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China's new ethical guidelines for the use of brain±computer interfaces

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National science Review 2024年第4期 7-8页

作者： Mu-ming Poo Institute of Neuroscience CAS Center for Excellence in Brain Science and Intelligence Technology NSR

While the world is bewildered by the dazzling advances of generative artificial intelligence and struggling to find consensual guidelines for its governance, the rapid pace of research on the brain–computer interface (BCI) also calls for governance on its development and application. In early February, China’s Ministry of science and technology (Mo ST) announced the ‘Guidelines for Research Ethics in brain-computer Interface’[1] based on recommendations of the National Research Ethics Committee. Its content is largely in line with an earlier announcement in October 2023 by the Psychiatry Society of China [2] on ‘Ethics Governance of brain-Computer Interface Research in Mental Disorders’. Both guidelines are formulated on the principles of the World Medical Association Declaration of Helsinki on ‘Ethical Principles for Medical Research Involving Human Subjects’in 2008.

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Prompt learning in computer vision: a survey

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Frontiers of Information technology & Electronic Engineering 2024年第1期25卷 42-63页

作者： Yiming LEI Jingqi LI Zilong LI Yuan CAO Hongming SHAN Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200438China Institute of science and technology for brain-Inspired intelligence Fudan UniversityShanghai 200433China MOE Frontiers center for brain science Fudan UniversityShanghai 200433China Shanghai center for brain science and brain-Inspired technology Shanghai 201210China

Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligencegenerated content (AIGC). In this survey, we provide a progressive and comprehensive review of visual promptlearning as related to AIGC. We begin by introducing VLM, the foundation of visual prompt learning. Then, wereview the vision prompt learning methods and prompt-guided generative models, and discuss how to improve theefficiency of adapting AIGC models to specific downstream tasks. Finally, we provide some promising researchdirections concerning prompt learning.

关键词： Prompt learning Visual prompt tuning(VPT) Image generation Image classification Artificial intelligence generated content(AIGC)

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Tuning Synaptic Connections Instead of Weights by Genetic Algorithm in Spiking Policy Network

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Machine intelligence Research 2024年第5期21卷 906-918页

作者： Duzhen Zhang Tielin Zhang Shuncheng Jia Qingyu Wang Bo Xu School of Artificial intelligence University of Chinese Academy of SciencesBeijing100049China Institute of Automation Chinese Academy of Sciences(CAS)Beijing100190China center for excellence in brain science and intelligence technology Chinese Academy of Sciences(CAS)Shanghai200031China

Learning from interaction is the primary way that biological agents acquire knowledge about their environment and *** deep reinforcement learning(DRL)explores a computational approach to learning from interaction and has made significant progress in solving various ***,despite its power,DRL still falls short of biological agents in terms of energy *** the underlying mechanisms are not fully understood,we believe that the integration of spiking communication between neurons and biologically-plausible synaptic plasticity plays a prominent role in achieving greater energy *** this biological intuition,we optimized a spiking policy network(SPN)using a genetic algorithm as an energy-efficient alternative to *** SPN mimics the sensorimotor neuron pathway of insects and communicates through event-based *** by biological research showing that the brain forms memories by creating new synaptic connections and rewiring these connections based on new experiences,we tuned the synaptic connections instead of weights in the SPN to solve given *** results on several robotic control tasks demonstrate that our method can achieve the same level of performance as mainstream DRL methods while exhibiting significantly higher energy efficiency.

关键词： Spiking neural networks genetic evolution bio-inspired learning agent&cognitive architectures robotic control

The Neural Mechanism of Knowledge Assembly in the Human brain Inspires Artificial intelligence Algorithm

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Neuroscience Bulletin 2024年第2期40卷 280-282页

作者： Xiang Ji Wentao Jiang Xiaoru Zhang Ming Song Shan Yu Tianzi Jiang University of the Chinese Academy of sciences Beijing 100049China brainnetome center Institute of AutomationThe Chinese Academy of SciencesBeijing 100190China Laboratory of brain Atlas and brain-inspired intelligence Institute of AutomationThe Chinese Academy of SciencesBeijing 100190China CAS center for excellence in brain science and intelligence technology The Chinese Academy of SciencesBeijing 100190China Research center for Augmented intelligence Zhejiang LabHangzhou 311100China

When new information enters the brain,a human's prior knowledge of the world can change rapidly through a process referred to as"knowledge assembly".Recently,Nelli et *** the neural correlates of knowledge assembly in the human brain using functional ***,inspired by the neural mechanism,the authors developed an artificial neural network algorithm to permit rapid knowledge assembly,improving the flexibility of the system[1].Once again,this research demonstrates that studying how the brain works can lead to better computational algorithms.

关键词： neural knowledge artificial

Neural Stem Cell Competition

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Neuroscience Bulletin 2024年第2期40卷 277-279页

作者： Yanan Li Jie He State Key Laboratory of Neuroscience Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai 200031China University of Chinese Academy of sciences Beijing 100049China

Cell competition is an adaptive surveillance mechanism to ensure the robustness of developmental outputs,maintain tissue integrity,and regulate tumor progression in multicellular organisms,which is highly conserved from invertebrates to vertebrates[1].At the cellular level,cell competition represents a non-cell-autonomous process in that cells can sense the fitness of their neighbors through cellular interactions,thereby eliminating low-fitness cells and expanding high-fitness counterparts,however,cells develop normally in homogenous populations.

关键词： thereby maintain eliminating

Novel heterozygous USH1C mutation impacts hair cell mechanotransduction and causes progressive hearing loss

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science Bulletin 2024年第2期69卷 167-172页

作者： Yanyan Jia Gaogan Jia Luo Guo Nan Song Yu-Meng Wang Luoying Jiang Yilai Shu Yan Chen Shujia Zhu Huawei Li Wenyan Li ENT Institute and Otorhinolaryngology Department of Eye&ENT Hospital State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceFudan UniversityShanghai 200031China Institutes of Biomedical sciences Fudan UniversityShanghai 200032China NHC Key Laboratory of Hearing Medicine(Fudan University) Shanghai 200031China The Institutes of brain science and the Collaborative Innovation center for brain science Fudan UniversityShanghai 200032China Shanghai center for brain science and brain-Inspired intelligence technology Shanghai 201210China Institute of Neuroscience State Key Laboratory of NeuroscienceCAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai 200031China Department of Cardiology and Institute for Developmental and Regenerative Cardiovascular MedicineXinhua Hospital Affiliated to Shanghai Jiaotong University School of MedicineShanghai 200092China

Hearing loss is one of the most common sensory disorders worldwide,affecting approximately 466 million people,including 34 million children[1].Genetic mutations accounts for approximately 60%of inherited hearing loss cases[2,3].These genetic changes result in a wide variety of clinical manifestations,ranging from nonsyndromic hearing loss(NSHL)to over 400 syndromes involving hearing loss and from mild adult-onset hearing loss to profound congenital deafness,covering the entire spectrum of Mendelian inheritance.

关键词： mec hearing approximate

End-to-End Paired Ambisonic-Binaural Audio Rendering

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 502-513页

作者： Yin Zhu Qiuqiang Kong Junjie Shi Shilei Liu Xuzhou Ye Ju-Chiang Wang Hongming Shan Junping Zhang Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Beijing ByteDance technology Co.Ltd. Shanghai 201102China. Chinese University of Hong Kong Hong KongChina IEEE Institute of science and technology for brain-Inspired intelligence and MOE Frontiers center for brain science Fudan UniversityShanghai 200433 Shanghai center for brain science and brain-Inspired technology Shanghai 200031China

Binaural rendering is of great interest to virtual reality and immersive media. Although humans can naturally use their two ears to perceive the spatial information contained in sounds, it is a challenging task for machines to achieve binaural rendering since the description of a sound field often requires multiple channels and even the metadata of the sound sources. In addition, the perceived sound varies from person to person even in the same sound field. Previous methods generally rely on individual-dependent head-related transferred function(HRTF)datasets and optimization algorithms that act on HRTFs. In practical applications, there are two major drawbacks to existing methods. The first is a high personalization cost, as traditional methods achieve personalized needs by measuring HRTFs. The second is insufficient accuracy because the optimization goal of traditional methods is to retain another part of information that is more important in perception at the cost of discarding a part of the information. Therefore, it is desirable to develop novel techniques to achieve personalization and accuracy at a low cost. To this end, we focus on the binaural rendering of ambisonic and propose 1) channel-shared encoder and channel-compared attention integrated into neural networks and 2) a loss function quantifying interaural level differences to deal with spatial information. To verify the proposed method, we collect and release the first paired ambisonic-binaural dataset and introduce three metrics to evaluate the content information and spatial information accuracy of the end-to-end methods. Extensive experimental results on the collected dataset demonstrate the superior performance of the proposed method and the shortcomings of previous methods.

关键词： Ambisonic attention binaural rendering neural network

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Multimodal Fusion of brain Imaging Data: Methods and Applications

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Machine intelligence Research 2024年第1期21卷 136-152页

作者： Na Luo Weiyang Shi Zhengyi Yang Ming Song Tianzi Jiang brainnetome center and National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial intelligence University of Chinese Academy of SciencesBeijing 100049China center for excellence in brain science and intelligence technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Research center for Augmented intelligence Zhejiang LaboratoryHangzhou 311100China

Neuroimaging data typically include multiple modalities,such as structural or functional magnetic resonance imaging,dif-fusion tensor imaging,and positron emission tomography,which provide multiple views for observing and analyzing the *** lever-age the complementary representations of different modalities,multimodal fusion is consequently needed to dig out both inter-modality and intra-modality *** the exploited rich information,it is becoming popular to combine multiple modality data to ex-plore the structural and functional characteristics of the brain in both health and disease *** this paper,we first review a wide spectrum of advanced machine learning methodologies for fusing multimodal brain imaging data,broadly categorized into unsupervised and supervised learning *** by this,some representative applications are discussed,including how they help to under-stand the brain arealization,how they improve the prediction of behavioral phenotypes and brain aging,and how they accelerate the biomarker exploration of brain ***,we discuss some exciting emerging trends and important future ***,we intend to offer a comprehensive overview of brain imaging fusion methods and their successful applications,along with the chal-lenges imposed by multi-scale and big data,which arises an urgent demand on developing new models and platforms.

关键词： Multimodal fusion supervised learning unsupervised learning brain atlas cognition brain disorders

Advancing neuroscience through real-time processing of big data:Transition from open-loop to closed-loop paradigms

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Zoological Research 2024年第3期45卷 518-519页

作者： Yu-Fan Wang Jiu-Lin Du Institute of Neuroscience State Key Laboratory of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai 200031China University of Chinese Academy of sciences Beijing 100049China School of Life science and technology ShanghaiTech UniversityShanghai 200031China

The brain functions as a closed-loop system that continuously generates behavior in response to the external environment and adjusts actions based on the *** research methodologies in neuroscience,especially those employed in brain imaging experiments,have mainly adopted an open-loop paradigm(Grosenick et al.,2015).Functional neural circuits are analyzed offline and subsequently tested through manipulation of neuronal activities within specific regions or with genetic *** establishing a closed-loop research paradigm,functional ensembles can be detected and tested in real time with temporal *** functional ensembles,rather than brain regions or genetically labeled neural populations,serve as fundamental units of neural networks,offering valuable insights into the dissection of neural *** closed-loop research paradigm also enables the capture of high-dimensional activities of internal brain dynamics and precise elucidation of physiological processes such as learning,decision-making,and sleep.

关键词： neural loop Transition

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