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arXiv 2024年

作者： Zheng, Tianyu Han, Liyuan Zhang, Tielin Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China Institute of Automation Chinese Academy of Sciences China

Spiking neural networks (SNNs) are becoming increasingly popular in both brain simulation and brain-inspired computation due to their biological plausibility and computational efficiency. This paper reviews the historical development of SNNs and highlights how these two fields are progressively and interactively converging. With the successful deployment of dynamic vision and audio sensors, SNN algorithms have proven crucial in subsequent information processing for various SNN-friendly applications. These include continuous visual signal tracking, automatic speech recognition, and reinforcement learning for continuous control. In these contexts, key features of SNNs, such as spike encoding, neuronal heterogeneity, function-based neural circuits, and learning-enhanced neural plasticity, are prominently demonstrated. In addition to these machine-learning applications, there is another biological paradigm: invasive brain-computer interfaces (BCIs). BCIs involve decoding natural spike trains directly from the brain, without the need for additional spike encoding as required in traditional machine-learning paradigms. Since natural spike information from the brain closely mirrors the external world, BCIs align well with SNNs, allowing them to leverage their advantages in energy efficiency, robustness, and flexibility. This synergy between BCIs and SNNs not only enhances our understanding of the brain but also accelerates advancements in brain-inspired intelligence technology. This bidirectional interaction fosters significant progress in both brain science and the development of brain-inspired algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Uncertainty-Aware Opponent Modeling for Deep Reinforcement Learning 25

Uncertainty-Aware Opponent Modeling for Deep Reinforcement L...

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Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems

作者： Likun Yang Pei Xu Shiyue Cao Yongjian Ren Xiaotang Chen Kaiqi Huang School of Artificial Intelligence University of Chinese Academy of Sciences & Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Institute of Automation Chinese Academy of Sciences & CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China

ISBN: (纸本)9798400714269

The ability to model opponent behavior is essential for autonomous decision-making in multi-agent games. Although stochastic behavior is universal in real-world situations, previous works have struggled to model opponents with high stochasticity, such as humans. The issue arises because stochasticity in opponent behavior introduces significant uncertainty into the opponent modeling process, which existing methods have not adequately addressed. We introduce a novel Uncertainty-Aware Opponent Modeling (UAOM ) method that addresses two key sources of uncertainty stemming from the inherent randomness of the opponent's actions. The first pertains to the uncertainty in constructing the opponent model, while the second concerns the uncertainty in applying the model during decision-making. For the first uncertainty, UAOM uses a hybrid behavior modeling module to learn a more powerful opponent-aware representation by ensembling the deterministic and probabilistic models to address both aleatoric and epistemic uncertainties in opponent modeling. For the second uncertainty, UAOM uses an opponent-aware dynamic modeling module to learn a dynamic-aware representation. We further provide a theoretical analysis showing that jointly optimizing our two modules can enhance downstream reinforcement learning performance while ensuring system convergence. We evaluate UAOM in both simulated settings and human-agent interaction scenarios. Our experimental results show that the proposed method significantly enhances performance when facing opponents with varying degrees of stochastic behavior, while efficiently managing the uncertainties introduced by such opponents.

关键词： deep reinforcement learning

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A Multi-modal Global Instance Tracking Benchmark (MGIT): Better Locating Target in Complex Spatio-temporal and Causal Relationship 37

A Multi-modal Global Instance Tracking Benchmark (MGIT): Bet...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Hu, Shiyu Zhang, Dailing Wu, Meiqi Feng, Xiaokun Li, Xuchen Zhao, Xin Huang, Kaiqi School of Artificial Intelligence University of Chinese Academy of Sciences China Institute of Automation Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China School of Computer Science Beijing University of Posts and Telecommunications China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China

ISBN: (纸本)9781713899921

Tracking an arbitrary moving target in a video sequence is the foundation for high-level tasks like video understanding. Although existing visual-based trackers have demonstrated good tracking capabilities in short video sequences, they always perform poorly in complex environments, as represented by the recently proposed global instance tracking task, which consists of longer videos with more complicated narrative content. Recently, several works have introduced natural language into object tracking, desiring to address the limitations of relying only on a single visual modality. However, these selected videos are still short sequences with uncomplicated spatio-temporal and causal relationships, and the provided semantic descriptions are too simple to characterize video content. To address these issues, we (1) first propose a new multi-modal global instance tracking benchmark named MGIT. It consists of 150 long video sequences with a total of 2.03 million frames, aiming to fully represent the complex spatio-temporal and causal relationships coupled in longer narrative content. (2) Each video sequence is annotated with three semantic grains (i.e., action, activity, and story) to model the progressive process of human cognition. We expect this multi-granular annotation strategy can provide a favorable environment for multi-modal object tracking research and long video understanding. (3) Besides, we execute comparative experiments on existing multi-modal object tracking benchmarks, which not only explore the impact of different annotation methods, but also validate that our annotation method is a feasible solution for coupling human understanding into semantic labels. (4) Additionally, we conduct detailed experimental analyses on MGIT, and hope the explored performance bottlenecks of existing algorithms can support further research in multi-modal object tracking. The proposed benchmark, experimental results, and toolkit will be released gradually on http://videocube.a

关键词： Semantics

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A Breakthrough in Understanding the Rapid Antidepressant Effect of Ketamine Based on Structural Analysis

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Neuroscience Bulletin 2022年第2期38卷 229-231页

作者： Hongmei Liu Xiaohui Wu Jun Chen Yiru Fang Clinical Research Center Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghai 200030China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China Shanghai Key Laboratory of Psychotic disorders Shanghai 201108China

Major depressive disorder(MDD)is one of the most common and serious mental *** overall 12-month prevalence rate is〜6%,while the lifetime risk increases about twofold(15%-18%),which means almost one in five people will experience at least one depressive episode during their lifetime[1].MDD is characterized by sad emotion accompanied by anhedonia,lack of energy or appetite,sleep disturbance,and low *** severe cases,suicidal ideation/attemp^ehavior or self-injurious behavior may occur,which seriously affects patients'daily life and social *** 2010,MDD was listed as the second greatest disease burden in the world,and is expected to rank first by 2030[2].

关键词： function lifetime listed

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Abnormal Prefrontal Neural Oscillations are Associated with Social Deficits in MECP2 Duplication Syndrome

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Neuroscience Bulletin 2022年第12期38卷 1598-1602页

作者： Xiao Li Yingnan Nie Qiyu Niu Xuanjun Guo Zilong Qiu Shouyan Wang Institute of Intelligent Robotics Academy for Engineering and TechnologyFudan UniversityShanghai 200433China Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai 200433China Center for Excellence in Brain Science and Intelligence Technology Institute of NeuroscienceState Key Laboratory of NeuroscienceCAS Key Laboratory of Primate NeurobiologyChinese Academy of SciencesShanghai 200031China National Clinical Research Center for Aging and Medicine Huashan HospitalFudan UniversityShanghai 200040China

DearEditor,Methyl-CpG-binding protein 2(MeCP2)participates in regulating the expression of the CREB,TDP-43 and Dnmtl genes[1,2].Recent studies have shown that MeCP2 is essential for maintaining normal neural functions in the mammalian *** of MeCP2causes MECP2 duplication syndrome[3],a type of autism spectrum disorder that results in a core phenotype of social deficits in human patients[4],nonhuman primates[5],and rodents[6].

关键词： MeCP2 MECP2 patients

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Extension of the Lifespan of a Mouse Model of Rett Syndrome by Intracerebroventricular Delivery of MECP2

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Neuroscience Bulletin 2023年第2期39卷 297-302页

作者： Kan Yang Cheng Cheng Yiting Yuan Yuefang Zhang Shifang Shan Zilong Qiu Center for Excellence in Brain Science and Intelligence Technology Institute of NeuroscienceState Key Laboratory of NeuroscienceCAS Key Laboratory of Primate NeurobiologyChinese Academy of SciencesShanghai 200031China Belief BioMed(Shanghai) Co.Ltd.Shanghai 200032China National Clinical Research Center for Aging and Medicine Huashan HospitalFudan UniversityShanghai 200040China

DearEditors,Rett Syndrome(RTT)is a severe neurodevelopmental disorder characterized by neural dysfunctions and a reduced lifespan,mainly in female patients,involving loss-of-function mutations in the methyl-CpG bindin... 详细信息

关键词： MECP2 Extension neural

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Gray Matter-Based Age Prediction Characterizes Different Regional Patterns

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Neuroscience Bulletin 2021年第1期37卷 94-98页

作者： Nianming Zuo Tianyu Hu Hao Liu Jing Sui Yong Liu Tianzi Jiang Brainnetome Center Institute of AutomationChinese Academy of Sciences(CAS)Beijing 100190China National Laboratory of Pattern Recognition Institute of AutomationCASBeijing 100190China University of CAS Beijing 100190China Chinese Institute for Brain Research Beijing 102206China Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationCASBeijing 100190China Key Laboratory for Neuroinformation of the Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu 625014China Queensland Brain Institute University of QueenslandBrisbaneQLD 4072Australia

Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional *** the span of adulthood,although the brain has matured from a neurobiological perspective,it is still continuously shaped by external factors such as habits,the family setting,socioeconomic status,and the work environment [1].In contrast to chronological age (CA),brain(or biological) age (BA) is conceptualized as an important index for characterizing the aging process and neuropsychological state,as well as individual *** evidence indicates that BA can be assessed by neuroimaging techniques,including MRI [2].

关键词： Gray Matter-Based Age Prediction Characterizes Different Regional Patterns

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GABAergic Retinal Ganglion Cells Projecting to the Superior Colliculus Mediate the Looming-Evoked Flight Response

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Neuroscience Bulletin 2024年第12期40卷 1886-1900页

作者： Man Yuan Gao Tan Danrui Cai Xue Luo Kejiong Shen Qinqin Deng Xinlan Lei Wen-Bo Zeng Min-Hua Luo Lu Huang Chaoran Ren Yin Shen Eye Center Renmin Hospital of Wuhan UniversityWuhan UniversityWuhan430060HubeiChina State Key Laboratory of Virology CAS Center for Excellence in Brain Science and Intelligence TechnologyWuhan Institute of VirologyWuhan430071China Department of Neurology and Stroke Center The First Affiliated Hospital of Jinan UniversityGuangzhou510632China Guangdong-Hongkong-Macao Institute of CNS Regeneration Ministry of Education CNS Regeneration Collaborative Joint LaboratoryJinan UniversityGuangzhou510632China Guangzhou Regenerative Medicine and Health Guangdong Laboratory Guangzhou510530China Frontier Science Center for Immunology and Metabolism Medical Research InstituteWuhan UniversityWuhan430071China

The looming stimulus-evoked flight response to approaching predators is a defensive behavior in most animals. However, how looming stimuli are detected in the retina and transmitted to the brain remains unclear. Here, we report that a group of GABAergic retinal ganglion cells (RGCs) projecting to the superior colliculus (SC) transmit looming signals from the retina to the brain, mediating the looming-evoked flight behavior by releasing GABA. GAD2-Cre and vGAT-Cre transgenic mice were used in combination with Cre-activated anterograde or retrograde tracer viruses to map the inputs to specific GABAergic RGC circuits. Optogenetic technology was used to assess the function of SC-projecting GABAergic RGCs (scpgRGCs) in the SC. FDIO-DTA (Flp-dependent Double-Floxed Inverted Open reading frame-Diphtheria toxin) combined with the FLP (Florfenicol, Lincomycin & Prednisolone) approach was used to ablate or silence scpgRGCs. In the mouse retina, GABAergic RGCs project to different brain areas, including the SC. ScpgRGCs are monosynaptically connected to parvalbumin-positive SC neurons known to be required for the looming-evoked flight response. Optogenetic activation of scpgRGCs triggers GABA-mediated inhibition in SC neurons. Ablation or silencing of scpgRGCs compromises looming-evoked flight responses without affecting image-forming functions. Our study reveals that scpgRGCs control the looming-evoked flight response by regulating SC neurons via GABA, providing novel insight into the regulation of innate defensive behaviors.

关键词： scpgRGCs Innate defensive behavior GABA Looming Superior colliculus Flight response

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Low-Complexity and Efficient Spike Sorting Algorithm Implemented on FPGA

Low-Complexity and Efficient Spike Sorting Algorithm Impleme...

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IEEE Biomedical Circuits and Systems (BIOcas)

作者： Linghui Kong Zhiwei Zhang Shan Yu Jingna Mao Chinese Academy of Sciences Institute of Automation Beijing China School of Future Technology University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

Next-generation neural interface systems will have an increasing number of channels and higher resolution to process signals from thousands of neurons. Online spike sorting is an effective method to ease the data bandwidth bottleneck. However, existing algorithms with high accuracy often have higher computational complexity in feature extraction. This paper presents a novel feature extraction method for spike sorting. The method is implemented on a Field Programmable Gate Array (FPGA) to verify its performance. The experimental results show that the proposed algorithm achieves high classification accuracy while compressing signals, without leading to a significant increase in computation or resource utilization.

关键词： Accuracy Neurons Signal processing algorithms Clustering algorithms Feature extraction Classification algorithms Resource management Computational complexity Field programmable gate arrays Sorting

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A Body Channel Communication Control Processor Utilizing Manchester Code

A Body Channel Communication Control Processor Utilizing Man...

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IEEE Biomedical Circuits and Systems (BIOcas)

作者： Chuer Lin Cheng Han Shan Yu Zhiwei Zhang Jingna Mao Institute of Automation Chinese Academy of Sciences Beijing China School of Future Technology University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

Compared with conventional communication technologies, body channel communication (BCC) shows more security and better energy efficiency by utilizing the human body as a transmission medium. This paper presents an integrated BCC control processor including a transmitter, a receiver, and a data processing unit. Manchester coding is used with the advantages of less low-frequency interference and better use of the characteristics of the human body channel. Instead of using a Field Programmable Gate Array (FPGA), data processing is integrated digitally to reduce the overall area and power consumption. The BCC control processor parameters can be configured flexibly via Serial Peripheral Interface (SPI), which is used more conveniently with other commercial chips. The proposed control processor has successfully implemented data transmission and processing with Manchester code and SPI protocol.

关键词： Codes Protocols Transmitters Process control Interference Receivers Data processing Transceivers Security Field programmable gate arrays

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