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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Honglin Hu, Peng Zhang, Changqing Li, Yunfan Peng, Xi College of Computer Science Sichuan University Chengdu China College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Lab of Machine Learning Tianjin China State Key Laboratory of Hydraulics and Mountain River Engineering Sichuan University Chengdu China

In the absence of class priors, recent deep clustering methods resort to data augmentation and pseudo-labeling strategies to generate supervision signals. Though achieved remarkable success, existing works struggle to discriminate hard samples at cluster boundaries, mining which is particularly challenging due to their unreliable cluster assignments. To break such a performance bottleneck, we propose incorporating user interaction to facilitate clustering instead of exhaustively mining semantics from the data itself. To be exact, we present Interactive Deep Clustering (IDC), a plug-and-play method designed to boost the performance of pre-trained clustering models with minimal interaction overhead. More specifically, IDC first quantitatively evaluates sample values based on hardness, representativeness, and diversity, where the representativeness avoids selecting outliers and the diversity prevents the selected samples from collapsing into a small number of clusters. IDC then queries the cluster affiliations of high-value samples in a user-friendly manner. Finally, it utilizes the user feedback to finetune the pre-trained clustering model. Extensive experiments demonstrate that IDC could remarkably improve the performance of various pre-trained clustering models, at the expense of low user interaction costs. The code could be accessed at ***. © 2024 Neural information processing systems foundation. All rights reserved.

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Self-Attentive Spatio-Temporal Calibration for Precise Intermediate Layer Matching in ANN-to-SNN Distillation

arXiv

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arXiv 2025年

作者： Hong, Di Wang, Yueming The College of Computer Science and Technology Zhejiang University China Nanhu Brain-computer Interface Institute Hangzhou China The State Key Laboratory of Brain-Machine Intelligence Zhejiang University China

Spiking Neural Networks (SNNs) are promising for low-power computation due to their event-driven mechanism but often suffer from lower accuracy compared to Artificial Neural Networks (ANNs). ANN-to-SNN knowledge distillation can improve SNN performance, but previous methods either focus solely on label information, missing valuable intermediate layer features, or use a layer-wise approach that neglects spatial and temporal semantic inconsistencies, leading to performance degradation. To address these limitations, we propose a novel method called self-attentive spatio-temporal calibration (SASTC). SASTC uses self-attention to identify semantically aligned layer pairs between ANN and SNN, both spatially and temporally. This enables the autonomous transfer of relevant semantic information. Extensive experiments show that SASTC outperforms existing methods, effectively solving the mismatching problem. Superior accuracy results include 95.12% on CIFAR-10, 79.40% on CIFAR-100 with 2 time steps, and 68.69% on ImageNet with 4 time steps for static datasets, and 97.92% on DVS-Gesture and 83.60% on DVS-CIFAR10 for neuromorphic datasets. This marks the first time SNNs have outperformed ANNs on both CIFAR-10 and CIFAR-100, shedding the new light on the potential applications of SNNs. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Self-Attentive Spatio-Temporal Calibration for Precise Intermediate Layer Matching in ANN-to-SNN Distillation 39

Self-Attentive Spatio-Temporal Calibration for Precise Inter...

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39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

ISBN: (纸本)157735897X

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ALADE-SNN: Adaptive Logit Alignment in Dynamically Expandable Spiking Neural Networks for Class Incremental Learning 39

ALADE-SNN: Adaptive Logit Alignment in Dynamically Expandabl...

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39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Ni, Wenyao Shen, Jiangrong Xu, Qi Tang, Huajin State Key Lab of Brain-Machine Intelligence Zhejiang University China College of Computer Science and Technology Zhejiang University China School of Computer Science and Technology Xi'an Jiaotong University China School of Computer Science and Technology Dalian University of Technology China

ISBN: (纸本)157735897X

Inspired by the human brain's ability to adapt to new tasks without erasing prior knowledge, we develop spiking neural networks (SNNs) with dynamic structures for Class Incremental Learning (CIL). Our comparative experiments reveal that limited datasets introduce biases in logits distributions among tasks. Fixed features from frozen past-task extractors can cause overfitting and hinder the learning of new tasks. To address these challenges, we propose the ALADE-SNN framework, which includes adaptive logit alignment for balanced feature representation and OtoN suppression to manage weights mapping frozen old features to new classes during training, releasing them during fine-tuning. This approach dynamically adjusts the network architecture based on analytical observations, improving feature extraction and balancing performance between new and old tasks. The experimental results show that ALADE-SNN achieves an average incremental accuracy of 75.42 ± 0.74% on the CIFAR100-B0 benchmark over 10 incremental steps. ALADE-SNN not only matches the performance of DNN-based methods but also surpasses state-of-the-art SNN-based continual learning algorithms. This advancement enhances continual learning in neuromorphic computing, offering a brain-inspired, energy-efficient solution for real-time data processing. Copyright © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

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Mind Artist: Creating Artistic Snapshots with Human Thought

Mind Artist: Creating Artistic Snapshots with Human Thought

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jiaxuan Chen Yu Qi Yueming Wang Gang Pan College of Computer Science and Technology Zhejiang University The State Key Lab of Brain-Machine Intelligence Zhejiang University MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Qiushi Academy for Advanced Studies Zhejiang University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce Mind Artist (MindArt), a novel and efficient neural decoding architecture to snap artistic photographs from our mind in a controllable manner. Recently, progress has been made in image reconstruction with non-invasive brain recordings, but it's still difficult to generate realistic images with high semantic fidelity due to the scarcity of data annotations. Unlike previous methods, this work casts the neural decoding into optimal transport (OT) and representation decoupling problems. Specifically, under discrete OT theory, we design a graph matching-guided neural representation learning framework to seek the underlying correspondences between conceptual semantics and neural signals, which yields a natural and meaningful self-supervisory task. Moreover, the proposed MindArt, structured with multiple stand-alone modal branches, enables the seamless incorporation of semantic representation into any visual style information, thus leaving it to have multi-modal reconstruction and training-free semantic editing capabilities. By doing so, the reconstructed images of MindArt have phenomenal realism both in terms of semantics and appearance. We compare our MindArt with leading alternatives, and achieve SOTA performance in different decoding tasks. Importantly, our approach can directly generate a series of stylized “mind snapshots” w/o extra optimizations, which may open up more potential applications. Code is available at https://***/JxuanC/MindArt.

关键词： Visualization Solid modeling Three-dimensional displays Computational modeling Semantics Linguistics Decoding

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Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks

arXiv

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arXiv 2023年

作者： Ma, Gehua Yan, Rui Tang, Huajin College of Computer Science and Technology Zhejiang University The State Key Lab of Brain-Machine Intelligence Hangzhou China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neural networks (SNNs), most studies are established on deterministic models, overlooking the inherent non-deterministic, noisy nature of neural computations. This study introduces the noisy spiking neural network (NSNN) and the noise-driven learning rule (NDL) by incorporating noisy neuronal dynamics to exploit the computational advantages of noisy neural processing. NSNN provides a theoretical framework that yields scalable, flexible, and reliable computation. We demonstrate that NSNN leads to spiking neural models with competitive performance, improved robustness against challenging perturbations than deterministic SNNs, and better reproducing probabilistic computation in neural coding. This study offers a powerful and easy-to-use tool for machine learning, neuromorphic intelligence practitioners, and computational neuroscience researchers. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamical systems

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A Blind Driving System Leveraging Multimodal Feedback

A Blind Driving System Leveraging Multimodal Feedback

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Smart World Congress (SWC), IEEE

作者： Ruizhe Zhang Sha Zhao Wei Yang Shijian Li Gang Pan the College of Computer Science and Technology Zhejiang University Hangzhou China the State Key Laboratory of Brain-machine Intelligence Zhejiang University Hangzhou China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Driving poses a significant challenge for blind and visually impaired (BVI) individuals, due to the loss of vision. Fortunately, the metaverse presents a promising platform where BVI individuals can experience independent driving just like sighted individuals. In order to assist such individuals in driving in the metaverse, we have devised a blind driving system that leverages multimodal feedback. We utilized stereo headphones and vibration motors to convey necessary information, including current and predicted vehicle states (i.e., speed, position, and direction) during driving, through sound, voice, and vibration feedback. Meanwhile, in order to improve the user-friendliness, we reduced the user’s information burden by data combination and compression. With the help of the multimodal feedback, BVI drivers could intuitively perceive the position and direction of the vehicle, thereby improving their driving experience. To evaluate the effectiveness of the system, we designed virtual scenarios to simulate a real driving environment, and we further devised two tasks: lane-keeping and lane-changing. We conducted user studies where sighted individuals wearing blindfolds were involved in the two tasks. The results suggest that our system demonstrates a beneficial effect in assisting BVI individuals in driving. Users could keep the vehicle in the center area of the lane for over ninety percent of driving time, and completed a lane-changing task with a high success rate. This study provides a potential solution for blind driving for BVI people by employing multimodal feedback.

关键词： Vibrations Headphones Metaverse Tactile sensors Motors Vehicles

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Advancing Logic in AI: Insights from the LNGAI 2021 Workshop

Advancing Logic in AI: Insights from the LNGAI 2021 Workshop

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作者： Liao, Beishui Liu, Fenrong van der Torre, Leendert Zhou, Yi School of Philosophy Zhejiang University Hangzhou310058 China The State Key Lab of Brain-Machine Intelligence Hangzhou310058 China Department of Philosophy Tsinghua University Beijing100084 China Department of Computer Science University of Luxembourg Esch-sur-AlzetteL-4365 Luxembourg School of Information Science and Technology University of Science and Technology of China HeFei230026 China

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Decoding Respiratory Signals from LFP in Olfactory Bulb based on ConvLSTM Framework 7

Decoding Respiratory Signals from LFP in Olfactory Bulb base...

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7th International Conference on Biological Information and Biomedical Engineering, BIBE 2024

作者： Xu, Haoze Yu, Bowen Yu, Chaonan Xu, Kedi Qiushi Academy for Advanced Studies Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Key Laboratory for Biomedical Engineering of Ministry of Education School of Biomedical Engineering and Instrument Science Zhejiang University China Nanhu Brain-computer interface institute Hangzhou311100 China State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China

ISBN: (纸本)9783800762910

Respiratory signals are crucial for decoding olfactory neural circuits and understanding respiration-entrained local field potential (LFP) rhythms. Acquiring high-precision respiratory signals often requires additional equipment, increasing the experimental burden, particularly in free-moving paradigms. However, within the current neuroscience paradigm, we have not yet found a non-invasive method for obtaining the respiratory signals of laboratory animals. To minimize surgical trauma to experimental animals, this paper introduces a ConvLSTM-based decoding method, which uses the LFP from the olfactory bulb (OB) region of animals to accurately decode respiratory signals. After validating the effectiveness of the ConvLSTM method through comparative experiments, we further investigated the factors influencing the model's decoding performance. The Gamma frequency band exhibited the best decoding results, with a mean absolute error (MAE) of 0.16±0.09, which may be related to the causal direction from respiratory signals to Gamma rhythms. Additionally, the ConvLSTM model demonstrated strong trend prediction capabilities in multi-sequence point predictions and forward predictions, despite a slight decline in single-point prediction accuracy. Extensive validation on publicly available datasets indicates that the ConvLSTM-based respiratory decoding approach is highly applicable, offering a non-invasive alternative for respiratory signal acquisition in animal studies. © VDE VERLAG GMBH.

关键词： Noninvasive medical procedures

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ALADE-SNN: Adaptive Logit Alignment in Dynamically Expandable Spiking Neural Networks for Class Incremental Learning

arXiv

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arXiv 2024年

作者： Ni, Wenyao Shen, Jiangrong Xu, Qi Tang, Huajin State Key Lab of Brain-Machine Intelligence Zhejiang University China College of Computer Science and Technology Zhejiang University China School of Computer Science and Technology Xi’an Jiaotong University China School of Computer Science and Technology Dalian University of Technology China

Inspired by the human brain’s ability to adapt to new tasks without erasing prior knowledge, we develop spiking neural networks (SNNs) with dynamic structures for Class Incremental Learning (CIL). Our comparative experiments reveal that limited datasets introduce biases in logits distributions among tasks. Fixed features from frozen past-task extractors can cause overfitting and hinder the learning of new tasks. To address these challenges, we propose the ALADE-SNN framework, which includes adaptive logit alignment for balanced feature representation and OtoN suppression to manage weights mapping frozen old features to new classes during training, releasing them during fine-tuning. This approach dynamically adjusts the network architecture based on analytical observations, improving feature extraction and balancing performance between new and old tasks. Experiment results show that ALADE-SNN achieves an average incremental accuracy of 75.42 ± 0.74% on the CIFAR100-B0 benchmark over 10 incremental steps. ALADE-SNN not only matches the performance of DNN-based methods but also surpasses state-of-the-art SNN-based continual learning algorithms. This advancement enhances continual learning in neuromorphic computing, offering a brain-inspired, energy-efficient solution for real-time data processing. © 2024, CC BY-NC-ND.

关键词： Contrastive Learning

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