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A Graded Speed Control Method for Cyborg Rats Based on Electrical Stimulation of the Cuneiform Nucleus

Journal of Bionic Engineering 2024年第3期21卷 1334-1346页

作者： Hanyi Ling Le Han Nenggan Zheng Qiushi Academy for Advanced Studies(QAAS) Zhejiang UniversityHangzhou310007China The State Key Lab of Brain-Machine Intelligence Zhejiang UniversityHangzhou310007China College of Biomedical Engineering&Instrument Science Zhejiang UniversityHangzhou310007China College of Computer Science and Technology Zhejiang UniversityHangzhou310007China CCAI by MOE and Zhejiang Provincial Government(ZJU) Hangzhou310007China

The precise movement speed regulation is a key factor to improve the control effect and efficiency of the cyborg ***,the current stimulation techniques cannot realize the graded control of the *** this study,we achieved the multi-level speed regulation of cyborg rats in the large open field and treadmill by specifically targeting the Cuneiform Nucleus(CnF)of the Mesencephalic Locomotor Region(MLR).Detailed,we measured the influence of each stimulation parameter on the speed control process which included the real-time speed,accelerated speed,response time,and acceleration *** concluded that the pulse period and the pulse width were the main determinants influencing the accelerated speed of cyborg *** the amplitude of stimulation was found to affect the response time exhibited by the cyborg *** study provides valuable insights into the regulation of rat locomotion speed and highlights the potential for utilizing this approach in various experimental settings.

关键词： Cyborg rats Cuneiform nucleus Graded control Speed regulation

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DRIP: Unleashing Diffusion Priors for Joint Foreground and Alpha Prediction in Image Matting 38

DRIP: Unleashing Diffusion Priors for Joint Foreground and A...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Xiaodi Yang, Zongxin Quan, Ruijie Yang, Yi The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China CCAI College of Computer Science and Technology Zhejiang University Hangzhou China DBMI HMS Harvard University Boston United States

Recovering the foreground color and opacity/alpha matte from a single image (i.e., image matting) is a challenging and ill-posed problem where data priors play a critical role in achieving precise results. Due to the limited matting datasets, traditional methods usually struggle to produce high-quality *** address this, we explore the potential of leveraging vision priors embedded in pre-trained latent diffusion models (LDM) for estimating foreground RGBA values in challenging scenarios and rare objects. We introduce Drip, a novel approach for image matting that harnesses the rich prior knowledge of LDM models. Our method incorporates a switcher and a cross-domain attention mechanism to extend the original LDM for joint prediction of the foreground color and opacity. This setup facilitates mutual information exchange and ensures high consistency across both modalities. To mitigate the inherent reconstruction errors of the LDM's VAE decoder, we propose a latent transparency decoder to align the RGBA prediction with the input image, thereby reducing discrepancies. Comprehensive experimental results demonstrate that our approach achieves state-of-the-art performance in foreground and alpha predictions and shows remarkable generalizability across various benchmarks. © 2024 Neural information processing systems foundation. All rights reserved.

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Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation 41

Safe Reinforcement Learning using Finite-Horizon Gradient-ba...

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41st International Conference on machine Learning, ICML 2024

作者： Dai, Juntao Yang, Yaodong Zheng, Qian Pan, Gang College of Computer Science and Technology Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Center for AI Safety and Governance Peking University Beijing China

A key aspect of Safe Reinforcement Learning (Safe RL) involves estimating the constraint condition for the next policy, which is crucial for guiding the optimization of safe policy updates. However, the existing Advantage-based Estimation (ABE) method relies on the infinite-horizon discounted advantage function. This dependence leads to catastrophic errors in finite-horizon scenarios with non-discounted constraints, resulting in safety-violation updates. In response, we propose the first estimation method for finite-horizon non-discounted constraints in deep Safe RL, termed Gradient-based Estimation (GBE), which relies on the analytic gradient derived along trajectories. Our theoretical and empirical analyses demonstrate that GBE can effectively estimate constraint changes over a finite horizon. Constructing a surrogate optimization problem with GBE, we developed a novel Safe RL algorithm called Constrained Gradient-based Policy Optimization (CGPO). CGPO identifies feasible optimal policies by iteratively resolving sub-problems within trust regions. Our empirical results reveal that CGPO, unlike baseline algorithms, successfully estimates the constraint functions of subsequent policies, thereby ensuring the efficiency and feasibility of each update. Copyright 2024 by the author(s)

关键词： Deep reinforcement learning

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FEEL-SNN: robust spiking neural networks with frequency encoding and evolutionary leak factor 24

FEEL-SNN: robust spiking neural networks with frequency enco...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Mengting Xu De Ma HuaJin Tang Qian Zheng Gang Pan The State Key Lab of Brain-Machine Intelligence and College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798331314385

Currently, researchers think that the inherent robustness of spiking neural networks (SNNs) stems from their biologically plausible spiking neurons, and are dedicated to developing more bio-inspired models to defend attacks. However, most work relies solely on experimental analysis and lacks theoretical support, and the direct-encoding method and fixed membrane potential leak factor they used in spiking neurons are simplified simulations of those in the biological nervous system, which makes it difficult to ensure generalizability across all datasets and networks. Contrarily, the biological nervous system can stay reliable even in a highly complex noise environment, one of the reasons is selective visual attention and non-fixed membrane potential leaks in biological neurons. This biological finding has inspired us to design a highly robust SNN model that closely mimics the biological nervous system. In our study, we first present a unified theoretical framework for SNN robustness constraint, which suggests that improving the encoding method and evolution of the membrane potential leak factor in spiking neurons can improve SNN robustness. Subsequently, we propose a robust SNN (FEEL-SNN) with Frequency Encoding (FE) and Evolutionary Leak factor (EL) to defend against different noises, mimicking the selective visual attention mechanism and non-fixed leak observed in biological systems. Experimental results confirm the efficacy of both our FE, EL, and FEEL methods, either in isolation or in conjunction with established robust enhancement algorithms, for enhancing the robustness of SNNs. Our code is available at https://***/zju-bmi-lab/FEEL_SNN.

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Make It Easy: Action Quality Assessment of Cyborg Animals Based on Spatial-Temporal Pose Inference 10

Make It Easy: Action Quality Assessment of Cyborg Animals Ba...

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10th IEEE Smart World Congress, SWC 2024

作者： Li, Qiqi Han, Le Wang, Pengfei Zheng, Nenggan Zhejiang University Qiushi Academy for Advanced Studies College of Computer Science and Technology Hangzhou China Zhejiang University School of Software Technology Hangzhou China Zhejiang University Qiushi Academy for Advanced Studies The State Key Lab of Brain-Machine Intelligence College of Computer Science and Technology Hangzhou China Bengbu University Bengbu China

ISBN: (纸本)9798331520861

Assessing the action quality of cyborg animals helps to adjust control strategies, guide the development of control algorithm, and enhance the efficiency of navigation and military applications. However, existing research is limited in control algorithm and ignores the assessment of movement smoothness. In this paper, we first propose EASY, an effective assessment framework, which is able to (i) evaluate the action quality of cyborg animals of various sizes and motion patterns;(ii) obtain assessment results directly based on keypoints. Specifically, EASY first captures keypoints sequences through a small object pose estimation module. It employs the convolutional block attention module (CBAM) and depthwise over parameterized convolution (DOConv), allowing more accurate feature recognition with fewer parameters. Then, EASY employs multi-field fusion strategy that merges commonalities and differences, enabling a comprehensive and accurate assessment of fluency. We tested EASY on both CyborgRat-MFA dataset and CyborgBee-MFA dataset. Extensive experiments demonstrate that the assessment results are consistent with human expert scores, which prove that our proposed EASY is reasonable, effective, and interpretable. © 2024 IEEE.

关键词： action quality assessment cyborg animals electrical stimulation control motion fluency

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An Experimental Survey of Missing Data Imputation Algorithms (Extended Abstract) 40

An Experimental Survey of Missing Data Imputation Algorithms...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Miao, Xiaoye Wu, Yangyang Chen, Lu Gao, Yunjun Yin, Jianwei Center for Data Science Zhejiang University Hangzhou China Zhejiang University The State Key Lab of Brain-Machine Intelligence Hangzhou China Software College Zhejiang University Ningbo China College of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9798350317152

Due to the ubiquity of missing data, data imputation has received extensive attention in the past decades. It is a well-recognized problem impacting almost all fields of scientific study. Existing imputation algorithms differ in problem settings, model selection, and data evaluation. There is a lack of systematic comparison study among imputation algorithms. In this paper, we survey this interesting and evolving research topic by broadly reviewing and experimentally comparing the state-of-the-art missing data imputation algorithms. We analyze and categorize 19 imputation algorithms. Extensive experiments over 15 real-world benchmark datasets are conducted under various settings of data types, missing mechanisms, missing rates, dataset parameters, as well as the post-imputation prediction task. We shed light on a series of constructive insights on imputation algorithms to tackle missing data problem in real-life scenarios. Moreover, we put forward promising future directions for data imputation. © 2024 IEEE.

关键词： Deep learning

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Learning Inverse Dynamics of Multi-segmented Larval Crawling Neuromechanics 10

Learning Inverse Dynamics of Multi-segmented Larval Crawling...

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10th IEEE Smart World Congress, SWC 2024

作者： Tang, Weihao Zhang, Hao Gong, Zhefeng Zheng, Nenggan Zhejiang University Qiushi Academy for Advanced Studies College of Computer Science and Technology Hangzhou China Zhejiang University School of Medicine School of Brain Science and Brain Medicine Department of Neurology of the Fourth Hospital Hangzhou310058 China Zhejiang University Qiushi Academy for Advanced Studies The State Key Lab of Brain-Machine Intelligence College of Computer Science and Technology Hangzhou China Bengbu University Bengbu China

ISBN: (纸本)9798331520861

Neuromechanical modeling that bridges neural activity and locomotion patterns is essential for studying locomotion control. The Drosophila larva, with its diverse softbodied locomotion, serves as an ideal subject for biomimetic research. Constructing a neuromechanical model of Drosophila larvae presents two primary challenges: efficiently simulating the larva's soft, multi-segmented body and controlling locomotion within a large action space by actuating muscle groups. This work presents a three-dimensional neuromechanical model of the larva and introduces an inverse dynamics learning method for locomotion control. Our approach efficiently simulates the larva's multi-segmented, deformable body by integrating computational modules for soft tissues, muscles, and friction. We employ a parameterized controller to reproduce peristaltic waves and use a hybrid artificial neural network architecture to learn inverse dynamics from partially observed states. Balancing biological interpretability with modeling complexity, this model advances the understanding of brain-body-environment interactions in Drosophila larvae, contributing to nature-inspired intelligent systems research. © 2024 IEEE.

关键词： Intelligent systems

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Online Query-Based Data Pricing with Time-Discounting Valuations 40

Online Query-Based Data Pricing with Time-Discounting Valuat...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Fu, Yicheng Miao, Xiaoye Peng, Huanhuan Na, Chongning Deng, Shuiguang Yin, Jianwei Zhejiang University Center for Data Science Hangzhou China Zhejiang University The State Key Lab of Brain-Machine Intelligence Hangzhou China PinTech Research Center Zhejiang Lab Hangzhou China College of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9798350317152

Online data marketplaces emerge in diverse data-driven applications, where dynamically arriving consumers pur-chase the data at posted prices. The data value decays over time in many tasks, such as machine learning predictions and realtime systems. Existing query pricing methods do not consider the time-discounting data value. In this paper, we study the query feature-based data pricing problem with unknown time-discounting data valuation. We propose an effective online data pricing mechanism Pride to maximize the cumulative sales revenue. It leverages the powerful property of the ellipsoid method to efficiently solve online optimization via exploration and exploitation. Based on Thompson sampling, we present a novel non-stationary MAB algorithm Biased-TS to determine a suitable discount factor and attain the dynamic posted price. It is theoretically proved that, the regret upper bound order of Pride is dominated by the discretization error O(Tk), where K and T are the numbers of discount candidates and total trading rounds, respectively. Biased-TS gets a sub-linear regret upper bound O(K3√TlnT+Kexp{4√ln T}). Extensive experiments using both synthetic and real datasets demonstrate that Pride yields around 90% of the optimal cumulative revenue, and it substantially outperforms the state-of-the-art methods. © 2024 IEEE.

关键词： Costs

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Editorial IEEE Transactions on Cognitive and Developmental Systems

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IEEE Transactions on Cognitive and Developmental Systems 2024年第1期16卷 3-3页

作者： Tang, Huajin Zhejiang University College of Computer Science and Technology The State Key Laboratory of Brain-Machine Intelligence MOE Frontier Science Center for Brain Science and Brain-Machine Integration Hangzhou310027 China

As we usher into the new year of 2024, in my capacity as the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems (TCDS), I am happy to extend to you a tapestry of New Year greetings, may th...

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Droflysim: A Collective Behavior Analysis and Evaluation Tool 10

Droflysim: A Collective Behavior Analysis and Evaluation Too...

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10th IEEE Smart World Congress, SWC 2024

作者： Zhu, Rui Zeng, Yudian Yang, Chuying Wang, Bangchuan Zheng, Nenggan Zhejiang University Qiushi Academy for Advanced Studies College of Computer Science and Technology Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China Zhejiang University Qiushi Academy for Advanced Studies School of Software Technology Hangzhou China Zhejiang University Qiushi Academy for Advanced Studies The State Key Lab of Brain-Machine Intelligence College of Computer Science and Technology Hangzhou China Bengbu University Bengbu China

ISBN: (纸本)9798331520861

The collective behaviors of various organisms exhibit complex patterns and statistical characteristics, especially evident within large populations. The complexity of these behaviors necessitates in-depth analysis of extensive trajectory data to reveal underlying dynamics and patterns. Current methods fall short in quantitative analysis and multi-angle assessment of collective behaviors, particularly in terms of the limitations of macroscopic indicators and the singularity of evaluation methods. This study introduces Droflysim, an efficient computational platform for analyzing and assessing large-scale trajectory data of collective behavior. Droflysim enhances data processing speed through parallel processing technology and integrates a variety of simulation models and multi-angle evaluation methods, overcoming the limitations of traditional tools. Notably, Droflysim emphasizes the statistical analysis of trajectories under environmental disturbances, providing a new perspective and deeper understanding of the complexity of collective behavior by establishing a trajectory fitting model. The design of Droflysim includes an intuitive interactive interface, modular architecture, and parallel processing capabilities, enhancing the efficiency and scalability of data analysis. By quantitatively analyzing trajectories, Droflysim lays the foundation for precise model calibration and authentic reproduction of behavior, offering a powerful tool for the study of collective behavior. © 2024 IEEE.

关键词： Collective Behavior Analysis Computational Modeling Environmental Disturbances Statistical Characteristics Trajectory Data Visualization

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