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Brain-inspired artificial intelligence research: A review

Science China(Technological Sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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ExpTopo: Explicit Topological Modeling for Bronchus Segmentation 22

ExpTopo: Explicit Topological Modeling for Bronchus Segmenta...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Zhao, Mingyue Qiu, Xiaolan Zhou, S.Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China (USTC) China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research Ustc China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China (USTC) China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China

ISBN: (纸本)9798331520526

Airway extraction is paramount in the early diagnosis and treatment of respiratory diseases. As a tree-like structure, both topological-aware learning and voxel-wise classification are equally crucial for the airway. However, existing methods demonstrate insufficient topological learning, emphasizing only the supervision of individual key topological points. Consequently, this paper proposes a Explicit Topological Modeling (ExpTopo) approach to aid airway segmentation. It explicitly introduces topological metric space learning based on semantic segmentation, enhancing the model's structural perception by implementing global skeleton-level sparse topological learning (STL) and local voxel-level dense topological perception (DTP). Extensive experimental results demonstrate that the algorithm achieves competitive performance at both the topological and voxel levels. Code will be available in https://***/MorineZ/ExpTopo. © 2025 IEEE.

关键词： Airway extraction dense topological perception explicit topological modeling sparse topological learning

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Machine learning prediction of network dynamics with privacy protection

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Physical Review Research 2022年第4期4卷 043076-043076页

作者： Xin Xia Yansen Su Linyuan Lü Xingyi Zhang Ying-Cheng Lai Hai-Feng Zhang The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei 230601 China

Predicting network dynamics based on data, a problem with broad applications, has been studied extensively in the past, but most existing approaches assume that the complete set of historical data from the whole network is available. This requirement presents a great challenge in applications, especially for large, distributed networks in the real world, where data collection is accomplished by many clients in a parallel fashion. Often, each client only has the time series data from a partial set of nodes, and the client has access to only partial time stamps of the whole set of time series data and the partial structure of the network. Due to privacy concerns or license-related issues, the data collected by different clients cannot be shared. Accurately predicting the network dynamics while protecting the privacy of different parties is a critical problem in modern times. Here, we propose a solution based on federated graph neural networks (FGNNs) that enables the training of a global dynamic model for all parties without data sharing. We validate the working of our FGNN framework through two types of simulations to predict a variety of network dynamics (four discrete and three continuous dynamics). As a significant real-world application, we demonstrate successful prediction of state-wise influenza spreading in the USA. Our FGNN scheme represents a general framework to predict diverse network dynamics through collaborative fusing of the data from different parties without disclosing their privacy.

关键词： Collective behavior in networks

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Pctlfusion: A Progressive Fusion Network Via Contextual Texture Learning for Infrared and Visible Image

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SSRN 2024年

作者： Lu, Yixiang Gong, Daiwei Zhao, Dawei Maksim, Davydau Gao, Qingwei The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China Belarusian State University of Informatics and Radioelectronics Minsk Belarus

The objective of infrared and visible image fusion is to generate a fused image that contains rich texture details and salient targets. However, most of the existing fusion methods tend to focus on preserving texture details in visible image and salient targets in infrared image, ignoring the fact that infrared image have richer texture details and better visual effects than visible image under low-light conditions. In other words, differences in information sources under the condition of illumination imbalance is ignored in setting the task goal. In addition, existing network designs usually ignore multimodal information interactions during the coding phase, which may result in loss of information. In this work, we propose a progressive fusion network via contextual texture learning for infrared and visible image, termed as PCTLFusion. To enhance the limited texture information, we design a content and contextual texture learning module (CCTLM) to model the texture and content of both the source images, respectively. Then, an affine transform-based multimodal pre-fusion module (ATMPM) is designed to learn and pre-fuse the complementary and similar enhanced information, which achieves information interaction during encoding. To learn fully feature information using multilayers, we iterate CCTLM and ATMPM three times in the process of model forming. Finally, the fused image is reconstructed by decoding the output of the last ATMPM. Numerous experiments show that our proposed fusion algorithm is superior to the state-of-the-art existing methods in both subjective and objective evaluations. © 2024, The Authors. All rights reserved.

关键词： Textures

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Multi-view Fusion Enhanced Social Text Representation for Depression Detection 11th

Multi-view Fusion Enhanced Social Text Representation for D...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Chen, Jie You, Xuetong Zhao, Shu Xiao, Jingxuan Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Province Hefei 230601 China School of Computer Science and Technology Anhui University Anhui Province Hefei 230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Province Hefei 230601 China Faculty of Innovation Engineering Department of Computer Science Macau University of Science and Technology Macau China

ISBN: (纸本)9783031927409

Depression has become a major global public health challenge. Social media texts reflect users’ emotional and psychological states, providing a valuable data source for depression detection through natural language analysis. However, current research still faces limitations in modeling the collaborative representation of user texts and dynamic behaviors. To address this, we propose MVDep, a model that integrates multi-view features to enhance social text representation for depression detection. Specifically, we enhance text representation by investigating depression from multi-view, including social media texts, user profiles, and social behaviors, enabling more effective depression detection. First, we conduct initial filtering of social media texts using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and then screen out the uncertain domain of data samples based on the three-way decision (3WD). Next, we extract quantified user profiles and social behavioral features to enhance the representation of depression tendencies for uncertain domain samples. Finally, we fuse the multi-view features derived from social media texts, user profiles and social behaviors, improving the model’s ability to detect depression. We evaluate our model on the SWDD dataset, demonstrating its superiority over state-of-the-art baseline models in depression detection tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3WD Depression Detection Multi-View Feature Fusion Social Media Texts

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Systematic review of digital twin technology and applications

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Visual computing for Industry,Biomedicine,and Art 2023年第1期6卷 119-138页

作者： Jun-Feng Yao Yong Yang Xue-Cheng Wang Xiao-Peng Zhang Center for Digital Media Computing School of FilmXiamen UniversityXiamen 361005China School of Informatics Xiamen UniversityXiamen 361005China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and TourismXiamen 361005China State Key Laboratory of Multimodal Artificial Intelligence Systems the Institute of AutomationChinese Academy of SciencesBeijing 101408China

As one of the most important applications of digitalization,intelligence,and service,the digital twin(DT)breaks through the constraints of time,space,cost,and security on physical entities,expands and optimizes the relevant functions of physical entities,and enhances their application *** phenomenon has been widely studied in academia and *** this study,the concept and definition of DT,as utilized by scholars and researchers in various fields of industry,are *** internal association between DT and related technologies is *** four stages of DT development history are *** fundamentals of the technology,evaluation indexes,and model frameworks are ***,a conceptual ternary model of DT based on time,space,and logic is *** technology and application status of typical DT systems are ***,the current technical challenges of DT technology are analyzed,and directions for future development are discussed.

关键词： Digital twin Digitalization Virtual modeling Information technology Simulation

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MEFFGRN: Matrix enhancement and feature fusion-based method for reconstructing the gene regulatory network of epithelioma papulosum cyprini cells by spring viremia of carp virus infection

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Computers in Biology and Medicine 2024年 179卷 108835-108835页

作者： Wei, Pi-Jing Bao, Jin-Jin Gao, Zhen Tan, Jing-Yun Cao, Rui-Fen Su, Yansen Zheng, Chun-Hou Deng, Li Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University 111 Jiulong Road HefeiAnhui230601 China Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University 111 Jiulong Road HefeiAnhui230601 China Shenzhen Key Laboratory of Microbial Genetic Engineering College of Life Sciences and Oceanology Shenzhen University Guangdong Shenzhen518055 China School of Artificial Intelligence Anhui University 111 Jiulong Road HefeiAnhui230601 China

Gene regulatory networks (GRNs) are crucial for understanding organismal molecular mechanisms and processes. Construction of GRN in the epithelioma papulosum cyprini (EPC) cells of cyprinid fish by spring viremia of carp virus (SVCV) infection helps understand the immune regulatory mechanisms that enhance the survival capabilities of cyprinid fish. Although many computational methods have been used to infer GRNs, specialized approaches for predicting the GRN of EPC cells following SVCV infection are lacking. In addition, most existing methods focus primarily on gene expression features, neglecting the valuable network structural information in known GRNs. In this study, we propose a novel supervised deep neural network, named MEFFGRN (Matrix Enhancement- and Feature Fusion-based method for Gene Regulatory Network inference), to accurately predict the GRN of EPC cells following SVCV infection. MEFFGRN considers both gene expression data and network structure information of known GRN and introduces a matrix enhancement method to address the sparsity issue of known GRN, extracting richer network structure information. To optimize the benefits of CNN (Convolutional Neural Network) in image processing, gene expression and enhanced GRN data were transformed into histogram images for each gene pair respectively. Subsequently, these histograms were separately fed into CNNs for training to obtain the corresponding gene expression and network structural features. Furthermore, a feature fusion mechanism was introduced to comprehensively integrate the gene expression and network structural features. This integration considers the specificity of each feature and their interactive information, resulting in a more comprehensive and precise feature representation during the fusion process. Experimental results from both real-world and benchmark datasets demonstrate that MEFFGRN achieves competitive performance compared with state-of-the-art computational methods. Furthermore, study

关键词： Gene expression

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Anisotropic Infrared Absorption in Monolayer Black Phosphorus Metasurface With/Without Local Oxidative Effect

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SSRN 2023年

作者： Feng, Naixing Yin, Jianxiang Wang, Huan Wang, Xuan Zhang, Yuxian Huang, Zhixiang Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei230601 China

As one kind of anisotropic two-dimensional (2-D) layered material, the monolayer black phosphorus (BP) has ability to excite the surface plasmon resonance in the infrared spectrum, thus providing sufficient conditions for the strong light-matter interaction, so that some potential applications can be shown in the field of optoelectronics. Based on those physical properties, we design the patterned monolayer BP to achieve extremely high infrared absorption, and meanwhile use the finite element method to model the effects of factors such as n-type doping concentration, dielectric thickness, and angle of incidence light. Moreover, due to the strong anisotropic in-plane properties of monolayer BP, we analyze detailedly the absorption spectra in both the armchair and zigzag directions separately, and then understand that low-angle narrow-band absorption in the zigzag direction could be captured in that structure. The structure could achieve perfect absorption in the zigzag direction at an incidence angle of 89.3° with n-type doping concentration n=3×1013 cm-2 and medium thickness d = 4.6 μm, and the maximum absorption can reach 99.98% with the full width at half peak (FWHM) of only 0.57 μm. Considering the case on surface oxidation, it may occur some possible effects for the perfect absorption, but with our structure, the proposed narrow-band absorption still has wide applications in the sensing and light body monitoring. © 2023, The Authors. All rights reserved.

关键词： Monolayers

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A Multi-Layer Cyclic Predictive Reversible Hiding Scheme for Data Blind Extraction of Compressed Domain

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Jisuanji Xuebao/Chinese Journal of Computers 2025年第4期48卷 910-926页

作者： Wen, Wen-Ying Yang, Yu-Heng Luo, Xin-Yu Zhang, Yu-Shu Fang, Yu-Ming School of Computing and Artificial Intelligence Jiangxi University of Finance and Economics Nanchang330013 China Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing Nanchang330013 China

Reversible data hiding in compressed sensing (CS) domains has been extensively studied in recent years. The proposal and development of reversible data hiding schemes in this domain have made it possible to hide large-capacity data in CS signals. However, existing reversible data hiding schemes in the CS domain face several challenges and limitations while realizing high-capacity data hiding. First, existing schemes usually lack directly usable measurements, which prevents the secret-carrying measurements from being directly used to reconstruct the original image, and thus can be easily detected during the transmission of the secret information. Second, some schemes generate auxiliary information when hiding the secret information, and these auxiliary information require an additional secure channel for transmission to ensure the correct extraction of the secret information, thus these schemes are unable to realize the blind extraction of the secret information. To address these problems, this paper proposes a new scheme, i. e., multi-layer cyclic predictive reversible hiding scheme for data blind-extraction of compressed domain. This new scheme utilizes the asymptotic recovery property of CS and the prediction error expansion technique to hide and extract data through multi-layer cyclic prediction. Specifically, this scheme performs cyclic prediction of CS measurements by CS multilayer cyclic prediction embedding technique and generates sharp histograms of prediction errors, which provide a large-capacity space for secret data embedding. Then, the additional information is embedded in the prediction error and then superimposed on the measurements by employing the histogram shifting method, and finally the auxiliary information is hidden in the lowest significant bit of the measurements in the specified region. In this way, blind extraction of information can also be realized when extracting information, and the lossless restoration of measurement values can be guar

关键词： Data encapsulation

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On four conjectures of Heng-Ding and p-ary linear codes from monomials

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IEEE Transactions on Information Theory 2025年

作者： Qian, Liqin Shi, Minjia Anhui University School of Mathematical Sciences Anhui Hefei230601 China Anhui University Key Laboratory of Intelligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui Hefei230601 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China

Subfield codes of linear codes over finite fields have recently attracted great attention due to their wide applications in secret sharing schemes, authentication codes and association schemes. There are two major ingredients in this paper. The first ingredient is to solve four conjectures recently proposed by Heng and Ding (IEEE Trans. Inf. Theory, 68(6): 3643-3656, 2022). Besides, we also determine the weight distributions of subfield codes derived from x3 and oval polynomials over F2m. The second ingredient is to obtain two classes of p-ary linear codes from monomials over Fpm. We study the parameters of the constructed codes and determine their weight distributions. Notably, we show that the codes C(p) (xpi, pm) are optimal and almost optimal in many cases with respect to the online Database of Grassl. Finally, we observe that the derived linear codes also have the minimality property for most cases. © 1963-2012 IEEE.

关键词： Linear code monomial oval polynomial subfield code weight distribution

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