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International Joint Conference on Neural Networks (IJCNN)

作者： Zilu Wang Xin Yao Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Shenzhen China Research Institute of Trustworthy Autonomous Systems Shenzhen China School of Computer Science University of Birmingham Birmingham UK

In this work, we propose a fully hardware implementation of Hopfield neural network (HNN) based on memristive arrays, which adopts a bottom-up brain-inspired hardware framework. Memristors are used as key computing units to design cell modules in the low layer to play roles similar to neurons and synapses, so as to perform information integration, filtering, and plasticity of weights in a simple circuit structure and in-memory computing way. Cell modules are cascaded by the mode of encoding and mapping based on HNN in a structured regular circuit way to construct functional modules with braininspired parallel analog computing capacity in middle layers. Different functional modules perform information interaction according to the system's requirements and goals, thus realizing the overall system in the top layer. Our proposed HNN system is then used to solve combinatorial optimization problems. Different from other similar work, our system starts from a memristor-based brain-inspired framework and is implemented fully in hardware. The experimental results show that our work can not only improve the convergence speed, but also can be conveniently used to solve problems of different scales because of its good scalability. In addition, with hardware overhead and power consumption analysis, our system has been shown to be very hardware-friendly. Our work represents an advance towards a memristor-based hardware system with brain-inspired structure and high performance.

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Generating Redstone Style Cities in Minecraft

arXiv

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arXiv 2023年

作者： Huang, Shuo Hu, Chengpeng Togelius, Julian Liu, Jialin Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Tandon School of Engineering New York University New York10012 United States

Procedurally generating cities in Minecraft provides players more diverse scenarios and could help understand and improve the design of cities in other digital worlds and the real world. This paper presents a city generator that was submitted as an entry to the 2023 Edition of Minecraft Settlement Generation Competition for Minecraft. The generation procedure is composed of six main steps, namely vegetation clearing, terrain reshaping, building layout generation, route planning, streetlight placement, and wall construction. Three algorithms, including a heuristic-based algorithm, an evolving layout algorithm, and a random one are applied to generate the building layout, thus determining where to place different redstone style buildings, and tested by generating cities on random maps in limited time. Experimental results show that the heuristic-based algorithm is capable of finding an acceptable building layout faster for flat maps, while the evolving layout algorithm performs better in evolving layout for rugged maps. A user study is conducted to compare our generator with outstanding entries of the competition's 2022 edition using the competition's evaluation criteria and shows that our generator performs well in the adaptation and functionality criteria. Copyright © 2023, The Authors. All rights reserved.

关键词： Heuristic algorithms

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Bi-Objective Splitting Delivery VRP with Loading Constraints and Restricted Access

Bi-Objective Splitting Delivery VRP with Loading Constraints...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Jiyuan Pei Chengpeng Hu Jialin Liu Yi Mei Xin Yao Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China School of Engineering and Computer Science Victoria University of Wellington Kelburn New Zealand

ISBN: (纸本)9781728190495

The splitting delivery vehicle routing problem with 3-dimensional loading constraints (3L-SDVRP) is an important and challenging VRP variant. The problem consists of two subproblems: the routing and 3D bin packing, each of which is NP-hard by itself. Compared with CVRP, 3L-SDVRP is much closer to the reality. There have been many studies on 3L-SDVRP. However, to our best knowledge, no complete mathematical model has been formalised with comprehensive loading constraints and there are still some real-world factors ignored in the existing studies. In this paper, we consider a more realistic 3L-SDVRP model with restricted access provided by the 2021 HUAWEI Logistics Competition, which is different from existing problem models in two aspects. First, this problem considers the total travel cost and average loading rate simultaneously. Second, this problem has additional constraints related to certain special pickup points. These differences make existing optimisation approaches not directly applicable. A major contribution of this paper is the formal mathematical model developed for this new 3L-SDVRP. In addition, we propose a genetic algorithm with an efficient fitness evaluation. The proposed algorithm has been demonstrated to significantly outperform the baseline solver provided by the competition in solving the problem instances from the competition and the ones adapted from benchmark datasets of related problems.

关键词： Three-dimensional displays Loading Vehicle routing Benchmark testing Routing Mathematical models Optimization

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CONIC10K: A Challenging Math Problem Understanding and Reasoning Dataset

arXiv

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arXiv 2023年

作者： Wu, Haoyi Hui, Wenyang Chen, Yezeng Wu, Weiqi Tu, Kewei Zhou, Yi School of Information Science and Technology ShanghaiTech University China Shanghai Engineering Research Center of Intelligent Vision and Imaging China School of Information Science and Technology University of Science and Technology of China China National Engineering Laboratory for Brain-inspired Intelligence Technology and Application China Key Laboratory of Brain Cognition and Education Sciences Ministry of Education China Shanghai Innovation Center for Processor Technologies China Department of Computer Science and Engineering Shanghai Jiao Tong University China

Mathematical understanding and reasoning are crucial tasks for assessing the capabilities of artificial intelligence (AI). However, existing benchmarks either require just a few steps of reasoning, or only contain a small amount of data in one specific topic, making it hard to analyse AI's behaviour with reference to different problems within a specific topic in detail. In this work, we propose CONIC10K, a challenging math problem dataset on conic sections in Chinese senior high school education. Our dataset contains various problems with different reasoning depths, while only the knowledge from conic sections is required. Since the dataset only involves a narrow range of knowledge, it is easy to separately analyse the knowledge a model possesses and the reasoning ability it has. For each problem, we provide a high-quality formal representation, the reasoning steps, and the final solution. Experiments show that existing large language models, including GPT-4, exhibit weak performance on complex reasoning. We hope that our findings could inspire more advanced techniques for precise natural language understanding and reasoning. Our dataset and codes are available at https://***/whyNLP/Conic10K. © 2023, CC BY.

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Graph convolutional neural networks via motif-based attention

arXiv

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arXiv 2018年

作者： Peng, Hao Li, Jianxin Gong, Qiran Wang, Senzhang Ning, Yuanxing Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Department of Computer Science University of Illinois at Chicago

Many real-world problems can be represented as graph-based learning problems. In this paper, we propose a novel framework for learning spatial and attentional convolution neural networks on arbitrary graphs. Different from previous convolutional neural networks on graphs, we first design a motif-matching guided subgraph normalization method to capture neighborhood information. Then we implement subgraph-level self-attentional layers to learn different importances from different subgraphs to solve graph classification problems. Analogous to image-based attentional convolution networks that operate on locally connected and weighted regions of the input, we also extend graph normalization from one-dimensional node sequence to two-dimensional node grid by leveraging motif-matching, and design self-attentional layers without requiring any kinds of cost depending on prior knowledge of the graph structure. Our results on both bioinformatics and social network datasets show that we can significantly improve graph classification benchmarks over traditional graph kernel and existing deep models. Copyright © 2018, The Authors. All rights reserved.

关键词： Graphic methods

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Implantable brain-computer interface Based On Printing technology

Implantable Brain-Computer Interface Based On Printing Techn...

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International Winter Workshop on brain-computer interface (BCI)

作者： Lusheng Liu Bo Wen Minjie Wang Aiping Wang Jing Zhang Yuan Zhang Song Le Lihua Zhang Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Meta-Medical Academy for Engineering & Technology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Fudan University Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

Implantable brain-computer interface is a braincomputer interaction device by implants optrodes/electrodes on the surface or deep of the brain. By stimulating and recording neural signals, we can explore the function of neural tissue and further guide disease treatment or brain signal decoding. This article reviews the development of printed electronics in implantable brain-computer interfaces, mainly discussing the advantages and disadvantages of printed electrodes in braincomputer interfaces and the possibility of printing technology used in optogenetic brain-computer interfaces, to illuminate a great prospect of printing technology in implantable braincomputer interfaces.

关键词： Printing Electrodes Implants Organic light emitting diodes brain-computer interfaces Optogenetics Recording

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Integration of Multimodal Data for Deciphering brain Disorders

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Biomedical Data science 2021年第1期4卷 43-56页

作者： Jingqi Chen Guiying Dong Liting Song Xingzhong Zhao Jixin Cao Xiaohui Luo Jianfeng Feng Xing-Ming Zhao 3Zhangjiang Fudan International Innovation Center Fudan University Shanghai 200433 China 2MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science Fudan University Ministry of Education Shanghai 200433 China 1Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai 200433 China email: xmzhao@***jffeng@*** 4Department of Computer Science University of Warwick Coventry CV4 7AL United Kingdom

The accumulation of vast amounts of multimodal data for the human brain, in both normal and disease conditions, has provided unprecedented opportunities for understanding why and how brain disorders arise. Compared with traditional analyses of single datasets, the integration of multimodal datasets covering different types of data (i.e., genomics, transcriptomics, imaging, etc.) has shed light on the mechanisms underlying brain disorders in greater detail across both the microscopic and macroscopic levels. In this review, we first briefly introduce the popular large datasets for the brain. Then, we discuss in detail how integration of multimodal human brain datasets can reveal the genetic predispositions and the abnormal molecular pathways of brain disorders. Finally, we present an outlook on how future data integration efforts may advance the diagnosis and treatment of brain disorders.

关键词： brain disorders data integration molecular omics genetics epigenetics neuroimaging

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Effectiveness of Neurofeedback-Assisted and Conventional 6-Week Web-Based Mindfulness Interventions on Mental Health of Chinese Nursing Students: Randomized Controlled Trial

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Journal of Medical Internet Research 2025年 27卷 e71741页

作者： Shu Jing Zhenwei Dai Xiaoyang Liu Xuelin Yang Jinglei Cheng Tianming Chen Zihang Feng Xin Liu Fenghe Dong You Xin Zhuoyan Han Haiyan Hu Xiaoyou Su Chen Wang School of Population Medicine and Public Health Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China Peking University Sixth Hospital Peking University Beijing China Peking University Institute of Mental Health Peking University Beijing China National Health Commission Key Laboratory of Mental Health Peking University Beijing China National Clinical Research Center for Mental Disorders Peking University Sixth Hospital Beijing China School of Nursing and Institute of Nursing Research School of Medicine Zhejiang University Hangzhou China School of Nursing Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China College of Computer Science and Technology Zhejiang University Hangzhou China The State Key Laboratory of Brain-machine Intelligence Zhejiang University Hangzhou China School of Translation and Interpreting Beijing Language and Culture University Beijing China Shenzhen Maternity and Child Healthcare Hospital Southern Medical University Shenzhen China State Key Laboratory of Respiratory Health and Multimorbidity Beijing China

Background Nursing students experience disproportionately high rates of mental health challenges, underscoring the urgent need for innovative, scalable interventions. Web-based mindfulness programs, and more recently, neurofeedback-enhanced approaches, present potentially promising avenues for addressing this critical issue. Objective This study aimed to explore the effectiveness of the neurofeedback-assisted online mindfulness intervention (NAOM) and the conventional online mindfulness intervention (COM) in reducing mental health symptoms among Chinese nursing students. Methods A 3-armed randomized controlled trial was conducted among 147 nursing students in Beijing, China, using a 6-week web-based mindfulness program. Participants received NAOM, COM, or general mental health education across 6 weeks. Electroencephalogram and validated tools such as the Patient Health Questionnaire and the Generalized Anxiety Disorder Questionnaire were used to primarily assess symptoms of depression and anxiety at baseline, immediately after the intervention, and at 1 and 3 months after the intervention. Generalized estimating equations were used to evaluate the effects of intervention and time. Results A total of 155 participants enrolled in the study, and 147 finished all assessments. Significant reductions in the symptoms of depression, anxiety, and fatigue were observed in the NAOM (mean difference [MD]=−3.330, Cohen d =0.926, P <.001; MD=−3.468, Cohen d =1.091, P <.001; MD=−2.620, Cohen d =0.743, P <.001, respectively) and the COM (MD=−1.875, Cohen d =0.490, P =.03; MD=−1.750, Cohen d =0.486, P =.02; MD=−2.229, Cohen d =0.629, P =.01, respectively) groups compared with the control group at postintervention assessment. Moreover, the NAOM group showed significantly better effects than the COM group in alleviating depressive symptoms (MD=−1.455; Cohen d =0.492; P =.04) and anxiety symptoms (MD=−1.718; Cohen d =0.670; P =.04) and improving the level of mindfulness (MD=−3.765; Coh

关键词： online mindfulness neurofeedback nursing student mental health randomized controlled trial depression anxiety

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Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

Noncontact Doppler Radar-based Heart Rate Detection on the S...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Shi, Qiuxia Hu, Bin Tian, Fuze Zhao, Qinglin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University China School of Computer and Communication Lanzhou University of Technology China Key Laboratory of Special Functional Materials and Structural Design Lanzhou University Ministry of Education Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education China

ISBN: (纸本)9781665468190

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar. © 2022 IEEE.

关键词： Singular value decomposition

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Can the brain do backpropagation? — exact implementation of backpropagation in predictive coding networks 20

Can the brain do backpropagation? — exact implementation of...

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Yuhang Song Thomas Lukasiewicz Zhenghua Xu Rafal Bogacz Department of Computer Science University of Oxford UK State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China MRC Brain Network Dynamics Unit University of Oxford UK

ISBN: (纸本)9781713829546

Backpropagation (BP) has been the most successful algorithm used to train artificial neural networks. However, there are several gaps between BP and learning in biologically plausible neuronal networks of the brain (learning in the brain, or simply BL, for short), in particular, (1) it has been unclear to date, if BP can be implemented exactly via BL, (2) there is a lack of local plasticity in BP, i.e., weight updates require information that is not locally available, while BL utilizes only locally available information, and (3) there is a lack of autonomy in BP, i.e., some external control over the neural network is required (e.g., switching between prediction and learning stages requires changes to dynamics and synaptic plasticity rules), while BL works fully autonomously. Bridging such gaps, i.e., understanding how BP can be approximated by BL, has been of major interest in both neuroscience and machine learning. Despite tremendous efforts, however, no previous model has bridged the gaps at a degree of demonstrating an equivalence to BP, instead, only approximations to BP have been shown. Here, we present for the first time a framework within BL that bridges the above crucial gaps. We propose a BL model that (1) produces exactly the same updates of the neural weights as BP, while (2) employing local plasticity, i.e., all neurons perform only local computations, done simultaneously. We then modify it to an alternative BL model that (3) also works fully autonomously. Overall, our work provides important evidence for the debate on the long-disputed question whether the brain can perform BP.

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