coefficients of correlation between the predicted and the actual data (or the yields produced by SYMLEK). The predictions by the neural networks were more accurate than those by Wood's models. Furthermore, the predictions by both analysed models were closer to reality than the values estimated with the SYMLEK system. Application of neural networks does not require the data meeting the assumptions that must otherwise be met in a regression model. Large datasets are not needed to design a quite reliable neural network and, what is more, it is much easier to work with such a model than with a regression model.     

一种模糊计算过程神经元网络及其应用

针对带有过程性模糊规则的时变信息处理问题,提出了一种模糊计算过程神经元网络。该模型将模糊神经网络推广到时域空间,可实现对过程性定量与定性混合信息的模糊计算。给出了一种5层结构的模糊计算过程神经元网络模型,并针对网络结构的优化问题给出了该网络模型的规则层节点的选取方法和相应的学习规则,对于具有较少输入节点的情况,网络有较快的训练速度。模糊过程神经元网络将传统模糊神经网络的模糊函数映像关系推广为模糊泛函映像,增强了对各种过程信息的综合处理能力。实际应用结果验证了模型和算法的有效性。     

机器学习算法在森林生长收获预估中的应用

森林生长收获预估是森林经理学的一个重要方向,采用模型技术进行森林生长收获估计是森林经营决策的重要前提。传统的统计模型如线性及非线性回归模型、混合效应模型、分位数回归、度量误差模型等统计方法已被广泛应用于研究林木生长,但这些统计方法在应用时常常需满足一定的统计假设前提,诸如数据独立、正态分布和等方差等。由于森林生长数据的连续观测和层次性,上述假设通常难以满足。近年来随着人工智能技术的发展,机器学习算法为森林生长收获预估提供了一种新的手段,它具有对输入数据的分布形式没有假设前提、能够揭示数据中的隐含结构、预测结果好等优点,但在森林生长收获预估中的应用仍十分有限。文章对分类和回归树、多元自适应样条、bagging回归、增强回归树、随机森林、人工神经网络、支持向量机、K最近邻等方法在森林生长收获预估中的应用、软件及调参等进行了综述,讨论了机器学习方法的优势和挑战,认为机器学习方法在森林生长收获预估方面有很大的潜力,必将得到广泛应用,并和传统统计模型相结合成为生长收获模型发展的一种趋势。      

深度学习在植物基因组学与作物育种中的应用现状与展望

[目的 /意义]随着单细胞测序、高通量技术的突破,植物基因组学也取得了巨大进步,可以低成本获取多维全基因组分子表型的海量数据。深度学习技术可以作为强大的数据挖掘工具对获取的分子表型进行进一步预测和解释。当前研究表明,深度学习在植物基因组学与作物育种研究任务中取得显著效果。但目前尚缺乏对于深度学习在植物基因组学中应用的完整综述。[方法 /过程]本文首先概述了深度学习方法背景,包括最新的图神经网络;随后着重从基因特性、蛋白质特性方面综述了基因组学和深度学习交叉领域的两个突出问题：1）如何对从植物基因组DNA序列到分子表型的信息流进行建模？2）如何使用深度学习模型识别自然种群中的功能变异？[结果 /结论]本文总结了当前研究中如何应用传统深度学习算法、图深度学习、生成对抗网络以及可解释性AI等方法解决上述两个问题。最后分析了深度学习在未来植物基因组学研究和作物遗传改良中的发展前景。     

基于深度卷积神经网络的柑橘黄龙病症状识别

【目的】探究深度学习在柑橘Citrus spp.黄龙病症状识别上的可行性,并评估识别器的识别准确率。【方法】以黄龙病/非黄龙病引起的发病叶片图像及健康叶片图像为训练素材,基于卷积神经网络及迁移学习技术构建二类识别器(I-2-C和M-2-C)和八类识别器(I-8-C和M-8-C)。【结果】M-8-C模型的整体识别表现最优,对所有图像的识别准确率为93.7%,表明构建的神经网络识别器能有效辨别柑橘黄龙病症状;I-8-C和M-8-C对所有类型图像的平均F1分值分别为77.9%和88.4%,高于I-2-C(56.3%)和M-2-C(52.5%),表明症状细分有利于提高模型的识别能力。同时M-8-C比I-8-C略高的平均F1分值表明基于MobileNetV1结构的八类识别器识别表现略优于基于InceptionV3的八类识别器。基于M-8-C改进的识别器M-8f-C能够转移到智能手机上,在田间测试中取得较好的识别表现。【结论】基于深度学习和迁移学习开发的识别器对黄龙病单叶症状具有较好的识别效果。     

人工神经网络的研究

This paper summarizes the history and current development of artificial neural networks (ANN), and briefly introduces the ANN study on modelling building and learning mechanism. It emphatically probes into the realization of neural networks and neural com     

BP和RBF神经网络短时交通流预测结果比较

对BP神经网络和RBF神经网络这2种模型的特征进行了分析,并将其应用于某高速公路的短时流量预测,比较了2种神经网络模型的预测结果。从量化的角度进一步证实了在交通流预测领域RBF神经网络比BP神经网络更快捷、更准确,从而更适合应用于对实时性和准确性要求比较高的交通系统。     

基于神经网络系统的附着系数计算模型

对几种附着系数计算模型进行了深入研究,在全面分析了主要影响附着系数因素的基础上,采用神经网络优化算法,分别建立了以路面状况、胎压及车速为输入,以附着系数为输出的3种轮胎花纹的神经网络附着系数计算模型,并验证了模型的有效性。该模型能够计算汽车在不同的行驶工况下的轮胎/路面间的附着系数,从而为附着系数实时监控提供理论依据,为行车安全提供保障。