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arXiv 2023年

作者： Pan, Wenxuan Zhao, Feifei Zeng, Yi Han, Bing Brain-inspired Cognitive Intelligence Lab Institute of Automation Chinese Academy of Sciences Beijing China School of Future Technology University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China

The architecture design and multi-scale learning principles of the human brain that evolved over hundreds of millions of years are crucial to realizing human-like intelligence. Spiking Neural Network (SNN) based Liquid State Machine (LSM) serves as a suitable architecture to study brain-inspired intelligence because of its brain-inspired structure and the potential for integrating multiple biological principles. Existing researches on LSM focus on different certain perspectives, including high-dimensional encoding or optimization of the liquid layer, network architecture search, and application to hardware devices. There is still a lack of in-depth inspiration from the learning and structural evolution mechanism of the brain. Considering these limitations, this paper presents a novel LSM learning model that integrates adaptive structural evolution and multi-scale biological learning rules. For structural evolution, an adaptive evolvable LSM model is developed to optimize the neural architecture design of liquid layer with separation property. For brain-inspired learning of LSM, we propose a dopamine-modulated Bienenstock-Cooper-Munros (DA-BCM) method that incorporates global long-term dopamine regulation and local trace-based BCM synaptic plasticity. Comparative experimental results on different decision-making tasks show that introducing structural evolution of the liquid layer, and the DA-BCM regulation of the liquid layer and the readout layer could improve the decision-making ability of LSM and flexibly adapt to rule reversal. This work is committed to exploring how evolution can help to design more appropriate network architectures and how multi-scale neuroplasticity principles coordinated to enable the optimization and learning of LSMs for relatively complex decision-making tasks. © 2023, CC BY-NC-ND.

关键词： Network architecture

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A Portable System of Mental Fatigue Detection and Mitigation based on Physiological Signals

A Portable System of Mental Fatigue Detection and Mitigation...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Wu, Chao Tian, Fuze Shi, Qiuxia Zhao, Qinglin Hu, Bin Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Lanzhou China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China Ministry of Education Lanzhou China

ISBN: (纸本)9781665468190

Mental fatigue of the brain will cause the weakening of the psychological and physiological functions of the human body, which increases the risk of mistakes and accidents. This paper designs a mental fatigue detection and mitigation system based on human physiological signals and classical music. Through the fatigue assessment and mitigation experiment on 20 healthy subjects, it is verified that the system is able to judge the mental fatigue state of the subjects and play classical music to mitigate fatigue via acquiring and analyzing (alpha+theta)/beta of electroencephalogram (EEG) and heart rate variability (HRV) of ballistocardiogram (BCG). The designed system realizes signal acquisition and fatigue analysis only through using portable device, this makes it have certain application value in office building, hospital, aircraft driving and other scenes. © 2022 IEEE.

关键词： Electroencephalography

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An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain

arXiv

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arXiv 2023年

作者： He, Xiang Zhao, Dongcheng Li, Yang Shen, Guobin Kong, Qingqun Zeng, Yi Brain-inspired Cognitive Intelligence Lab Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Future Technology University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China

Spiking neural networks (SNNs) are rich in spatio-temporal dynamics and are suitable for processing event-based neuromorphic data. However, event-based datasets are usually less annotated than static datasets. This small data scale makes SNNs prone to overfitting and limits their performance. In order to improve the generalization ability of SNNs on event-based datasets, we use static images to assist SNN training on event data. In this paper, we first discuss the domain mismatch problem encountered when directly transferring networks trained on static datasets to event data. We argue that the inconsistency of feature distributions becomes a major factor hindering the effective transfer of knowledge from static images to event data. To address this problem, we propose solutions in terms of two aspects: feature distribution and training strategy. Firstly, we propose a knowledge transfer loss, which consists of domain alignment loss and spatio-temporal regularization. The domain alignment loss learns domain-invariant spatial features by reducing the marginal distribution distance between the static image and the event data. Spatio-temporal regularization provides dynamically learnable coefficients for domain alignment loss by using the output features of the event data at each time step as a regularization term. In addition, we propose a sliding training strategy, which gradually replaces static image inputs probabilistically with event data, resulting in a smoother and more stable training for the network. We validate our method on neuromorphic datasets, including N-Caltech101, CEP-DVS, and N-Omniglot. The experimental results show that our proposed method achieves better performance on all datasets compared to the current state-of-the-art methods. Code is available at https://***/brain-Cog-Lab/Transfer-for-DVS. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

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Remote Operation with Haptic Force and Virtual Proxy for an Underwater Vehicle-Manipulator System 10

Remote Operation with Haptic Force and Virtual Proxy for an ...

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10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021

作者： Ma, Jin Wang, Yu Wang, Rui Wang, Shuo University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Cas Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China

ISBN: (纸本)9781665424233

This paper aims to investigate a smooth teleoperation method for the underwater vehicle-manipulator system. First, a coordinated mapping control method for the vehicle is presented. The haptic force is considered to help assist the operation. Then, two mapping modes are used to teleoperate the manipulator: when the end-effector needs to move in a large area, two virtual points and a spring-damping system are implemented to filter the operator's hand jitter and limit the manipulator's speed;when the end-effector needs to move in a small area, a position increment control method with a small proportional coefficient is used to improve the precision. Finally, the simulation demonstrates the effectiveness of the proposed teleoperation method. © 2021 IEEE.

关键词： Manipulators

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ROTATION TRANSFORMATION NETWORK: LEARNING VIEW-INVARIANT POINT CLOUD FOR CLASSIFICATION AND SEGMENTATION

ROTATION TRANSFORMATION NETWORK: LEARNING VIEW-INVARIANT POI...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Deng, Shuang Liu, Bo Dong, Qiulei Hu, Zhanyi National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China

ISBN: (纸本)9781665438643

Many recent works show that a spatial manipulation module could boost the performances of deep neural networks (DNNs) for 3D point cloud analysis. In this paper, we aim to provide an insight into spatial manipulation modules. Firstly, we find that the smaller the rotational degree of freedom (RDF) of objects is, the more easily these objects are handled by these DNNs. Then, we investigate the effect of the popular T-Net module and find that it could not reduce the RDF of objects. Motivated by the above two issues, we propose a rotation transformation network for point cloud analysis, called RTN, which could reduce the RDF of input 3D objects to 0. The RTN could be seamlessly inserted into many existing DNNs for point cloud analysis. Extensive experimental results on 3D point cloud classification and segmentation tasks demonstrate that the proposed RTN could improve the performances of several state-of-the-art methods significantly. © 2021 IEEE

关键词： Deep neural networks

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Cyclin A2/cyclin-dependent kinase 1-dependent phosphorylation of Top2a is required for S phase entry during retinal development in zebrafish

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Journal of Genetics and Genomics 2021年第1期48卷 63-74页

作者： Miaomiao Jin Jingyu Li Ruikun Hu Baijie Xu Guanliang Huang Weilai Huang Bo Chen Jie He Ying Cao Department of Clinical Laboratory Medicine of Shanghai Tenth People’s Hospital Tongji University School of Life Sciences and TechnologyShanghai 200092China Institute of Neuroscience State Key Laboratory of NeuroscienceCAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai 200031China University of Chinese Academy of Sciences Beijing 100049China

Cyclin-dependent kinase 1 (CDK1) plays an essential role in cell cycle ***,as mouse Cdk1embryos die early,the role of CDK1 in regulating the cell cycle and embryo development remains ***,we showed that zebrafish cdk1^(-/-)embryos exhibit severe microphthalmia accompanied by multiple defects in S phase entry,M phase progression,and cell differentiation but not in interkinetic nuclear *** identified Top2a as a potential downstream target and cyclin A2 and cyclin B1 as partners of Cdk1 in cell cycle regulation via an in silico *** depletion of either cyclin A2 or Top2a led to the decreased S phase entry in zebrafish retinal cells,the depletion of cyclin B1 led to M phase ***,phosphorylation of Top2a at serine 1213 (S1213) was nearly abolished in both cdk1 and ccna2mutants,but not in ccnb1 ***,overexpression of TOP2A^(S1213D),the phosphomimetic form of human TOP2A,rescued S phase entry and alleviated the microphthalmia defects in both cdk1^(-/-)and ccna2^(-/-)*** together,our data suggest that Cdk1 interacts with cyclin A2 to regulate S phase entry partially through Top2a phosphorylation and interacts with cyclin B1 to regulate M phase progression.

关键词： Cdk1 Cyclin A2 Cyclin B1 Top2a M phase S phase entry IKNM

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Pan-cancer network disorders revealed by overall and local signaling entropy

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Journal of Molecular Cell Biology 2021年第9期13卷 622-635页

作者： Li Feng Yi-Di Sun Chen Li Yi-Xue Li Luo-Nan Chen Rong Zeng CAS Key Laboratory of Systems Biology CAS Center for Excellence in Molecular Cell ScienceInstitute of Biochemistry and Cell BiologyChinese Academy of SciencesShanghai 200031China University of Chinese Academy of Sciences Shanghai 200031China Institute of Neuroscience CAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai 200031China Center for Single-Celt Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyCAS-MPG Partner Institute for Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China CAS Key Laboratory of Systems Biology Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou 310024China

Tumor development is a process involving loss of the differentiation phenotype and acquisition of stem-like characteristics,which is driven by intracellular rewiring of signaling *** measurement of network reprogramming and disorder would be challenging due to the complexity and heterogeneity of ***,we proposed signaling entropy(SR)to assess the degree of tumor network *** calculated SR for 33 tumor types in The Cancer Genome Atlas database based on transcrip-tomic and proteomic *** SR of tumors was significantly higher than that of normal samples and was highly correlated with cell sternness,cancer type,tumor grade,and *** further demonstrated the sensitivity and accuracy of using local SR in prognosis prediction and drug response ***,SR could reveal cancer network disorders related to tumor malignant potency,clinical prognosis,and drug response.

关键词： pan-cancer network entropy

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Spatial and Temporal Abnormalities of Spontaneous Fixational Saccades and Their Correlates With Positive and Cognitive Symptoms in Schizophrenia (vol 50, pg 78, 2023)

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SCHIZOPHRENIA BULLETIN 2024年第2期50卷 485-485页

作者： Liu, Xu Li, Yu Xu, Lihua Zhang, Tianhong Cui, Huiru Wei, Yanyan Xia, Mengqing Su, Wenjun Tang, Yingying Tang, Xiaochen Zhang, Dan Spillmann, Lothar Andolina, Ian Max McLoughlin, Niall Wang, Wei Wang, Jijun Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine Shanghai China. Institute of Neuroscience Center for Excellence in Brain Science and Intelligence Technology State Key Laboratory of Neuroscience Key Laboratory of Primate Neurobiology Chinese Academy of Sciences Shanghai China. Department of Psychological Medicine Children's Hospital of Fudan University National Children's Medical Center Shanghai China. Department of Neurology University of Freiburg Freiburg Germany. Shanghai Center for Brain and Brain-inspired Intelligence Technology Shanghai China. School of Optometry and Vision Science University of Bradford Bradford UK. University of Chinese Academy of Sciences Beijing China. CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) Chinese Academy of Science Beijing China. Institute of Psychology and Behavioral Science Shanghai Jiao Tong University Shanghai China.

BACKGROUND AND HYPOTHESIS:Visual fixation is a dynamic process, with the spontaneous occurrence of microsaccades and macrosaccades. These fixational saccades are sensitive to the structural and functional alterations of the cortical-subcortical-cerebellar circuit. Given that dysfunctional cortical-subcortical-cerebellar circuit contributes to cognitive and behavioral impairments in schizophrenia, we hypothesized that patients with schizophrenia would exhibit abnormal fixational saccades and these abnormalities would be associated with the clinical manifestations. STUDY DESIGN:Saccades were recorded from 140 drug-naïve patients with first-episode schizophrenia and 160 age-matched healthy controls during ten separate trials of 6-second steady fixations. Positive and negative symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS). Cognition was assessed using the Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery (MCCB). STUDY RESULTS:Patients with schizophrenia exhibited fixational saccades more vertically than controls, which was reflected in more vertical saccades with angles around 90° and a greater vertical shift of horizontal saccades with angles around 0° in patients. The fixational saccades, especially horizontal saccades, showed longer durations, faster peak velocities, and larger amplitudes in patients. Furthermore, the greater vertical shift of horizontal saccades was associated with higher PANSS total and positive symptom scores in patients, and the longer duration of horizontal saccades was associated with lower MCCB neurocognitive composite, attention/vigilance, and speed of processing scores. Finally, based solely on these fixational eye movements, a K-nearest neighbors model classified patients with an accuracy of 85%. Conclusions: Our results reveal spatial and temporal abnormalities of fixational saccades and suggest fixational saccades as a promising biomarker for cognitive and

关键词： First-episode schizophrenia biomarker classification cognition and symptoms microsaccades and macrosaccades visual fixation

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Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction

arXiv

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arXiv 2022年

作者： Shao, Pengpeng Liu, Tong Che, Feihu Zhang, Dawei Tao, Jianhua National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China

Temporal knowledge prediction is a crucial task for the event early warning that has gained increasing attention in recent years, which aims to predict the future facts by using relevant historical facts on the temporal knowledge graphs. There are two main difficulties in this prediction task. First, from the historical facts point of view, how to model the evolutionary patterns of the facts to predict the query accurately. Second, from the query perspective, how to handle the two cases where the query contains seen and unseen entities in a unified framework. Driven by the two problems, we propose a novel adaptive pseudo-siamese policy network for temporal knowledge prediction based on reinforcement learning. Specifically, we design the policy network in our model as a pseudo-siamese policy network that consists of two sub-policy networks. In sub-policy network I, the agent searches for the answer for the query along the entity-relation paths to capture the static evolutionary patterns. And in sub-policy network II, the agent searches for the answer for the query along the relation-time paths to deal with unseen entities. Moreover, we develop a temporal relation encoder to capture the temporal evolutionary patterns. Finally, we design a gating mechanism to adaptively integrate the results of the two sub-policy networks to help the agent focus on the destination answer. To assess our model performance, we conduct link prediction on four benchmark datasets, the experimental results demonstrate that our method obtains considerable performance compared with existing methods. © 2022, CC BY.

关键词： Forecasting

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Improving Visual Grounding with Visual-Linguistic Verification and Iterative Reasoning

arXiv

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arXiv 2022年

作者： Yang, Li Xu, Yan Yuan, Chunfeng Liu, Wei Li, Bing Hu, Weiming NLPR Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China The Chinese University of Hong Kong Hong Kong CAS Center for Excellence in Brain Science and Intelligence Technology China

Visual grounding is a task to locate the target indicated by a natural language expression. Existing methods extend the generic object detection framework to this problem. They base the visual grounding on the features from pre-generated proposals or anchors, and fuse these features with the text embeddings to locate the target mentioned by the text. However, modeling the visual features from these predefined locations may fail to fully exploit the visual context and attribute information in the text query, which limits their performance. In this paper, we propose a transformer-based framework for accurate visual grounding by establishing text-conditioned discriminative features and performing multi-stage cross-modal reasoning. Specifically, we develop a visual-linguistic verification module to focus the visual features on regions relevant to the textual descriptions while suppressing the unrelated areas. A language-guided feature encoder is also devised to aggregate the visual contexts of the target object to improve the object's distinctiveness. To retrieve the target from the encoded visual features, we further propose a multi-stage cross-modal decoder to iteratively speculate on the correlations between the image and text for accurate target localization. Extensive experiments on five widely used datasets validate the efficacy of our proposed components and demonstrate state-of-the-art performance. Our code is public at https://***/yangli18/VLTVG. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

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