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arXiv 2017年

作者： Xi, Xuanyang Luo, Yongkang Li, Fengfu Wang, Peng Qiao, Hong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing100190 China LSEC Institute of Applied Mathematics AMSS Chinese Academy of Sciences Beijing100190 China

Visual saliency detection aims at identifying the most visually distinctive parts in an image, and serves as a pre-processing step for a variety of computer vision and image processing tasks. To this end, the saliency detection procedure must be as fast and compact as possible and optimally processes input images in a real time manner. It is an essential application requirement for the saliency detection task. However, contemporary detection methods often utilize some complicated procedures to pursue feeble improvements on the detection precession, which always take hundreds of milliseconds and make them not easy to be applied practically. In this paper, we tackle this problem by proposing a fast and compact saliency score regression network which employs fully convolutional network, a special deep convolutional neural network, to estimate the saliency of objects in images. It is an extremely simplified end-to-end deep neural network without any pre-processings and post-processings. When given an image, the network can directly predict a dense full-resolution saliency map (image-to-image prediction). It works like a compact pipeline which effectively simplifies the detection procedure. Our method is evaluated on six public datasets, and experimental results show that it can achieve comparable or better precision performance than the state-of-the-art methods while get a significant improvement in detection speed (35 FPS, processing in real time). Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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Say NO to aging related cognitive impairment and metabolic disorder

Say NO to aging related cognitive impairment and metabolic d...

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第十五次中国暨国际生物物理大会

作者：张玉英吴凯源粟文婷张登峰王平乔新华姚勤袁增强姚永刚刘光慧张晨 Limin Liu 陈畅 National Laboratory of Biomacromolecules CAS Center for Excellence in BiomacromoleculesInstitute of BiophysicsChinese Academy of Sciences University of Chinese Academy of Sciences Beijing Institute for Brain Disorders Capital medical university Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences&Yunnan Province Kunming Institute of ZoologyChinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences School of Life Sciences Peking University Department of Microbiology and Immunology University of California

随着世界人口老龄化问题的加剧,脑老化以及神经退行性疾病带给人们的威胁越来越大。美国Marist民意调查机构在1247名成年人对于衰老的恐惧调查显示,对比容颜消逝以及多种退行性疾病,82%的民众认为衰老相关的认知功能损伤对他们来说是衰... 详细信息

随着世界人口老龄化问题的加剧,脑老化以及神经退行性疾病带给人们的威胁越来越大。美国Marist民意调查机构在1247名成年人对于衰老的恐惧调查显示,对比容颜消逝以及多种退行性疾病,82%的民众认为衰老相关的认知功能损伤对他们来说是衰老带来的最大恐惧。衰老相关的认知功能损伤确实严重降低了老年人的生活质量和自主生活能力,并且给家庭和社会带来了巨大的看护压力和经济负担,所以研究衰老相关的认知功能损伤机制并寻找应对策略有重大意义。

关键词： GSNOR 亚硝基化 Say NO to aging related cognitive impairment and metabolic disorder 海马脑片

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Transfer of convalescent serum to pregnant mice prevents Zika virus infection and microcephaly in offspring

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细胞研究（英文版） 2017年第1期27卷 158-160页

作者： Shuo Wang Shuai Hong Yong-Qiang Deng Qing Ye Ling-Zhai Zhao Fu-Chun Zhang Cheng-Feng Qin State Key Laboratory of Molecular Developmental Biology CAS Center for Excellence in Brain Science and Intelligence Technology Institute of Genetics and Developmental Biology Chinese Academy of Sciences Beijing 100101 China University of Chinese Academy of Sciences Beijing 100101 China Department of Virology State Key Laboratory of Pathogen and Biosecurity Beijing Institute of Microbiology and Epidemiology Beijing 100071 China Guangzhou Eighth People's Hospital Guangzhou Medical University Guangzhou China

Dear Editor,Zika virus (ZIKV) is spreading rapidly around the world in over 60 *** is a mounting concern over the association of ZIKV infection and devastating cases of fetal and newborn microcephaly cases [1].The connection between ZIKV infection and microcephaly was first proposed based on the presence of ZIKV in microcephalic fetal brain tissues [1].

关键词： @@

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Human brainnetome atlas and its potential applications in brain-inspired computing 1

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2nd International Workshop on brain-Inspired Computing, brainComp 2015

作者： Fan, Lingzhong Li, Hai Yu, Shan Jiang, Tianzi Brainnetome Center Institute of Automation Chinese Academy of Sciences Beijing100190 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Brain Science and Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China Key Laboratory for NeuroInformation of the Ministry of Education School of Life Science and Technology University of Electronic Science and Technology of China Chengdu625014 China The Queensland Brain Institute University of Queensland BrisbaneQLD4072 Australia

ISBN: (数字)9783319508627

ISBN: (纸本)9783319508610

brain atlases are considered to be the cornerstone of neuroscience, but most available brain atlases lack fine-grained parcellation results and do not provide information about functionally important connectivity. Recently, novel methodologies and computerized brain mapping techniques could be used to explore the structure, function, and spatio-temporal changes in the human brain. The human brainnetome Atlas is an in vivo map that includes fine-grained functional brain subregions and detailed anatomical and functional connection patterns for each area. These features should enable researchers to describe the large scale architecture of the human brain more accurately. Using the human brainnetome Atlas, researchers could simulate and model brain networks using informatics and simulation technologies to elucidate the basic organizing principles of the brain. Others could use this same atlas to design novel neuromorphic systems that are inspired by the architecture of the brain. Therefore, this cutting-edge human brainnetome Atlas paves the way for constructing an even more fine-grained atlas of the human brain and offers the potential for applications in brain-inspired computing. © Springer International Publishing AG 2016.

关键词： Diffusion tensor imaging

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Distributed functional connectivity impairment in schizophrenia: A multi-site study 2

Distributed functional connectivity impairment in schizophre...

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2nd IET International Conference on Biomedical Image and Signal Processing, ICBISP 2017

作者： Yang, Yong Cui, Yue Xu, Kaibing Liu, Bing Song, Ming Chen, Jun Wang, Huiling Chen, Yunchun Guo, Hua Li, Peng Lu, Lin Lv, Luxian Wan, Ping Wang, Huaning Yan, Hao Yan, Jun Zhang, Hongxing Zhang, Dai Jiang, Tianzi Brainnetome Center Institute of Automation Chinese Academy of Sciences Beijing100190 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Department of Radiology Renmin Hospital Wuhan University Wuhan430060 China Department of Psychiatry Renmin Hospital Wuhan University Wuhan430060 China Department of Psychiatry Xijing Hospital Fourth Military Medical University Xi'an710032 China Zhumadian Psychiatric Hospital Zhumadian463000 China Peking University Sixth Hospital Institute of Mental Health Beijing100191 China Key Laboratory of Mental Health Ministry of Health Peking University Beijing100191 China Department of Psychiatry Henan Mental Hospital Second Affiliated Hospital of Xinxiang Medical University Xinxiang453002 China Henan Key Lab of Biological Psychiatry Xinxiang Medical University Xinxiang453002 China Department of Psychology Xinxiang Medical University Xinxiang453002 China Center for Life Sciences PKU-IDG McGovern Institute for Brain Research Peking University Beijing100871 China Queensland Brain Institute University of Queensland BrisbaneQLD4072 Australia CAS Center for Excellence in Brain Science and Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781785613180

Schizophrenia has been considered as a dysconneciton syndrome, which means the disintegration, or over interaction between brain regions may underlie the pathophysiology of this disease. Noninvasive techniques like functional magnetic resonance imaging (fMRI) were utilized to test this hypothesis. However, there is no consensus on which brain areas and which functional network is related with it, mostly due to the small sample size of previous studies. Supervised machine learning techniques are able to examine fMRI connectivity data in a multivariate manner and extract features predictive of group membership. This technique requires large sample sizes and results from small sample study may not generalize well. By applying a multi-task classification framework to large size multi-site schizophrenia resting functional MRI (rsMRI) dataset, we were able to find consistent and robust features. We observed that schizophrenia patients had widespread deficits in the brain. The most informative and robustly selected functional connectivity (FC) features were between and within functional networks such as the default mode network (DMN), the fronto-parietal control network (FPN), the subcortical network, and the cingulo-opercular task control network (CON). Our finding validated the dysconnection hypothesis of schizophrenia and shed light on the details of the impaired functional connectivity.

关键词： Magnetic resonance imaging

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Tensor power iteration for multi-graph matching

Tensor power iteration for multi-graph matching

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2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016

作者： Shi, Xinchu Ling, Haibin Hu, Weiming Xing, Junliang Zhang, Yanning CAS Center for Excellence in Brain Science and Intelligence Technology National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China Department of Computer and Information Sciences Temple University Philadelphia United States School of Computer Science Northwestern Polytechnical University Xian China

ISBN: (纸本)9781467388511

Due to its wide range of applications, matching between two graphs has been extensively studied and remains an active topic. By contrast, it is still under-exploited on how to jointly match multiple graphs, partly due to its intrinsic combinatorial intractability. In this work, we address this challenging problem in a principled way under the rank-1 tensor approximation framework. In particular, we formulate multi-graph matching as a combinational optimization problem with two main ingredients: unary matching over graph vertices and structure matching over graph edges, both of which across multiple graphs. Then we propose an efficient power iteration solution for the resulting NP-hard optimization problem. The proposed algorithm has several advantages: 1) the intrinsic matching consistency across multiple graphs based on the high-order tensor optimization; 2) the free employment of powerful high-order node affinity; 3) the flexible integration between various types of node affinities and edge/hyper-edge affinities. Experiments on diverse and challenging datasets validate the effectiveness of the proposed approach in comparison with state-ofthe-arts.

关键词： Graph theory

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Discriminatively boosted image clustering with fully convolutional auto-encoders

arXiv

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arXiv 2017年

作者： Li, Fengfu Qiao, Hong Zhang, Bo Xi, Xuanyang Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Academy of Sciences Beijing100049 China CAS Centre for Excellence in Brain Science and Intelligence Technology Shanghai200031 China

Traditional image clustering methods take a two-step approach, feature learning and clustering, sequentially. However, recent research results demonstrated that combining the separated phases in a unified framework and training them jointly can achieve a better performance. In this paper, we first introduce fully convolutional auto-encoders for image feature learning and then propose a unified clustering framework to learn image representations and cluster centers jointly based on a fully convolutional auto-encoder and soft k-means scores. At initial stages of the learning procedure, the representations extracted from the auto-encoder may not be very discriminative for latter clustering. We address this issue by adopting a boosted discriminative distribution, where high score assignments are highlighted and low score ones are de-emphasized. With the gradually boosted discrimination, clustering assignment scores are discriminated and cluster purities are enlarged. Experiments on several vision benchmark datasets show that our methods can achieve a state-of-the-art performance. Copyright © 2017, The Authors. All rights reserved.

关键词： Machine learning

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Anterograde Monosynaptic Transneuronal Tracers Derived from Herpes Simplex Virus 1 strain H129

Anterograde Monosynaptic Transneuronal Tracers Derived from ...

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中国神经科学学会第十二届全国学术会议

作者： Wen-Bo Zeng Hai-Fei Jiang Yousheng Shu Shao-Qun Zeng Fuqiang Xu Fei Zhao Min-Hua Luo State Key Laboratory of Virology CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) Wuhan Institute of Virology Chinese Academy of Sciences State Key Laboratory of Membrane Biology School of Life Sciences PKU-IDG/Mc Govern Institute for Brain Research Peking University Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Brain Research Center Wuhan Institute of Physics and Mathematics Chinese Academy of Sciences

Objective Herpes simplex virus type 1 strain 129(H129) has represented a promising anterograde neuronal circuit tracing tool, which complements the existing retrograde tracers. However, the current H129 derived tracers are multisynaptic, neither bright enough to label the details of neurons nor capable of determining direct projection targets as monosynaptic tracer. The present study is to introduce an improved multisynaptic anterograde tracer H129-G4 with strong labeling intensity, and a novel mono-synaptic anterograde tracer H129-ΔTK-td T. Methods Based on the bacterial artificial chromosome of H129, we have generated a serial of recombinant viruses for neuronal circuit tracing. Among them, H129-G4 was obtained by inserting binary tandemly connected GFP cassettes into the H129 genome, and H129-ΔTK-td T was obtained by deleting the thymidine kinase(TK) gene and adding td Tomato coding gene to the H129 genome. Then the obtained viral tracers were tested in vitro and in vivo for the tracing capacity. Results H129-G4 is capable of transmitting through multiple synapses, labeling the neurons by green florescent protein, and visualizing the morphological details of the labeled neurons. H129-ΔTK-td T neither replicates nor spreads in neurons alone, but transmits to and labels the postsynaptic neurons with td Tomato in the presence of complementary expressed TK from a helper virus. H129-ΔTK-td T is also capable to map the direct projectome of the specific neuron type in the given brain regions in Cre transgenic mice. In the tested brain regions where circuits are well known, the H129-ΔTK-td T tracing patterns are consistent with the previous results. Conclusion With the assistance of the helper virus complimentarily expressing TK, H129-ΔTK-td T replicates in the initially infected neuron, transmits anterogradely through one synapse, and labeled the postsynaptic neurons with td Tomato. The H129-ΔTK-td T anterograde monosynaptic tracing system offers a useful tool for mappi

关键词： Herpes simplex virus type 1(HSV-1) H129 strain anterograde neuronal tracer H129-ΔTK-tdT monosynaptic H129-G4 multisynaptic

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PI3K Regulatory Subunit P85 Regulates Mouse Cortical Development

PI3K Regulatory Subunit P85 Regulates Mouse Cortical Develop...

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中国神经科学学会第十二届全国学术会议

作者： Xinran Cheng Kaikai Li Meng Meng Liu Xinde Hu Mingrui Xu Runchuan Yan Shanting Zhao College of Veterinary Medicine Northwest A&F University Institute of Neuroscience State Key Laboratory of Neuroscience Key Laboratory of Primate Neurobiology CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences College of Food Science and Engineering Northwest A&F University

Mouse cortical development requires an exquisite regulation of neurons production and neuronal migration. Neurons production is mainly depend on the balance between proliferation and differentiation of neural progenitor cells(NPCs) and neural stem cells(NSCs), and after that, projection neurons migrate from the ventricular zone(VZ) to the cortical plate(CP), guided by radial glial cells(RGCs) with a transformation between bipolar and multipolar morphology. Previous studies have demonstrated that the PI3 K-Akt-m TOR signaling pathway plays a critical role in brain development. However, the function of p85 subunit in cortical development is still unclear. In this study, through primary neuron culture and in utero electroporation combined with immunohistochemistry and microscope image, we analyzed the function of p85 in mouse cortical development. Our results showed that overexpression of p85 inhibited NPCs proliferation. Also, overexpression of p85 perturbed the transition from intermediate neuronal progenitors(INPs) to projection neurons, and restrained neuronal ***'s more, we found that overexpression of p85 impaired cortical neuronal migration by affecting neuronal morphology. However, overexpression of p85 had impact on the final destination of cortical neurons, but did not affect ultimate fate decision of them. These findings indicated that the p85 subunit plays an essential role in architectural and functional integrity of the mouse cerebral cortex.

关键词： p85 subunit in utero electroporation neuronal migration proliferation differentiation

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Accelerating convolutional neural networks for mobile applications 16

Accelerating convolutional neural networks for mobile applic...

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24th ACM Multimedia Conference, MM 2016

作者： Wang, Peisong Cheng, Jian National Laboratory of Pattern Recognition CASIA Beijing China China University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology China

ISBN: (纸本)9781450336031

Convolutional neural networks (CNNs) have achieved remarkable performance in a wide range of computer vision tasks, typically at the cost of massive computational complexity. The low speed of these networks may hinder realtime applications especially when computational resources are limited. In this paper, an efficient and effective approach is proposed to accelerate the test-phase computation of CNNs based on low-rank and group sparse tensor decomposition. Specifically, for each convolutional layer, the kernel tensor is decomposed into the sum of a small number of low multilinear rank tensors. Then we replace the original kernel tensors in all layers with the approximate tensors and fine-tune the whole net with respect to the final classification task using standard backpropagation. Comprehensive experiments on ILSVRC-12 demonstrate significant reduction in computational complexity, at the cost of negligible loss in accuracy. For the widely used VGG-16 model, our approach obtains a 6.6× speed-up on PC and 5.91× speed-up on mobile device of the whole network with less than 1% increase on top-5 error. © 2016 ACM.

关键词： Image classification

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