融合视角下创造力的认知神经机制

创造力的认知神经机制是近年来心理学研究领域的前沿和热点问题。通过融合创造力整体宏观视角和创造性产生过程的微观视角,对创造力的认知神经机制进行了综述。宏观视角下,创造力主要涉及α波和大脑前额叶、内外侧颞叶以及外侧顶叶; 微观视角下,在创造力产生过程中主要涉及α波序列位置效应以及默认网络和执行控制网络的功能耦合。未来研究方向应该结合多模态脑成像数据库,利用机器算法来探究创造力的本质; 关注青少年群体创造力的纵向发展趋势; 结合分子遗传学研究,探究与创造力有关的基因问题。     

Developmental changes in neural letter‐selectivity: A 1‐year follow‐up of beginning readers

The developmental course of neural tuning to visual letter strings is unclear. Here we tested 39 children longitudinally, at the beginning of grade 1 (6.45 ± 0.33 years old) and 1 year after, with fast periodic visual stimulation in electroencephalography to assess the evolution of selective neural responses to letter strings and their relationship with emerging reading abilities. At both grades, frequency‐tagged letter strings were discriminated from pseudofont strings (i.e. letter‐selectivity) over the left occipito‐temporal cortex, with effects observed at the individual level in 62% of children. However, visual words were not discriminated from pseudowords (lexical access) at either grade. Following 1 year of schooling, letter‐selective responses showed a specific increase in amplitude, a more complex pattern of harmonics, and were located more anteriorly over the left occipito‐temporal cortex. Remarkably, at both grades, neural responses were highly significant at the individual level and correlated with individual reading scores. The amplitude increase in letter‐selective responses between grades was not found for discrimination responses of familiar keyboard symbols from pseudosymbols, and was not related to a general increase in visual stimulation responses. These findings demonstrate a rapid onset of left hemispheric letter selectivity, with 1 year of reading instruction resulting in increased emerging reading abilities and a clear quantitative and qualitative evolution within left hemispheric neural circuits for reading.     

Using spotted hyena optimizer for training feedforward neural networks

Spotted hyena optimizer (SHO) is a novel metaheuristic optimization algorithm based on the behavior of spotted hyena and their collaborative behavior in nature. In this paper, we design a spotted hyena optimizer for training feedforward neural network (FNN), which is regarded as a challenging task since it is easy to fall into local optima. Our objective is to apply metaheuristic optimization algorithm to tackle this problem better than the mathematical and deterministic methods. In order to confirm that using SHO to train FNN is more effective, five classification datasets and three function-approximations are applied to benchmark the performance of the proposed method. The experimental results show that the proposed SHO algorithm for optimization FNN has the best comprehensive performance and has more outstanding performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.     

Earthquake disaster avoidance learning system using deep learning

The popularity of deep learning has influenced the field of surveillance and human safety. We adopt the advantages of deep learning techniques to recognize potentially harmful objects inside living rooms, offices, and dining rooms during earthquakes. In this study, we propose an educational system to teach earthquake risks using indoor object recognition based on deep learning algorithms. The system is based on the You Look Only Once (YOLO) deployed on our cloud-based server named Earthquake Situation Learning System (ESLS) for the detection of harmful objects associated with risk tags. ESLS is trained on our own indoor images dataset. The user interacts with the ESLS server through video or image files, and the object detection algorithm using YOLO recognizes the indoor objects with associated risk tags. Results show that the service time of ESLS is low enough to serve it to users in 0.8 s on average, including processing and communication times. Furthermore, the accuracy of the harmful object detection is 96% in the general indoor lighting situation. The results show that the proposed ESLS is applicable to real service for teaching the earthquake disaster avoidance.     

Risk assessment of knowledge fusion in an innovation ecosystem based on a GA-BP neural network

The risk assessment of knowledge fusion in innovation ecosystems is directly related to these ecosystems’ success or failure. A back-propagation (BP) neural network optimized by a genetic algorithm (GA) is thus proposed to evaluate the risk of knowledge fusion in innovation ecosystems. First, an index system is constructed for evaluating the risk of knowledge fusion in innovation ecosystems, and data are collected by questionnaire for use as training data for the neural networks. To realize machine learning, 84 datasets were generated, of which 60 were used to train the network, and 24 were used to test the network in MATLAB (R2014b). Evaluation models were then constructed by the BP neural network and GA-BP neural network, and their accuracy was judged by comparing the evaluation value with the target value. The comparison shows that the GA-BP neural network has faster convergence speed and higher stability, can achieve the goal more often, and reduces the possibility of the BP neural network falling into a local optimum instead of reaching global optimization. The GA-BP neural network model for the knowledge fusion risk assessment of innovation ecosystems provides a new method for practice.     

Spiking neural networks and hippocampal function: A web-accessible survey of simulations,modeling methods,and underlying theories

Computational modeling has contributed to hippocampal research in a wide variety of ways and through a large diversity of approaches, reflecting the many advanced cognitive roles of this brain region. The intensively studied neuron type circuitry of the hippocampus is a particularly conducive substrate for spiking neural models. Here we present an online knowledge base of spiking neural network simulations of hippocampal functions. First, we overview theories involving the hippocampal formation in subjects such as spatial representation, learning, and memory. Then we describe an original literature mining process to organize published reports in various key aspects, including: (i) subject area (e.g., navigation, pattern completion, epilepsy); (ii) level of modeling detail (Hodgkin-Huxley, integrate-and-fire, etc.); and (iii) theoretical framework (attractor dynamics, oscillatory interference, self-organizing maps, and others). Moreover, every peer-reviewed publication is also annotated to indicate the specific neuron types represented in the network simulation, establishing a direct link with the Hippocampome.org portal. The web interface of the knowledge base enables dynamic content browsing and advanced searches, and consistently presents evidence supporting every annotation. Moreover, users are given access to several types of statistical reports about the collection, a selection of which is summarized in this paper. This open access resource thus provides an interactive platform to survey spiking neural network models of hippocampal functions, compare available computational methods, and foster ideas for suitable new directions of research.     

外在奖赏对陈述性记忆的影响

学习和记忆是个体赖以生存和发展的前提,如何取得好的学习和记忆效果是心理学和神经科学关注的重点。近年来,许多研究揭示奖赏可以促进记忆效果,奖赏对记忆的影响逐渐成为心理学和神经科学的热点研究课题。大脑的中脑多巴胺奖赏系统与海马记忆系统在结构和功能上均有关联。奖赏通过编码和巩固阶段以不同机制对记忆效果产生作用：在记忆编码阶段,奖赏会激活奖赏系统、注意控制系统,将更多认知资源分配给奖赏相关信息,从而促进奖赏信息的记忆效果;在记忆巩固阶段,奖赏会促进多巴胺释放,作用于海马对奖赏相关信息的加工,从而促进奖赏信息的记忆效果。未来研究可以关注奖赏对行为影响的复杂模式和内在奖赏对学习记忆的影响等方面。     

催产素对产后抑郁症患者母性行为的调节作用

产后抑郁症是指女性分娩后一段时间内出现的严重抑郁发作现象。研究表明, 催产素不仅参与分娩过程和正常的母性行为表达, 也在调节产后抑郁的发病及患者的母性行为中起到了重要的作用。产后抑郁症患者的情绪和认知功能受损, 这可能导致了母性行为表达质量的下降; 催产素可以通过中脑边缘多巴胺系统来调控母性行为, 作用于内侧视前区(MPOA)来激活中脑腹侧被盖区(VTA)-伏隔核(NAc)环路从而影响伏隔核内多巴胺的分泌; 催产素对产后抑郁症中母性行为的调节机制可能是通过调节五羟色胺系统的功能来发挥抗焦虑作用, 也可能是通过与其它激素的交互作用来加速母性行为的表达。未来研究进一步明确催产素在中枢神经环路中的功能差异, 以及社会支持对产后抑郁症患者催产素的使用带来的影响。     

Critical factors in consumer supermarket shopping behaviour: a neural network approach

This paper studies UK supermarket shopping behaviour, by analysing the antecedent variables of three critical factors: overall levels of customer satisfaction, number of trips to the supermarket, and amount spent. A neural network approach predicts these factors using ten input variables and three hidden nodes. Results show that the most satisfied and high‐spending customers tend to be those who have the income to take full advantage of the choice and quality offered. Other customers are more concerned with prices being reasonable and discounts available, but the satisfaction of these shoppers is also linked with store atmosphere. Copyright © 2001 Henry Stewart Publications.     

精神分裂症的潜伏抑制动物模型

精神分裂症动物模型的建立,是当前研究精神分裂症的神经生物学机制以及开发新的抗精神病药物的一个重要课题。文章系统地阐述了以潜伏抑制为行为模式的精神分裂症动物模型的理论以及神经机制,并重点介绍了精神分裂症的潜伏抑制动物模型的建立方法,包括（1）调节某些与精神分裂症相关的中枢神经递质的传递活动;（2）对与精神分裂症有关的中脑-伏隔核神经环路进行结构损伤或药物性干预;（3）在发育早期给予免疫刺激或环境应激