伤害回避的脑机制及其生物基础

伤害回避是指个体对厌恶刺激信号做出强烈的反应,并学会被动地回避惩罚的一种倾向,这一倾向使得个体反复思考未来的结局,并谨慎小心地对待不确定情景中的事件,进而更有可能诱发情感障碍。伤害回避涉及的神经网络包括三个子网络,即额顶叶-前扣带皮层的连接、皮层-杏仁核的连接和白质通道的结构性连接,这三个子网络分别与羞怯感-易疲劳性、预期担心以及不确定环境中的害怕情绪有关。而其生物基础则包括单一基因多态性和基因多态间的交互作用。今后的研究应该集中在深化伤害回避神经网络与生物基础间的联合机制、研究三种及以上基因多态间的交互作用、考察其他因素对基因效应的调节作用、注重伤害回避四种亚型相关神经网络之间的连接、探讨5-HT4等其他几种5-羟色胺受体多态性与伤害回避的关系以及分析伤害回避内部机制在抗抑郁治疗中的作用等方面。     

Artificial intelligence for the measurement of vocal stereotypy

Both researchers and practitioners often rely on direct observation to measure and monitor behavior. When these behaviors are too complex or numerous to be measured in vivo, relying on direct observation using human observers increases the amount of resources required to conduct research and to monitor the effects of interventions in practice. To address this issue, we conducted a proof of concept examining whether artificial intelligence could measure vocal stereotypy in individuals with autism. More specifically, we used an artificial neural network with over 1,500 minutes of audio data from 8 different individuals to train and test models to measure vocal stereotypy. Our results showed that the artificial neural network performed adequately (i.e., session-by-session correlation near or above .80 with a human observer) in measuring engagement in vocal stereotypy for 6 of 8 participants. Additional research is needed to further improve the generalizability of the approach.     

言语想象的神经机制

言语想象不仅在大脑预处理机制方面起到重要的作用,还是目前脑机接口领域研究的热点。与正常言语产生过程相比,言语想象的理论模型、激活脑区、神经传导路径等均与其有较多相似之处。而言语障碍群体的言语想象、想象有意义的词语和句子时的脑神经机制与正常言语产生存在差异。鉴于人类言语系统的复杂性,言语想象的神经机制研究还面临一系列挑战,未来研究可在言语想象质量评价工具及神经解码范式、脑控制回路、激活通路、言语障碍群体的言语想象机制、词语和句子想象的脑神经信号等方面进一步探索,为有效提高脑机接口的识别率提供依据,为言语障碍群体的沟通提供便利。     

增塑剂DEHP的神经和行为发育毒性

增塑剂邻苯二甲酸二乙基己酯(Di-(2-ethylhexyl) phthalate,DEHP)是一种具有拟雌激素和抗雄激素活性的环境内分泌干扰物,人类和野生动物可通过不同途径终生暴露于DEHP.母体摄入的DEHP可通过胎盘和乳汁转入子代体内并进入脑组织.围生期DEHP暴露性别特异性地改变子代大鼠下丘脑视前区芳香酶活性而影响雌激素合成,并通过改变雌激素受体活性干扰雌激素对脑发育的调节作用.DEHP及其代谢产物可以影响神经细胞的增殖分化和突触形成,干扰性激素调控发育过程中的下丘脑氨基酸递质系统对促性腺激素释放激素分泌的刺激作用,影响中脑多巴胺递质系统发育而诱导自发性多动症.DEHP对脑发育的作用最终影响动物的早期行为、学习记忆和情感等行为发育.     

神经元活动的时相同步与脑功能整合

从大脑整合的角度分析心理的神经机制。神经元之间的交互和动态联结而构成神经集合被认为是每一个认知活动的基础。然而这一交互作用的具体性质,即脑整合的机制尚未明确。通过对有关实验结果的分析,认为神经元活动的时相同步可能是脑整合的机制。     

On the current status of the evolutionary theory of behavior dynamics

The evolutionary theory of behavior dynamics is a complexity theory that instantiates the Darwinian principles of selection, reproduction, and mutation in a genetic algorithm. The algorithm is used to animate artificial organisms that behave continuously in time and can be placed in any experimental environment. The present paper is an update on the status of the theory. It includes a summary of the evidence supporting the theory, a list of the theory's untested predictions, and a discussion of how the algorithmic operations of the theory may correspond to material reality. Based on the evidence reviewed here, the evolutionary theory appears to be a strong candidate for a comprehensive theory of adaptive behavior.     

Rolling element bearing fault diagnosis using convolutional neural network and vibration image

Detecting in prior bearing faults is an essential task of machine health monitoring because bearings are the vital components of rotary machines. The performance of traditional intelligent fault diagnosis methods depend on feature extraction of fault signals, which requires signal processing techniques, expert knowledge, and human labor. Recently, deep learning algorithms have been applied widely in machine health monitoring. With the capacity of automatically learning complex features of input data, deep learning architectures have great potential to overcome drawbacks of traditional intelligent fault diagnosis. This paper proposes a method for diagnosing bearing faults based on a deep structure of convolutional neural network. Using vibration signals directly as input data, the proposed method is an automatic fault diagnosis system which does not require any feature extraction techniques and achieves very high accuracy and robustness under noisy environments.     

Law,discipline and governance based on cognitive process simulation – analysis of legal forms in the modern rights relationship

People are becoming more and more humanized in the process of understanding the law. According to the right to discipline, the law has its own core setting factors, while some limits can't reach people's desire. Therefore, the legal and illegal mode of transcending rights is very important. In order to analyze the legal form of modern rights, in this paper, the cognitive learning and memory process of human brain were simulated through the artificial neural network and the understanding of human brain structure, and the role of law, discipline and governance was reflected. In the study, the structure and algorithm of the model neural network were optimized, the memory forgetting curve mechanism that can simulate the human brain was introduced, and thus the network recognition rate was improved. And in the algorithm, the calculation of matching degree was avoided, and the computational complexity was reduced to the sample. Then the sample was compared with the SOM, ART1, and PNN algorithms. The experimental simulation results show that the recognition speed of this sample is 1.9 times faster than that of ART1, 58 times than that of SOM, and 1.5 times than that of the PNN network.