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Hybrid neural state machine for neural network

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Science China(Information Sciences) 2021年第3期64卷 136-148页

作者： Lei TIAN Zhenzhi WU Shuang WU Luping SHI department of Precision Instrument Center for Brain-Inspired Computing Research(CBICR)Beijing Innovation Center for Future Chip Tsinghua University

The integration of computer-science-oriented and neuroscience-oriented approaches is believed to be a promising way for the development of artificial general intelligence(AGI). Recently, a hybrid Tianjic chip that integrates both approaches has been reported, providing a general platform to facilitate the research of AGI. The control algorithm for handling various neural networks is the key to this platform; however, it is still primitive. In this work, we propose a hybrid neural state machine(H-NSM) framework that can efficiently cooperate with artificial neural networks and spiking neural networks and control the workflows to accomplish complex tasks. The H-NSM receives input from different types of networks, makes decisions according to the fusing of various information, and sends control signals to the sub-network or actuator. The H-NSM can be trained to adapt to context-aware tasks or sequential tasks, thereby improving system robustness. The training algorithm works correctly even if only 50% of the forced state information is provided. It achieved performance comparable to the optimum algorithm on the Tower of Hanoi task and achieved multiple tasks control on a self-driving bicycle. After only 50 training epochs, the transfer accuracy reaches 100% in the test case. It proves that H-NSM has the potential to advance control logic for hybrid systems, paving the way for designing complex intelligent systems and facilitating the research towards AGI.

关键词： hybrid neural state machine ANNs SNNs supervised learning context-aware task sequential task

Associative learning of a three-terminal memristor network for digits recognition

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Science China(Information Sciences) 2023年第2期66卷 209-218页

作者： Yiming REN Bobo TIAN Mengge YAN Guangdi FENG Bin GAO Fangyu YUE Hui PENG Xiaodong TANG Qiuxiang ZHU Junhao CHU Chungang DUAN Key laboratory of Polar Materials and Devices (MOE) Ministry of Education Department of ElectronicsEast China Normal University Zhejiang Lab Institute of Microelectronics Beijing Innovation Center for Future Chips (ICFC) Tsinghua University Collaborative Innovation Center of Extreme Optics Shanxi University

Imitating the associative intelligence of the biological brain is attractive but is poorly achieved in hardware because the complex tunable connection in neural networks is difficult to reproduce. We develop a circuit composed of a three-terminal memristor network to reproduce the biological conditioning process artificially. The synaptic weight between co-firing neurons is strengthened simultaneously by generating a feedback signal from the integrate-and-fire neuron to the gate of the synaptic memristor. The network allows the multi-associative capacity of recalling more than one digit in one circuit. Both single and multi-associative learning for recalling digital images are achieved. Furthermore, all 10 digital images from “0” to “9” are successfully recalled in an associative network with such paralleling circuits. Assisted by this associative layer, a typical classification network effectively improves the recognition rate for fragmentary digital *** work sheds light on brain-inspired artificial associative memory and provides a strategy for applications,such as object recognition with partial features and similar scenes.

关键词： artificial intelligence associative learning memristor classification network

Protocol for the development of clinical guidelines for the management of chronic primary pain

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General Psychiatry 2023年第1期36卷 70-75页

作者： Hong Gan Shuo Li Yanli Luo Chunbo Li Jiang Du Min Zhao Shanghai Mental Health Center Shanghai Jiao Tong University School of MedicineShanghaiChina department of Psychological Medicine Shanghai Jiao Tong University School of Medicine Affiliated Renji HospitalShanghaiChina Shanghai Key laboratory of Psychotic Disorders ShanghaiChina CAS Center for Excellence in brain Science and intelligence Technology(CEBSIT) Chinese Academy of ScienceShanghaiChina Institute of psychology and Behavioral Science Shanghai Jiao Tong UniversityShanghaiChina brain Science and Technology Research Center Shanghai Jiao Tong UniversityShanghaiChina

Introduction Pain is an unpleasant sensory and emotional experience associated with actual or potential tissue *** 2018,the World Health Organization(WHO)revised the International Classification of Diseases,11th Revision(ICD-11)to include chronic pain as an independent disease for the first time(MG30.0).1 At the same time,the ICD-11 also developed new classifications for chronic pain,including chronic primary pain(CPP),chronic cancer-related pain,chronic post-surgical or post-traumatic pain,chronic secondary musculoskeletal pain,chronic secondary visceral pain.

关键词： pain chronic clinical

TNF-α,IL-6 and hs CRP in patients with melancholic,atypical and anxious depression:an antibodyyarrayanalysis related to somatic symptoms

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General Psychiatry 2022年第5期35卷 354-365页

作者： Hongmei Liu Xiaohui Wu Yun Wang Xiaohua Liu Daihui Peng Yan Wu Jun Chen Yun-Ai Su Jia Xu Xiancang Ma Yi Li Jianfei Shi Xiaodong Yang Han Rong Marta Di Forti Yiru Fang Clinical Research Center Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina Shanghai Key laboratory of Psychotic Disorders ShanghaiChina Peking University Sixth Hospital BeijingChina Harbin First Specific Hospital HarbinChina department of Psychiatry The First Affiliated Hospital of Xi'an Jiaotong UniversityXianChina Wuhan Mental Health Center WuhanChina Hangzhou Seventh People's Hospital HangzhouChina Shandong Mental Health Center JinanChina Shenzhen Mental Health Center ShenzhenChina department of Social Genetics and Developmental Psychiatry Institute of Psychiatry psychology and Neuroscience King's College LondonLondonUK Guangdong-Hong Kong-Macao Greater Bay Area Center for brain Science and brain-Inspired intelligence GuangzhouChina State Key laboratory of Neuroscience Shangha nstitute for Biological SciencesCASShanghaiChina

Background The association between inflammation and major depressive disorder(MDD)remains poorly understood,given the heterogeneity of patients with *** We investigated inflammatory markers,such as interleukin(IL)-6,high-sensitivity C reactive protein(hsCRP)and tumour necrosis factor-α.(TNF-α)in melancholic,atypical and anxious depression and explored whether baseline inflammatory protein levels could indicate *** The sample consisted of participants(aged 18-55 years)from a previously reported multicentre randomised controlled trial with a parallel-group design registered with ***,including melancholic(n=44),atypical(n=37)and anxious(n=44)patients with depression and healthy controls(HCs)(n=33).Subtypes of MDD were classified according to the 30-item Inventory of Depressive Symptomatology,Self-Rated Version and the.17-item Hamilton Depression Rating *** *** TNF-α,IL-6 and hsCRP were assessed using antibody array *** Patients with MDD,classified according to melancholic,atypical and anxious depression subtypes,and HCs did not differ significantly in baseline TNF-α,IL-6 and hsCRP levels after *** patients with anxious depression,hsCRP levels increased significantly if they experienced no pain(adjusted(adj.)p=0.010)or mild to moderate pain(adj.p=0.038)compared with those with severe ***,the patients with anxious depression and severe pain showed a lower trend in hsCRP levels than patients with atypical depression who experienced severe pain(p=0.022;adj.p=0.155).Baseline TNF-α(adj.p=0.038)and IL-6(adj.p=0.006)levels in patients in remission were significantly lower than those in patients with no remission among the participants with the atypical depression subtype at the eighth-week *** This study provides evidence of differences in inflammatory proteins in patients with varied symptoms among melancholic,atypical and anxious depression *** studies on the immuno

关键词： patients atypical Blood

Unsupervised content-preserving transformation for optical microscopy

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Light(Science & Applications) 2021年第3期10卷 390-400页

作者： Xinyang Li Guoxun Zhang Hui Qiao Feng Bao Yue Deng Jiamin Wu Yangfan He Jingping Yun Xing Lin Hao Xie Haoqian Wang Qionghai Dai department of Automation Tsinghua UniversityBeijing 100084China tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China Institute for brain and Cognitive Sciences Tsinghua UniversityBeijing 100084China School of Astronautics Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and brain Computing Beihang UniversityBeijing 100191China department of Pathology Sun Yat-sen University Cancer CenterGuangzhou 510060China State Key laboratory of Oncology in South China Guangzhou 510060China Collaborative Innovation Center for Cancer Medicine Guangzhou 510060China Beijing Innovation Center for Future Chips Tsinghua UniversityBeijing 100084China

The development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation,which is gradually changing the landscape of optical imaging and biomedical ***,current implementations of deep learning usually operate in a supervised manner,and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general ***,we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy,even in some cases in which supervised models cannot be *** the introduction of a saliency constraint,the unsupervised model,named Unsupervised content-preserving Transformation for Optical Microscopy(UTOM);can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image *** shows promising performance in a wide range of biomedical image transformation tasks,including in silico histological staining,fluorescence image restoration,and virtual fluorescence *** evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and *** anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

关键词： transformation preserving image

Neural plasticity in high-level visual cortex underlying object perceptual learning

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Frontiers in Biology 2013年第4期8卷 434-443页

作者： Taiyong BI Fang FANG department of psychology and Key laboratory of Machine Perception (Ministry of Education) Peking University Beijing 100871 China Peking-tsinghua Center for Life Sciences Peking University Beijing 100871 China PKU-IDG/MeGovern Institute for brain Research Peking University Beijing 100871 China

With intensive training, human can achieve impressive behavioral improvement on various perceptual tasks. This phenomenon, termed perceptual learning, has long been considered as a hallmark of the plasticity of sensory neural system. Not surprisingly, high-level vision, such as object perception, can also be improved by perceptual learning. Here we review recent psychophysical, electrophysiological, and neuroimaging studies investigating the effects of training on object selective cortex, such as monkey inferior temporal cortex and human lateral occipital area. Evidences show that learning leads to an increase in object selectivity at the single neuron level and/or the neuronal population level. These findings indicate that high-level visual cortex in humans is highly plastic and visual experience can strongly shape neural functions of these areas. At the end of the review, we discuss several important future directions in this area.

关键词： plasticity object perceptual learning neural mechanism inferior temporal cortex lateral occipital

Applicability Evaluation of Simplified Cognitive Behavioral Therapy

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上海精神医学 2018年第2期30卷 102-109页

作者：张丽朱智佩方芳申远柳娜李春波 department of Clinical psychology Shanghai Hongkou Mental Health Center Shanghai Key laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine department of Public Mental Health Shanghai Putuo Mental Health Center department of Psychiatry Tenth People's Hospital of Tongji University department of Medical psychology Nanjing Brain Hospital affiliated to Nanjing Medical University brain Science and Technology Research Center Shanghai Jiao Tong University Center for Excellence in brain Science and intelligence Technology (CEBSIT) Chinese Academy of Science

背景:我们在国内较早开展并研发了针对焦虑症的结构化认知行为治疗操作手册,本研究在前期研究基础上对简化认知行为治疗的适宜性进行评价。目的:通过多中心临床对照实验,评价简化认知行为治疗(Simplified Cognitive Behavioral Therapy,... 详细信息

背景:我们在国内较早开展并研发了针对焦虑症的结构化认知行为治疗操作手册,本研究在前期研究基础上对简化认知行为治疗的适宜性进行评价。目的:通过多中心临床对照实验,评价简化认知行为治疗(Simplified Cognitive Behavioral Therapy,SCBT)的适宜性。方法:通过来自不同级别的精神卫生专科医疗机构和综合医院精神科等多个中心,对广泛性焦虑患者进行SCBT的临床对照研究,分为3组:SCBT组,SCBT+药物组和药物组。比较三组脱落率,接受SCBT患者对治疗满意度以及治疗师对SCBT评价。结果:(1)三组脱落率差异没有统计学意义。(2)接受SCBT的两组患者仅在治疗时间和次数上差异有统计学意义,SCBT组比SCBT+药物组患者治疗满意度高。(3)18名SCBT实施人员均认为操作手册内容容易理解、易于操作,并到达治疗目标。结论:SCBT对广泛性焦虑患者适宜性较强,有望成为在不同级别医疗机构应用推广的心理治疗方法。

关键词：心理治疗认知行为适用性评估中心控制持续时间心理健康综合医院

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Functional magnetic resonance imaging study of group independent components underpinning item responses to paranoid-depressive scale

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World Journal of Clinical Cases 2023年第36期11卷 8458-8474页

作者： Drozdstoy Stoyanov Rositsa Paunova Julian Dichev Sevdalina Kandilarova Vladimir Khorev Semen Kurkin department of Psychiatry Medical University PlovdivPlovdiv 4000Bulgaria Research Institute Medical UniversityPlovdiv 4002Bulgaria Faculty of Medicine Medical UniversityPlovdiv 4002Bulgaria department of Psychiatry and Medical psychology Medical UniversityPlovdiv 4002Bulgaria Baltic Center for Artificial intelligence and Neurotechnology Immanuel Kant Baltic Federal UniversityKaliningrad 236041Russia

BACKGROUND Our study expand upon a large body of evidence in the field of neuropsychiatric imaging with cognitive,affective and behavioral tasks,adapted for the functional magnetic resonance imaging(MRI)(fMRI)experimental *** is sufficient evidence that common networks underpin activations in task-based fMRI across different mental *** To investigate whether there exist specific neural circuits which underpin differ-ential item responses to depressive,paranoid and neutral items(DN)in patients respectively with schizophrenia(SCZ)and major depressive disorder(MDD).METHODS 60 patients were recruited with SCZ and *** patients have been scanned on 3T magnetic resonance tomography platform with functional MRI paradigm,comprised of block design,including blocks with items from diagnostic paranoid(DP),depression specific(DS)and DN from general interest *** performed a two-sample t-test between the two groups-SCZ patients and depressive *** purpose was to observe different brain networks which were activated during a specific condition of the task,respectively DS,DP,*** Several significant results are demonstrated in the comparison between SCZ and depressive groups while performing this *** identified one component that is task-related and independent of condition(shared between all three conditions),composed by regions within the temporal(right superior and middle temporal gyri),frontal(left middle and inferior frontal gyri)and limbic/salience system(right anterior insula).Another com-ponent is related to both diagnostic specific conditions(DS and DP)*** is shared between DEP and SCZ,and includes frontal motor/language and parietal *** specific component is modulated preferentially by to the DP condition,and is related mainly to prefrontal regions,whereas other two components are significantly modulated with the DS condition and include clusters within the default mode network such as posterior cingulate and prec

关键词： Paranoid-depressive scale Functional magnetic resonance imaging Cross-validation Group independent component analysis Schizophrenia Depression

Computational neuroscience:a frontier of the 21st century

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National Science Review 2020年第9期7卷 1418-1422页

作者： Xiao-Jing Wang Hailan Hu Chengcheng Huang Henry Kennedy Chengyu Tony Li Nikos Logothetis Zhong-Lin Lu Qingming Luo Mu-ming Poo Doris Tsao Si Wu Zhaohui Wu Xu Zhang Douglas Zhou Center for Neural Science New York University Center for Neuroscience Key Laboratory of Medical Neurobiology of the Ministry of Health of China School of Medicine Zhejiang University department of Neuroscience and department of Mathematics Center for the Neural Basis of Cognition University of Pittsburgh Université Claude Bernard Lyon 1 Inserm Stem Cell and Brain Research Institute U1208 Institute of Neuroscience State Key Laboratory of Neuroscience Chinese Academy of SciencesCAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Center for brain Science and brain-Inspired Technology Division of Arts and Sciences and NYU-ECNU Institute of Cognitive Neuroscience NYU Shanghai School of Biomedical Engineering Hainan University Huazhong University of Science and Technology-Suzhou Institute for brainsmatics Division of Biology and Biological Engineering California Institute of Technology Howard Hughes Medical Institute School of Electronics Engineering and Computer Science IDG/Mc Govern Institute for Brain Research PKU-Tsinghua Center for Life SciencesPeking University College of Computer Science and Technology Zhejiang University Institute of brain-intelligence Science and Technology Zhangjiang Lab School of Mathematical Sciences MOE-LSCand Institute of Natural Sciences Shanghai Jiao Tong University

THEORY IN NEUROSCIENCE The human brain is a biological organ,weighing about three pounds or 1.4 kg,that determines our behaviors, thoughts,emotions and consciousness. Although comprising only 2%of the total body weigh... 详细信息

关键词： NETWORKS