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Gang Pan

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58

AAAI Conference 2026 Conference Paper

EMOD: A Unified EEG Emotion Representation Framework Leveraging V-A Guided Contrastive Learning

  • Yuning Chen
  • Sha Zhao
  • Shijian Li
  • Gang Pan

Emotion recognition from EEG signals is essential for affective computing and has been widely explored using deep learning. While recent deep learning approaches have achieved strong performance on single EEG emotion datasets, their generalization across datasets remains limited due to the heterogeneity in annotation schemes and data formats. Existing models typically require dataset-specific architectures tailored to input structure and lack semantic alignment across diverse emotion labels. To address these challenges, we propose EMOD: A Unified EEG Emotion Representation Framework Leveraging Valence–Arousal (V–A) Guided Contrastive Learning. EMOD learns transferable and emotion-aware representations from heterogeneous datasets by bridging both semantic and structural gaps. Specifically, we project discrete and continuous emotion labels into a unified V–A space and formulate a soft-weighted supervised contrastive loss that encourages emotionally similar samples to cluster in the latent space. To accommodate variable EEG formats, EMOD employs a flexible backbone comprising a Triple-Domain Encoder followed by a Spatial-Temporal Transformer, enabling robust extraction and integration of temporal, spectral, and spatial features. We pretrain EMOD on 8 public EEG datasets and evaluate its performance on three benchmark datasets. Experimental results show that EMOD achieves the state-of-the-art performance, demonstrating strong adaptability and generalization across diverse EEG-based emotion recognition scenarios.

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AAAI Conference 2026 Conference Paper

S³: Spiking Neurons as an Isolating Segmenter for Brain Signal Decoding

  • Qian Zheng
  • Ming Chen
  • Sha Zhao
  • Shi Gu
  • Peng Lin
  • De Ma
  • Huajin Tang
  • Gang Pan

Recent brain decoding studies have primarily emphasized the development of brain decoders, while largely neglecting the segmentation step. Existing methods typically adopt fixed-length segmentation, which might overlook subject- or task-level variability and disrupt temporal patterns within brain signals. To address this gap, we propose S3, which leverages spiking neurons as an isolating segmenter for brain signal decoding. S3 segments brain signals adaptively, considering subject- and task-level variability while preserving intrinsic temporal patterns of brain signals. It exploits the unique reset mechanism of spiking neurons to isolate previous irrelevant temporal patterns during the generation of each segmentation point. To optimize S3 for enhancing task performance in the absence of segmentation labels, we develop an optimization method where segmentation pseudo-labels are created with a stochastic-greedy algorithm to optimize them, while circumventing gradient blockade between S3 and task performance. Experiments on 10 downstream tasks across 13 public datasets demonstrate that S3 consistently outperforms existing methods, validating its effectiveness, generalizability and interpretability.

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AAMAS Conference 2025 Conference Paper

Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors

  • Lang Feng
  • Jiahao Lin
  • Dong Xing
  • Li Zhang
  • De Ma
  • Gang Pan

Population-population generalization is a challenging problem in multi-agent reinforcement learning (MARL), particularly when agents encounter unseen co-players. However, existing self-playbased methods are constrained by the limitation of inside-space generalization. In this study, we propose Bidirectional Distillation (BiDist), a novel mixed-play framework, to overcome this limitation in MARL. BiDist leverages knowledge distillation in two alternating directions: forward distillation, which emulates the historical policies’ space and creates an implicit self-play, and reverse distillation, which systematically drives agents towards novel distributions outside the known policy space in a non-self-play manner. Our results highlight its remarkable generalization ability across a variety of cooperative, competitive, and social dilemma tasks, and reveal that BiDist significantly diversifies the policy distribution space.

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IJCAI Conference 2025 Conference Paper

EDyGS: Event Enhanced Dynamic 3D Radiance Fields from Blurry Monocular Video

  • Mengxu Lu
  • Zehao Chen
  • Yan Liu
  • De Ma
  • Huajin Tang
  • Qian Zheng
  • Gang Pan

The task of generating novel views in dynamic scenes plays a critical role in the 3D vision domain. Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS) have shown great promise in this domain but struggle with motion blur, which often arises in real-world scenarios due to camera or object motion. Existing methods address camera motion blur but fall short in dynamic scenes, where the coupling of camera and object motion complicates multi-view consistency and temporal coherence. In this work, we propose EDyGS, a model designed to reconstruct sharp novel views from event streams and monocular videos of dynamic scenes with motion blur. Our approach introduces a motion-mask 3D Gaussian model that assigns each Gaussian an additional attribute to distinguish between static and dynamic regions. By leveraging this motion mask field, we separate and optimize the static and dynamic regions independently. A progressive learning strategy is adopted, where static regions are reconstructed by jointly optimizing camera poses and learnable 3D Gaussians, while dynamic regions are modeled using an implicit deformation field alongside learnable 3D Gaussians. We conduct both quantitative and qualitative experiments on synthetic and real-world data. Experimental results demonstrate that EDyGS effectively handles blurry inputs in dynamic scenes.

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AAAI Conference 2025 Conference Paper

EvHDR-GS: Event-guided HDR Video Reconstruction with 3D Gaussian Splatting

  • Zehao Chen
  • Zhan Lu
  • De Ma
  • Huajin Tang
  • Xudong Jiang
  • Qian Zheng
  • Gang Pan

High Dynamic Range (HDR) video reconstruction seeks to accurately restore the extensive dynamic range present in real-world scenes and is widely employed in downstream applications. Existing methods typically operate on one or a small number of consecutive frames, which often leads to inconsistent brightness across the video due to their limited perspective on the video sequence. Moreover, supervised learning-based approaches are susceptible to data bias, resulting in reduced effectiveness when confronted with test inputs exhibiting a domain gap relative to the training data. To address these limitations, we present an event-guided HDR video reconstruction method through building 3D Gaussian Splatting (3DGS), to ensure consistent brightness imposed by 3D consistency. We introduce HDR 3D Gaussians capable of simultaneously representing HDR and low-dynamic-range (LDR) colors. Furthermore, we incorporate a learnable HDR-to-LDR transformation optimized by input event streams and LDR frames to eliminate the data bias. Experimental results on both synthetic and real-world datasets demonstrate that the proposed method achieves state-of-the-art performance.

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AAAI Conference 2025 Conference Paper

EvHDR-NeRF: Building High Dynamic Range Radiance Fields with Single Exposure Images and Events

  • Zehao Chen
  • Zhanfeng Liao
  • De Ma
  • Huajin Tang
  • Qian Zheng
  • Gang Pan

We present EvHDR-NeRF to recover a High Dynamic Range (HDR) radiance field from event streams and a set of Low Dynamic Range (LDR) views with single exposures. Using the EvHDR-NeRF, we can generate both novel HDR views and novel LDR views under different exposures. The key to our method is to model the new relationship between events streams and LDR images, which considers both the Camera Response Function (CRF) and exposure time. Based on this relationship, we categorize events into inter-frame events and intra-exposure. The former is utilized for building HDR radiance field and the latter is used to deblur potentially blurred images. Compared to existing methods, this method can effectively reconstruct the HDR radiance field even when the input images are degraded. Experimental results demonstrate that our method achieves state-of-the-art HDR reconstruction, providing a more adaptable and accurate solution for complex imaging applications.

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JBHI Journal 2025 Journal Article

EvoMoE: Evolutionary Mixture-of-Experts for SSVEP-EEG Classification With User-Independent Training

  • Xiaoli Yang
  • Yurui Li
  • Jianyu Zhang
  • Huiyuan Tian
  • Shijian Li
  • Gang Pan

The analysis of EEG data in BCI systems captures unique individual characteristics, presenting diverse patterns that deviate from conventional identical distribution assumptions. Therefore, applying AI models directly to brain data becomes challenging due to the non-identical distribution issue. Meanwhile, as user numbers in BCI systems rise, scalable models are crucial to handle the growing data volume. Moreover, the limited availability of individual data necessitates the use of collective data for training, requiring models with strong generalization capabilities. To address these challenges, we propose Evolutionary Mixture of Experts (EvoMoE), a framework leveraging a set of diverse experts to model data from individuals. Users with similar distributions are grouped together, allowing experts to handle EEG data with different distribution types. The gating network of EvoMoE selects experts that closely match the distribution of the current sample, effectively tackling non-identical distribution issues. When encountering an unrecognized distribution, a new expert is introduced to accommodate the new data pattern, ensuring model adaptability. Evaluations on two 40-category BCI Speller datasets demonstrate significant performance improvements over state-of-the-art methods. On the BETA dataset, our online EvoMoE achieves 13. 06% increase in accuracy and a 27. 24-point increase in high information transfer rate (ITR) compared to the online UI method. The Bench dataset shows 3. 64% increase in accuracy and a 10. 42-point increase in ITR. These qualities make it a promising solution for practical BCI implementation, while setting the stage for the development of comprehensive biological big models.

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AAAI Conference 2025 Conference Paper

EvSTVSR: Event Guided Space-Time Video Super-Resolution

  • Haojie Yan
  • Zhan Lu
  • Zehao Chen
  • De Ma
  • Huajin Tang
  • Qian Zheng
  • Gang Pan

In the domain of space-time video super-resolution, it is typically challenging to handle complex motions (including large and nonlinear motions) and varying illumination scenes due to the lack of inter-frame information. Leveraging the dense temporal information provided by event signals offers a promising solution. Traditional event-based methods typically rely on multiple images, using motion estimation and compensation, which can introduce errors. Accumulated errors from multiple frames often lead to artifacts and blurriness in the output. To mitigate these issues, we propose EvSTVSR, a method that uses fewer adjacent frames and integrates dense temporal information from events to guide alignment. Additionally, we introduce a coordinate-based feature fusion upsampling module to achieve spatial super-resolution. Experimental results demonstrate that our method not only outperforms existing RGB-based approaches but also excels in handling large motion scenarios.

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NeurIPS Conference 2025 Conference Paper

Neural-Driven Image Editing

  • Pengfei Zhou
  • Jie Xia
  • Xiaopeng Peng
  • Wangbo Zhao
  • Zilong Ye
  • Zekai Li
  • Suorong Yang
  • Jiadong Pan

Traditional image editing typically relies on manual prompting, making it labor-intensive and inaccessible to individuals with limited motor control or language abilities. Leveraging recent advances in brain-computer interfaces (BCIs) and generative models, we propose LoongX, a hands-free image editing approach driven by multimodal neurophysiological signals. LoongX utilizes state-of-the-art diffusion models trained on a comprehensive dataset of 23, 928 image editing pairs, each paired with synchronized electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), photoplethysmography (PPG), and head motion signals that capture user intent. To effectively address the heterogeneity of these signals, LoongX integrates two key modules. The cross-scale state space (CS3) module encodes informative modality-specific features. The dynamic gated fusion (DGF) module further aggregates these features into a unified latent space, which is then aligned with edit semantics via fine-tuning on a diffusion transformer (DiT). Additionally, we pre-train the encoders using contrastive learning to align cognitive states with semantic intentions from embedded natural language. Extensive experiments demonstrate that LoongX achieves performance comparable to text-driven methods (CLIP-I: 0. 6605 vs. 0. 6558; DINO: 0. 4812 vs. 0. 4637) and outperforms them when neural signals are combined with speech (CLIP-T: 0. 2588 vs. 0. 2549). These results highlight the promise of neural-driven generative models in enabling accessible, intuitive image editing and open new directions for cognitive-driven creative technologies. The code and dataset are released on the project website: https: //loongx1. github. io.

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AAAI Conference 2025 Conference Paper

Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation

  • Yangxuan Zhou
  • Sha Zhao
  • Jiquan Wang
  • Haiteng Jiang
  • Shijian Li
  • Benyan Luo
  • Tao Li
  • Gang Pan

Sleep staging is important for monitoring sleep quality and diagnosing sleep-related disorders. Recently, numerous deep learning-based models have been proposed for automatic sleep staging using polysomnography recordings. Most of them are trained and tested on the same labeled datasets which results in poor generalization to unseen target domains. However, they regard the subjects in the target domains as a whole and overlook the individual discrepancies, which limits the model's generalization ability to new patients (i.e., unseen subjects) and plug-and-play applicability in clinics. To address this, we propose a novel Source-Free Unsupervised Individual Domain Adaptation (SF-UIDA) framework for sleep staging, leveraging sequential cross-view contrasting and pseudo-label based fine-tuning. It is actually a two-step subject-specific adaptation scheme, which enables the source model to effectively adapt to newly appeared unlabeled individual without access to the source data. It meets the practical needs in real-world scenarios, where the personalized customization can be plug-and-play applied to new ones. Our framework is applied to three classic sleep staging models and evaluated on three public sleep datasets, achieving the state-of-the-art performance.

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IS Journal 2025 Journal Article

SNNL: A Programming Language for SNN Development

  • Qinghui Xing
  • Zirun Li
  • Ying Li
  • Schahram Dustdar
  • Xin Du
  • Gang Pan
  • Shuiguang Deng

Spiking Neural Networks (SNNs) are gaining attention for biological plausibility and energy efficiency. Advances in neuromorphic systems—integrating hardware and software tools—accelerate SNN implementation. Yet, deploying SNNs on such platforms remains challenging due to model complexity and system heterogeneity, requiring flexible frameworks. Existing tools (e. g. , PyNN, Brian2) show limited expressiveness for neuromorphic applications or poor cross-platform support. This paper proposes SNNL, a flexible domain-specific language for SNN development and deployment on neuromorphic hardware. SNNL decouples neuronal dynamics modeling from network topology specification: equation-based representations handle diverse neuron/synapse models, while hierarchical constructs define complex connectivity patterns. We present a Darwin3-targeted compiler with efficient code generation. Evaluations confirm SNNL achieves precise neuronal dynamic descriptions and flexible network configurations. This work bridges algorithm-hardware gaps in neuromorphic computing by enhancing programmability. Experimental results have demonstrated the feasibility of SNNL in developing SNNs for neuromorphic systems.

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NeurIPS Conference 2025 Conference Paper

SPICED: A Synaptic Homeostasis-Inspired Framework for Unsupervised Continual EEG Decoding

  • Yangxuan Zhou
  • Sha Zhao
  • Jiquan Wang
  • Haiteng Jiang
  • Shijian Li
  • Tao Li
  • Gang Pan

Human brain achieves dynamic stability-plasticity balance through synaptic homeostasis, a self-regulatory mechanism that stabilizes critical memory traces while preserving optimal learning capacities. Inspired by this biological principle, we propose SPICED: a neuromorphic framework that integrates the synaptic homeostasis mechanism for unsupervised continual EEG decoding, particularly addressing practical scenarios where new individuals with inter-individual variability emerge continually. SPICED comprises a novel synaptic network that enables dynamic expansion during continual adaptation through three bio-inspired neural mechanisms: (1) critical memory reactivation, which mimics brain functional specificity, selectively activates task-relevant memories to facilitate adaptation; (2) synaptic consolidation, which strengthens these reactivated critical memory traces and enhances their replay prioritizations for further adaptations and (3) synaptic renormalization, which are periodically triggered to weaken global memory traces to preserve learning capacities. The interplay within synaptic homeostasis dynamically strengthens task-discriminative memory traces and weakens detrimental memories. By integrating these mechanisms with continual learning system, SPICED preferentially replays task-discriminative memory traces that exhibit strong associations with newly emerging individuals, thereby achieving robust adaptations. Meanwhile, SPICED effectively mitigates catastrophic forgetting by suppressing the replay prioritization of detrimental memories during long-term continual learning. Validated on three EEG datasets, SPICED show its effectiveness. More importantly, SPICED bridges biological neural mechanisms and artificial intelligence through synaptic homeostasis, providing insights into the broader applicability of bio-inspired principles.

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AAAI Conference 2024 Conference Paper

Bridging the Semantic Latent Space between Brain and Machine: Similarity Is All You Need

  • Jiaxuan Chen
  • Yu Qi
  • Yueming Wang
  • Gang Pan

How our brain encodes complex concepts has been a longstanding mystery in neuroscience. The answer to this problem can lead to new understandings about how the brain retrieves information in large-scale data with high efficiency and robustness. Neuroscience studies suggest the brain represents concepts in a locality-sensitive hashing (LSH) strategy, i.e., similar concepts will be represented by similar responses. This finding has inspired the design of similarity-based algorithms, especially in contrastive learning. Here, we hypothesize that the brain and large neural network models, both using similarity-based learning rules, could contain a similar semantic embedding space. To verify that, this paper proposes a functional Magnetic Resonance Imaging (fMRI) semantic learning network named BrainSem, aimed at seeking a joint semantic latent space that bridges the brain and a Contrastive Language-Image Pre-training (CLIP) model. Given that our perception is inherently cross-modal, we introduce a fuzzy (one-to-many) matching loss function to encourage the models to extract high-level semantic components from neural signals. Our results claimed that using only a small set of fMRI recordings for semantic space alignment, we could obtain shared embedding valid for unseen categories out of the training set, which provided potential evidence for the semantic representation similarity between the brain and large neural networks. In a zero-shot classification task, our BrainSem achieves an 11.6% improvement over the state-of-the-art.

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JBHI Journal 2024 Journal Article

CareSleepNet: A Hybrid Deep Learning Network for Automatic Sleep Staging

  • Jiquan Wang
  • Sha Zhao
  • Haiteng Jiang
  • Yangxuan Zhou
  • Zhenghe Yu
  • Tao Li
  • Shijian Li
  • Gang Pan

Sleep staging is essential for sleep assessment and plays an important role in disease diagnosis, which refers to the classification of sleep epochs into different sleep stages. Polysomnography (PSG), consisting of many different physiological signals, e. g. electroencephalogram (EEG) and electrooculogram (EOG), is a gold standard for sleep staging. Although existing studies have achieved high performance on automatic sleep staging from PSG, there are still some limitations: 1) they focus on local features but ignore global features within each sleep epoch, and 2) they ignore cross-modality context relationship between EEG and EOG. In this paper, we propose CareSleepNet, a novel hybrid deep learning network for automatic sleep staging from PSG recordings. Specifically, we first design a multi-scale Convolutional-Transformer Epoch Encoder to encode both local salient wave features and global features within each sleep epoch. Then, we devise a Cross-Modality Context Encoder based on co-attention mechanism to model cross-modality context relationship between different modalities. Next, we use a Transformer-based Sequence Encoder to capture the sequential relationship among sleep epochs. Finally, the learned feature representations are fed into an epoch-level classifier to determine the sleep stages. We collected a private sleep dataset, SSND, and use two public datasets, Sleep-EDF-153 and ISRUC to evaluate the performance of CareSleepNet. The experiment results show that our CareSleepNet achieves the state-of-the-art performance on the three datasets. Moreover, we conduct ablation studies and attention visualizations to prove the effectiveness of each module and to analyze the influence of each modality.

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NeurIPS Conference 2024 Conference Paper

FEEL-SNN: Robust Spiking Neural Networks with Frequency Encoding and Evolutionary Leak Factor

  • Mengting Xu
  • De Ma
  • Huajin Tang
  • Qian Zheng
  • Gang Pan

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.

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AAAI Conference 2024 Conference Paper

Generalizable Sleep Staging via Multi-Level Domain Alignment

  • Jiquan Wang
  • Sha Zhao
  • Haiteng Jiang
  • Shijian Li
  • Tao Li
  • Gang Pan

Automatic sleep staging is essential for sleep assessment and disorder diagnosis. Most existing methods depend on one specific dataset and are limited to be generalized to other unseen datasets, for which the training data and testing data are from the same dataset. In this paper, we introduce domain generalization into automatic sleep staging and propose the task of generalizable sleep staging which aims to improve the model generalization ability to unseen datasets. Inspired by existing domain generalization methods, we adopt the feature alignment idea and propose a framework called SleepDG to solve it. Considering both of local salient features and sequential features are important for sleep staging, we propose a Multi-level Feature Alignment combining epoch-level and sequence-level feature alignment to learn domain-invariant feature representations. Specifically, we design an Epoch-level Feature Alignment to align the feature distribution of each single sleep epoch among different domains, and a Sequence-level Feature Alignment to minimize the discrepancy of sequential features among different domains. SleepDG is validated on five public datasets, achieving the state-of-the-art performance.

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IJCAI Conference 2024 Conference Paper

LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization

  • Qianhui Liu
  • Jiaqi Yan
  • Malu Zhang
  • Gang Pan
  • Haizhou Li

Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient, making them well-suited for low-power edge devices. However, the pursuit of accuracy in current studies leads to large, long-timestep SNNs, conflicting with the resource constraints of these devices. In order to design lightweight and efficient SNNs, we propose a new approach named LitE-SNN that incorporates both spatial and temporal compression into the automated network design process. Spatially, we present a novel Compressive Convolution block (CompConv) to expand the search space to support pruning and mixed-precision quantization. Temporally, we are the first to propose a compressive timestep search to identify the optimal number of timesteps under specific computation cost constraints. Finally, we formulate a joint optimization to simultaneously learn the architecture parameters and spatial-temporal compression strategies to achieve high performance while minimizing memory and computation costs. Experimental results on CIFAR-10, CIFAR-100, and Google Speech Command datasets demonstrate our proposed LitE-SNNs can achieve competitive or even higher accuracy with remarkably smaller model sizes and fewer computation costs.

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NeurIPS Conference 2024 Conference Paper

Rethinking the Membrane Dynamics and Optimization Objectives of Spiking Neural Networks

  • Hangchi Shen
  • Qian Zheng
  • Huamin Wang
  • Gang Pan

Despite spiking neural networks (SNNs) have demonstrated notable energy efficiency across various fields, the limited firing patterns of spiking neurons within fixed time steps restrict the expression of information, which impedes further improvement of SNN performance. In addition, current implementations of SNNs typically consider the firing rate or average membrane potential of the last layer as the output, lacking exploration of other possibilities. In this paper, we identify that the limited spike patterns of spiking neurons stem from the initial membrane potential (IMP), which is set to 0. By adjusting the IMP, the spiking neurons can generate additional firing patterns and pattern mappings. Furthermore, we find that in static tasks, the accuracy of SNNs at each time step increases as the membrane potential evolves from zero. This observation inspires us to propose a learnable IMP, which can accelerate the evolution of membrane potential and enables higher performance within a limited number of time steps. Additionally, we introduce the last time step (LTS) approach to accelerate convergence in static tasks, and we propose a label smooth temporal efficient training (TET) loss to mitigate the conflicts between optimization objective and regularization term in the vanilla TET. Our methods improve the accuracy by 4. 05\% on ImageNet compared to baseline and achieve state-of-the-art performance of 87. 80\% on CIFAR10-DVS and 87. 86\% on N-Caltech101.

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AAAI Conference 2024 Conference Paper

Spiking NeRF: Representing the Real-World Geometry by a Discontinuous Representation

  • Zhanfeng Liao
  • Yan Liu
  • Qian Zheng
  • Gang Pan

A crucial reason for the success of existing NeRF-based methods is to build a neural density field for the geometry representation via multiple perceptron layers (MLPs). MLPs are continuous functions, however, real geometry or density field is frequently discontinuous at the interface between the air and the surface. Such a contrary brings the problem of unfaithful geometry representation. To this end, this paper proposes spiking NeRF, which leverages spiking neurons and a hybrid Artificial Neural Network (ANN)-Spiking Neural Network (SNN) framework to build a discontinuous density field for faithful geometry representation. Specifically, we first demonstrate the reason why continuous density fields will bring inaccuracy. Then, we propose to use the spiking neurons to build a discontinuous density field. We conduct a comprehensive analysis for the problem of existing spiking neuron models and then provide the numerical relationship between the parameter of the spiking neuron and the theoretical accuracy of geometry. Based on this, we propose a bounded spiking neuron to build the discontinuous density field. Our method achieves SOTA performance. The source code and the supplementary material are available at https://github.com/liaozhanfeng/Spiking-NeRF.

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NeurIPS Conference 2023 Conference Paper

Alleviating the Semantic Gap for Generalized fMRI-to-Image Reconstruction

  • Tao Fang
  • Qian Zheng
  • Gang Pan

Although existing fMRI-to-image reconstruction methods could predict high-quality images, they do not explicitly consider the semantic gap between training and testing data, resulting in reconstruction with unstable and uncertain semantics. This paper addresses the problem of generalized fMRI-to-image reconstruction by explicitly alleviates the semantic gap. Specifically, we leverage the pre-trained CLIP model to map the training data to a compact feature representation, which essentially extends the sparse semantics of training data to dense ones, thus alleviating the semantic gap of the instances nearby known concepts (i. e. , inside the training super-classes). Inspired by the robust low-level representation in fMRI data, which could help alleviate the semantic gap for instances that far from the known concepts (i. e. , outside the training super-classes), we leverage structural information as a general cue to guide image reconstruction. Further, we quantify the semantic uncertainty based on probability density estimation and achieve Generalized fMRI-to-image reconstruction by adaptively integrating Expanded Semantics and Structural information (GESS) within a diffusion process. Experimental results demonstrate that the proposed GESS model outperforms state-of-the-art methods, and we propose a generalized scenario split strategy to evaluate the advantage of GESS in closing the semantic gap.

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AAAI Conference 2023 Conference Paper

Augmented Proximal Policy Optimization for Safe Reinforcement Learning

  • Juntao Dai
  • Jiaming Ji
  • Long Yang
  • Qian Zheng
  • Gang Pan

Safe reinforcement learning considers practical scenarios that maximize the return while satisfying safety constraints. Current algorithms, which suffer from training oscillations or approximation errors, still struggle to update the policy efficiently with precise constraint satisfaction. In this article, we propose Augmented Proximal Policy Optimization (APPO), which augments the Lagrangian function of the primal constrained problem via attaching a quadratic deviation term. The constructed multiplier-penalty function dampens cost oscillation for stable convergence while being equivalent to the primal constrained problem to precisely control safety costs. APPO alternately updates the policy and the Lagrangian multiplier via solving the constructed augmented primal-dual problem, which can be easily implemented by any first-order optimizer. We apply our APPO methods in diverse safety-constrained tasks, setting a new state of the art compared with a comprehensive list of safe RL baselines. Extensive experiments verify the merits of our method in easy implementation, stable convergence, and precise cost control.

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YNIMG Journal 2023 Journal Article

Design and application of a multimodality-compatible 1Tx/6Rx RF coil for monkey brain MRI at 7T

  • Shuxian Qu
  • Sunhang Shi
  • Zhiyan Quan
  • Yang Gao
  • Minmin Wang
  • Yueming Wang
  • Gang Pan
  • Hsin-Yi Lai

OBJECTIVE: Blood-oxygen-level-dependent functional MRI allows to investigte neural activities and connectivity. While the non-human primate plays an essential role in neuroscience research, multimodal methods combining functional MRI with other neuroimaging and neuromodulation enable us to understand the brain network at multiple scales. APPROACH: In this study, a tight-fitting helmet-shape receive array with a single transmit loop for anesthetized macaque brain MRI at 7T was fabricated with four openings constructed in the coil housing to accommodate multimodal devices, and the coil performance was quantitatively evaluated and compared to a commercial knee coil. In addition, experiments over three macaques with infrared neural stimulation (INS), focused ultrasound stimulation (FUS), and transcranial direct current stimulation (tDCS) were conducted. MAIN RESULTS: The RF coil showed higher transmit efficiency, comparable homogeneity, improved SNR and enlarged signal coverage over the macaque brain. Infrared neural stimulation was applied to the amygdala in deep brain region, and activations in stimulation sites and connected sites were detected, with the connectivity consistent with anatomical information. Focused ultrasound stimulation was applied to the left visual cortex, and activations were acquired along the ultrasound traveling path, with all time course curves consistent with pre-designed paradigms. The existence of transcranial direct current stimulation electrodes brought no interference to the RF system, as evidenced through high-resolution MPRAGE structure images. SIGNIFICANCE: This pilot study reveals the feasibility for brain investigation at multiple spatiotemporal scales, which may advance our understanding in dynamic brain networks.

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NeurIPS Conference 2023 Conference Paper

Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks

  • Qi Xu
  • Yuyuan Gao
  • Jiangrong Shen
  • Yaxin Li
  • Xuming Ran
  • Huajin Tang
  • Gang Pan

Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from Dynamic Vision Sensor (DVS). Although convolutional SNNs have achieved remarkable performance on these AER datasets, benefiting from the predominant spatial feature extraction ability of convolutional structure, they ignore temporal features related to sequential time points. In this paper, we develop a recurrent spiking neural network (RSNN) model embedded with an advanced spiking convolutional block attention module (SCBAM) component to combine both spatial and temporal features of spatio-temporal patterns. It invokes the history information in spatial and temporal channels adaptively through SCBAM, which brings the advantages of efficient memory calling and history redundancy elimination. The performance of our model was evaluated in DVS128-Gesture dataset and other time-series datasets. The experimental results show that the proposed SRNN-SCBAM model makes better use of the history information in spatial and temporal dimensions with less memory space, and achieves higher accuracy compared to other models.

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AAAI Conference 2023 Conference Paper

ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks

  • Jiangrong Shen
  • Qi Xu
  • Jian K. Liu
  • Yueming Wang
  • Gang Pan
  • Huajin Tang

Spiking neural networks (SNNs) have manifested remarkable advantages in power consumption and event-driven property during the inference process. To take full advantage of low power consumption and improve the efficiency of these models further, the pruning methods have been explored to find sparse SNNs without redundancy connections after training. However, parameter redundancy still hinders the efficiency of SNNs during training. In the human brain, the rewiring process of neural networks is highly dynamic, while synaptic connections maintain relatively sparse during brain development. Inspired by this, here we propose an efficient evolutionary structure learning (ESL) framework for SNNs, named ESL-SNNs, to implement the sparse SNN training from scratch. The pruning and regeneration of synaptic connections in SNNs evolve dynamically during learning, yet keep the structural sparsity at a certain level. As a result, the ESL-SNNs can search for optimal sparse connectivity by exploring all possible parameters across time. Our experiments show that the proposed ESL-SNNs framework is able to learn SNNs with sparse structures effectively while reducing the limited accuracy. The ESL-SNNs achieve merely 0.28% accuracy loss with 10% connection density on the DVS-Cifar10 dataset. Our work presents a brand-new approach for sparse training of SNNs from scratch with biologically plausible evolutionary mechanisms, closing the gap in the expressibility between sparse training and dense training. Hence, it has great potential for SNN lightweight training and inference with low power consumption and small memory usage.

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AAAI Conference 2023 Conference Paper

Extracting Semantic-Dynamic Features for Long-Term Stable Brain Computer Interface

  • Tao Fang
  • Qian Zheng
  • Yu Qi
  • Gang Pan

Brain-computer Interface (BCI) builds a neural signal to the motor command pathway, which is a prerequisite for the realization of neural prosthetics. However, a long-term stable BCI suffers from the neural data drift across days while retraining the BCI decoder is expensive and restricts its application scenarios. Recent solutions of neural signal recalibration treat the continuous neural signals as discrete, which is less effective in temporal feature extraction. Inspired by the observation from biologists that low-dimensional dynamics could describe high-dimensional neural signals, we model the underlying neural dynamics and propose a semantic-dynamic feature that represents the semantics and dynamics in a shared feature space facilitating the BCI recalibration. Besides, we present the joint distribution alignment instead of the common used marginal alignment strategy, dealing with the various complex changes in neural data distribution. Our recalibration approach achieves state-of-the-art performance on the real neural data of two monkeys in both classification and regression tasks. Our approach is also evaluated on a simulated dataset, which indicates its robustness in dealing with various common causes of neural signal instability.

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AAAI Conference 2023 Conference Paper

Loan Fraud Users Detection in Online Lending Leveraging Multiple Data Views

  • Sha Zhao
  • Yongrui Huang
  • Ling Chen
  • Chunping Wang
  • Shijian Li
  • Lei Chen
  • Gang Pan

In recent years, online lending platforms have been becoming attractive for micro-financing and popular in financial industries. However, such online lending platforms face a high risk of failure due to the lack of expertise on borrowers' creditworthness. Thus, risk forecasting is important to avoid economic loss. Detecting loan fraud users in advance is at the heart of risk forecasting. The purpose of fraud user (borrower) detection is to predict whether one user will fail to make required payments in the future. Detecting fraud users depend on historical loan records. However, a large proportion of users lack such information, especially for new users. In this paper, we attempt to detect loan fraud users from cross domain heterogeneous data views, including user attributes, installed app lists, app installation behaviors, and app-in logs, which compensate for the lack of historical loan records. However, it is difficult to effectively fuse the multiple heterogeneous data views. Moreover, some samples miss one or even more data views, increasing the difficulty in fusion. To address the challenges, we propose a novel end-to-end deep multiview learning approach, which encodes heterogeneous data views into homogeneous ones, generates the missing views based on the learned relationship among all the views, and then fuses all the views together to a comprehensive view for identifying fraud users. Our model is evaluated on a real-world large-scale dataset consisting of 401,978 loan records of 228,117 users from January 1, 2019, to September 30, 2019, achieving the state-of-the-art performance.

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AAAI Conference 2023 Conference Paper

Off-Policy Proximal Policy Optimization

  • Wenjia Meng
  • Qian Zheng
  • Gang Pan
  • Yilong Yin

Proximal Policy Optimization (PPO) is an important reinforcement learning method, which has achieved great success in sequential decision-making problems. However, PPO faces the issue of sample inefficiency, which is due to the PPO cannot make use of off-policy data. In this paper, we propose an Off-Policy Proximal Policy Optimization method (Off-Policy PPO) that improves the sample efficiency of PPO by utilizing off-policy data. Specifically, we first propose a clipped surrogate objective function that can utilize off-policy data and avoid excessively large policy updates. Next, we theoretically clarify the stability of the optimization process of the proposed surrogate objective by demonstrating the degree of policy update distance is consistent with that in the PPO. We then describe the implementation details of the proposed Off-Policy PPO which iteratively updates policies by optimizing the proposed clipped surrogate objective. Finally, the experimental results on representative continuous control tasks validate that our method outperforms the state-of-the-art methods on most tasks.

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ICRA Conference 2023 Conference Paper

SceneCalib: Automatic Targetless Calibration of Cameras and Lidars in Autonomous Driving

  • Ayon Sen
  • Gang Pan
  • Anton Mitrokhin
  • Ashraful Islam

Accurate camera-to-lidar calibration is a requirement for sensor data fusion in many 3D perception tasks. In this paper, we present SceneCalib, a novel method for simultaneous self-calibration of extrinsic and intrinsic parameters in a system containing multiple cameras and a lidar sensor. Existing methods typically require specially designed calibration targets and human operators, or they only attempt to solve for a subset of calibration parameters. We resolve these issues with a fully automatic method that requires no explicit correspondences between camera images and lidar point clouds, allowing for robustness to many outdoor environments. Furthermore, the full system is jointly calibrated with explicit cross-camera constraints to ensure that camera-to-camera and camera-to-lidar extrinsic parameters are consistent.

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NeurIPS Conference 2022 Conference Paper

Constrained Update Projection Approach to Safe Policy Optimization

  • Long Yang
  • Jiaming Ji
  • Juntao Dai
  • Linrui Zhang
  • Binbin Zhou
  • Pengfei Li
  • Yaodong Yang
  • Gang Pan

Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only maximize reward but also avoid exploring unsafe areas. In this study, we propose CUP, a novel policy optimization method based on Constrained Update Projection framework that enjoys rigorous safety guarantee. Central to our CUP development is the newly proposed surrogate functions along with the performance bound. Compared to previous safe reinforcement learning meth- ods, CUP enjoys the benefits of 1) CUP generalizes the surrogate functions to generalized advantage estimator (GAE), leading to strong empirical performance. 2) CUP unifies performance bounds, providing a better understanding and in- terpretability for some existing algorithms; 3) CUP provides a non-convex im- plementation via only first-order optimizers, which does not require any strong approximation on the convexity of the objectives. To validate our CUP method, we compared CUP against a comprehensive list of safe RL baselines on a wide range of tasks. Experiments show the effectiveness of CUP both in terms of reward and safety constraint satisfaction. We have opened the source code of CUP at https: //github. com/zmsn-2077/CUP-safe-rl.

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TIST Journal 2022 Journal Article

Jointly Optimizing Expressional and Residual Models for 3D Facial Expression Removal

  • Qian Zheng
  • Yueming Wang
  • Zhenfang Hu
  • Xiaobo Zhang
  • Zhaohui Wu
  • Gang Pan

This article proposes a facial expression removal method to recover a 3D neutral face from a single 3D expressional or non-neutral face. We treat a 3D non-neutral face as the sum of its neutral one and the residual. This can be satisfied if the correspondence between 3D vertices of expressional faces and those of neutral faces is established. We propose a non-rigid deformation method to establish the correspondence between 3D faces. Then, according to algebra inequality, the minimization of a neutral face model can be replaced by the minimization of its upper bound, i.e., the errors of an expressional face model and a residual model. Thus, we co-optimize the representation errors of the latter two models and build the relationship between the representation coefficients of the two models. Given an expressional face as the input, its corresponding neutral face can be inferred by the associative representation parameters in these two models. In the testing stage, we use an iterative joint fitting scheme to obtain a more accurate recovery. Extensive experiments are conducted to evaluate our method. The results show that our method obtains considerably better performance than existing methods in terms of average root mean square errors and recognition rates, and also better visual effects.

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IJCAI Conference 2022 Conference Paper

Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks

  • Lang Feng
  • Qianhui Liu
  • Huajin Tang
  • De Ma
  • Gang Pan

Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous discrete and sparse characteristics, which have increasingly manifested their superiority in low energy consumption. Recent research is devoted to utilizing spatio-temporal information to directly train SNNs by backpropagation. However, the binary and non-differentiable properties of spike activities force directly trained SNNs to suffer from serious gradient vanishing and network degradation, which greatly limits the performance of directly trained SNNs and prevents them from going deeper. In this paper, we propose a multi-level firing (MLF) method based on the existing spatio-temporal back propagation (STBP) method, and spiking dormant-suppressed residual network (spiking DS-ResNet). MLF enables more efficient gradient propagation and the incremental expression ability of the neurons. Spiking DS-ResNet can efficiently perform identity mapping of discrete spikes, as well as provide a more suitable connection for gradient propagation in deep SNNs. With the proposed method, our model achieves superior performances on a non-neuromorphic dataset and two neuromorphic datasets with much fewer trainable parameters and demonstrates the great ability to combat the gradient vanishing and degradation problem in deep SNNs.

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NeurIPS Conference 2022 Conference Paper

Online Neural Sequence Detection with Hierarchical Dirichlet Point Process

  • Weihan Li
  • Yu Qi
  • Gang Pan

Neural sequence detection plays a vital role in neuroscience research. Recent impressive works utilize convolutive nonnegative matrix factorization and Neyman-Scott process to solve this problem. However, they still face two limitations. Firstly, they accommodate the entire dataset into memory and perform iterative updates of multiple passes, which can be inefficient when the dataset is large or grows frequently. Secondly, they rely on the prior knowledge of the number of sequence types, which can be impractical with data when the future situation is unknown. To tackle these limitations, we propose a hierarchical Dirichlet point process model for efficient neural sequence detection. Instead of computing the entire data, our model can sequentially detect sequences in an online unsupervised manner with Particle filters. Besides, the Dirichlet prior enables our model to automatically introduce new sequence types on the fly as needed, thus avoiding specifying the number of types in advance. We manifest these advantages on synthetic data and neural recordings from songbird higher vocal center and rodent hippocampus.

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AAAI Conference 2022 Conference Paper

Policy Optimization with Stochastic Mirror Descent

  • Long Yang
  • Yu Zhang
  • Gang Zheng
  • Qian Zheng
  • Pengfei Li
  • Jianhang Huang
  • Gang Pan

Improving sample efficiency has been a longstanding goal in reinforcement learning. This paper proposes VRMPO algorithm: a sample efficient policy gradient method with stochastic mirror descent. In VRMPO, a novel variance-reduced policy gradient estimator is presented to improve sample efficiency. We prove that the proposed VRMPO needs only O( −3 ) sample trajectories to achieve an -approximate first-order stationary point, which matches the best sample complexity for policy optimization. Extensive empirical results demonstrate that VRMPO outperforms the state-of-the-art policy gradient methods in various settings.

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IJCAI Conference 2022 Conference Paper

TinyLight: Adaptive Traffic Signal Control on Devices with Extremely Limited Resources

  • Dong Xing
  • Qian Zheng
  • Qianhui Liu
  • Gang Pan

Recent advances in deep reinforcement learning (DRL) have largely promoted the performance of adaptive traffic signal control (ATSC). Nevertheless, regarding the implementation, most works are cumbersome in terms of storage and computation. This hinders their deployment on scenarios where resources are limited. In this work, we propose TinyLight, the first DRL-based ATSC model that is designed for devices with extremely limited resources. TinyLight first constructs a super-graph to associate a rich set of candidate features with a group of light-weighted network blocks. Then, to diminish the model's resource consumption, we ablate edges in the super-graph automatically with a novel entropy-minimized objective function. This enables TinyLight to work on a standalone microcontroller with merely 2KB RAM and 32KB ROM. We evaluate TinyLight on multiple road networks with real-world traffic demands. Experiments show that even with extremely limited resources, TinyLight still achieves competitive performance. The source code and appendix of this work can be found at https: //bit. ly/38hH8t8.

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NeurIPS Conference 2022 Conference Paper

Tracking Functional Changes in Nonstationary Signals with Evolutionary Ensemble Bayesian Model for Robust Neural Decoding

  • Xinyun Zhu
  • Yu Qi
  • Gang Pan
  • Yueming Wang

Neural signals are typical nonstationary data where the functional mapping between neural activities and the intentions (such as the velocity of movements) can occasionally change. Existing studies mostly use a fixed neural decoder, thus suffering from an unstable performance given neural functional changes. We propose a novel evolutionary ensemble framework (EvoEnsemble) to dynamically cope with changes in neural signals by evolving the decoder model accordingly. EvoEnsemble integrates evolutionary computation algorithms in a Bayesian framework where the fitness of models can be sequentially computed with their likelihoods according to the incoming data at each time slot, which enables online tracking of time-varying functions. Two strategies of evolve-at-changes and history-model-archive are designed to further improve efficiency and stability. Experiments with simulations and neural signals demonstrate that EvoEnsemble can track the changes in functions effectively thus improving the accuracy and robustness of neural decoding. The improvement is most significant in neural signals with functional changes.

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IJCAI Conference 2021 Conference Paper

Event-based Action Recognition Using Motion Information and Spiking Neural Networks

  • Qianhui Liu
  • Dong Xing
  • Huajin Tang
  • De Ma
  • Gang Pan

Event-based cameras have attracted increasing attention due to their advantages of biologically inspired paradigm and low power consumption. Since event-based cameras record the visual input as asynchronous discrete events, they are inherently suitable to cooperate with the spiking neural network (SNN). Existing works of SNNs for processing events mainly focus on the task of object recognition. However, events from the event-based camera are triggered by dynamic changes, which makes it an ideal choice to capture actions in the visual scene. Inspired by the dorsal stream in visual cortex, we propose a hierarchical SNN architecture for event-based action recognition using motion information. Motion features are extracted and utilized from events to local and finally to global perception for action recognition. To the best of the authors’ knowledge, it is the first attempt of SNN to apply motion information to event-based action recognition. We evaluate our proposed SNN on three event-based action recognition datasets, including our newly published DailyAction-DVS dataset comprising 12 actions collected under diverse recording conditions. Extensive experimental results show the effectiveness of motion information and our proposed SNN architecture for event-based action recognition.

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IJCAI Conference 2021 Conference Paper

Learning with Generated Teammates to Achieve Type-Free Ad-Hoc Teamwork

  • Dong Xing
  • Qianhui Liu
  • Qian Zheng
  • Gang Pan

In ad-hoc teamwork, an agent is required to cooperate with unknown teammates without prior coordination. To swiftly adapt to an unknown teammate, most works adopt a type-based approach, which pre-trains the agent with a set of pre-prepared teammate types, then associates the unknown teammate with a particular type. Typically, these types are collected manually. This hampers previous works by both the availability and diversity of types they manage to obtain. To eliminate these limitations, this work addresses to achieve ad-hoc teamwork in a type-free approach. Specifically, we propose the model of Entropy-regularized Deep Recurrent Q-Network (EDRQN) to generate teammates automatically, meanwhile utilize them to pre-train our agent. These teammates are obtained from scratch and are designed to perform the task with various behaviors, therefore their availability and diversity are both ensured. We evaluate our model on several benchmark domains of ad-hoc teamwork. The result shows that even if our model has no access to any pre-prepared teammate types, it still achieves significant performance.

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AAAI Conference 2021 Conference Paper

On Convergence of Gradient Expected Sarsa(λ)

  • Long Yang
  • Gang Zheng
  • Yu Zhang
  • Qian Zheng
  • Pengfei Li
  • Gang Pan

We study the convergence of Expected Sarsa(λ) with function approximation. We show that with off-line estimate (multi-step bootstrapping) to Expected Sarsa(λ) is unstable for off-policy learning. Furthermore, based on convex-concave saddle-point framework, we propose a convergent Gradient Expected Sarsa(λ) (GES(λ)) algorithm. The theoretical analysis shows that the proposed GES(λ) converges to the optimal solution at a linear convergence rate under true gradient setting. Furthermore, we develop a Lyapunov function technique to investigate how the stepsize influences finite-time performance of GES(λ). Additionally, such a technique of Lyapunov function can be potentially generalized to other gradient temporal difference algorithms. Finally, our experiments verify the effectiveness of our GES(λ). For the details of proof, please refer to https: //arxiv. org/pdf/2012. 07199. pdf.

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AAAI Conference 2021 Conference Paper

Sample Complexity of Policy Gradient Finding Second-Order Stationary Points

  • Long Yang
  • Qian Zheng
  • Gang Pan

The policy-based reinforcement learning (RL) can be considered as maximization of its objective. However, due to the inherent non-concavity of its objective, the policy gradient method to a first-order stationary point (FOSP) cannot guarantee a maximal point. A FOSP can be a minimal or even a saddle point, which is undesirable for RL. It has be found that if all the saddle points are strict, all the second-order stationary points (SOSP) are exactly equivalent to local maxima. Instead of FOSP, we consider SOSP as the convergence criteria to characterize the sample complexity of policy gradient. Our result shows that policy gradient converges to an (, √ χ)- SOSP with probability at least 1 − e O(δ) after the total cost of O − 9 2 (1−γ) √ χ log 1 δ = O( − 9 2 ), where γ ∈ (0, 1). It significantly improves the state of the art cost e O( −9 ). Our analysis is based on the key idea that decomposes the parameter space Rp into three non-intersected regions: non-stationary point region, saddle point region, and local optimal region, then making a local improvement of the objective of RL in each region. This technique can be potentially generalized to extensive policy gradient methods. For the complete proof, please refer to https: //arxiv. org/pdf/2012. 01491. pdf.

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AAAI Conference 2020 Conference Paper

Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks

  • Qianhui Liu
  • Haibo Ruan
  • Dong Xing
  • Huajin Tang
  • Gang Pan

Address event representation (AER) cameras have recently attracted more attention due to the advantages of high temporal resolution and low power consumption, compared with traditional frame-based cameras. Since AER cameras record the visual input as asynchronous discrete events, they are inherently suitable to coordinate with the spiking neural network (SNN), which is biologically plausible and energyefficient on neuromorphic hardware. However, using SNN to perform the AER object classification is still challenging, due to the lack of effective learning algorithms for this new representation. To tackle this issue, we propose an AER object classification model using a novel segmented probabilitymaximization (SPA) learning algorithm. Technically, 1) the SPA learning algorithm iteratively maximizes the probability of the classes that samples belong to, in order to improve the reliability of neuron responses and effectiveness of learning; 2) a peak detection (PD) mechanism is introduced in SPA to locate informative time points segment by segment, based on which information within the whole event stream can be fully utilized by the learning. Extensive experimental results show that, compared to state-of-the-art methods, not only our model is more effective, but also it requires less information to reach a certain level of accuracy.

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TIST Journal 2020 Journal Article

Forecasting Price Trend of Bulk Commodities Leveraging Cross-domain Open Data Fusion

  • Binbin Zhou
  • Sha Zhao
  • Longbiao Chen
  • Shijian Li
  • Zhaohui Wu
  • Gang Pan

Forecasting price trend of bulk commodities is important in international trade, not only for markets participants to schedule production and marketing plans but also for government administrators to adjust policies. Previous studies cannot support accurate fine-grained short-term prediction, since they mainly focus on coarse-grained long-term prediction using historical data. Recently, cross-domain open data provides possibilities to conduct fine-grained price forecasting, since they can be leveraged to extract various direct and indirect factors of the price. In this article, we predict the price trend over upcoming days, by leveraging cross-domain open data fusion. More specifically, we formulate the price trend into three classes (rise, slight-change, and fall), and then we predict the specific class in which the price trend of the future day lies. We take three factors into consideration: (1) supply factor considering sources providing bulk commodities,<?brk?> (2) demand factor focusing on vessel transportation with reflection of short time needs, and (3) expectation factor encompassing indirect features (e.g., air quality) with latent influences. A hybrid classification framework is proposed for the price trend forecasting. Evaluation conducted on nine real-world cross-domain open datasets shows that our framework can forecast the price trend accurately, outperforming multiple state-of-the-art baselines.

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NeurIPS Conference 2020 Conference Paper

Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN

  • Tao Fang
  • Yu Qi
  • Gang Pan

Reconstructing seeing images from fMRI recordings is an absorbing research area in neuroscience and provides a potential brain-reading technology. The challenge lies in that visual encoding in brain is highly complex and not fully revealed. Inspired by the theory that visual features are hierarchically represented in cortex, we propose to break the complex visual signals into multi-level components and decode each component separately. Specifically, we decode shape and semantic representations from the lower and higher visual cortex respectively, and merge the shape and semantic information to images by a generative adversarial network (Shape-Semantic GAN). This 'divide and conquer' strategy captures visual information more accurately. Experiments demonstrate that Shape-Semantic GAN improves the reconstruction similarity and image quality, and achieves the state-of-the-art image reconstruction performance.

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NeurIPS Conference 2019 Conference Paper

Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces

  • Yu Qi
  • Bin Liu
  • Yueming Wang
  • Gang Pan

Brain-computer interfaces (BCIs) have enabled prosthetic device control by decoding motor movements from neural activities. Neural signals recorded from cortex exhibit nonstationary property due to abrupt noises and neuroplastic changes in brain activities during motor control. Current state-of-the-art neural signal decoders such as Kalman filter assume fixed relationship between neural activities and motor movements, thus will fail if this assumption is not satisfied. We propose a dynamic ensemble modeling (DyEnsemble) approach that is capable of adapting to changes in neural signals by employing a proper combination of decoding functions. The DyEnsemble method firstly learns a set of diverse candidate models. Then, it dynamically selects and combines these models online according to Bayesian updating mechanism. Our method can mitigate the effect of noises and cope with different task behaviors by automatic model switching, thus gives more accurate predictions. Experiments with neural data demonstrate that the DyEnsemble method outperforms Kalman filters remarkably, and its advantage is more obvious with noisy signals.

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IJCAI Conference 2019 Conference Paper

STCA: Spatio-Temporal Credit Assignment with Delayed Feedback in Deep Spiking Neural Networks

  • Pengjie Gu
  • Rong Xiao
  • Gang Pan
  • Huajin Tang

The temporal credit assignment problem, which aims to discover the predictive features hidden in distracting background streams with delayed feedback, remains a core challenge in biological and machine learning. To address this issue, we propose a novel spatio-temporal credit assignment algorithm called STCA for training deep spiking neural networks (DSNNs). We present a new spatiotemporal error backpropagation policy by defining a temporal based loss function, which is able to credit the network losses to spatial and temporal domains simultaneously. Experimental results on MNIST dataset and a music dataset (MedleyDB) demonstrate that STCA can achieve comparable performance with other state-of-the-art algorithms with simpler architectures. Furthermore, STCA successfully discovers predictive sensory features and shows the highest performance in the unsegmented sensory event detection tasks.

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YNICL Journal 2019 Journal Article

Structural connectome alterations in patients with disorders of consciousness revealed by 7-tesla magnetic resonance imaging

  • Xufei Tan
  • Zhen Zhou
  • Jian Gao
  • Fanxia Meng
  • Yamei Yu
  • Jie Zhang
  • Fangping He
  • Ruili Wei

Although the functional connectivity of patients with disorders of consciousness (DOC) has been widely examined, less is known about brain white matter connectivity. The aim of this study was to explore structural network alterations for the diagnosis and prognosis of patients with chronic DOC. Eleven DOC patients and 11 sex- and age-matched controls were included in the study. Participants underwent diffusion magnetic resonance imaging (MRI) and T1-weighted structural MRI at 7 tesla (7 T). Graph-theoretical analysis and network-based statistics were used to analyze the group differences. Two patients were scanned twice for a longitudinal study to examine the relationship between connectome metrics and the patients' prognoses. Compared with healthy controls, DOC patients showed significantly elevated transitivity (p < .001), local efficiency (p = .009), and clustering coefficient (p = .039). When comparing the connectome metrics within the three groups (healthy controls, minimally conscious state (MCS), and vegetative state/unresponsive wakefulness syndrome (VS/UWS)), significant group differences were observed in transitivity (p < .001) and local efficiency (p = .031). Significantly increased transitivity was observed in vegetative state/unresponsive wakefulness syndrome compared with minimally conscious state (p = .0217, Bonferroni corrected). Transitivity showed significant negative correlations with the Coma Recovery Scale-Revised score (r = -0.6902, p = .023), consistent with the longitudinal study results. A subnetwork with significantly decreased structural connections was identified using network-based statistical analysis comparing DOC patients with healthy controls, which was mainly located in the frontal cortex, limbic system, and occipital and parietal lobes. This preliminary study suggests that graph theoretical approaches for assessing white matter connectivity may enable various states of DOC to be distinguished. Of the metrics analyzed, transitivity had a critical role in distinguishing the diagnostic groups. Larger cohorts will be necessary to confirm the predictive value of 7 T MRI in the prognosis of DOC patients.

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AAMAS Conference 2019 Conference Paper

TBQ(σ): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning

  • Longxiang Shi
  • Shijian Li
  • Longbing Cao
  • Long Yang
  • Gang Pan

Off-policy reinforcement learning with eligibility traces faces is challenging because of the discrepancy between target policy and behavior policy. One common approach is to measure the difference between two policies in a probabilistic way, such as importance sampling and tree-backup. However, existing off-policy learning methods based on probabilistic policy measurement are inefficient when utilizing traces under a greedy target policy, which is ineffective for control problems. The traces are cut immediately when a non-greedy action is taken, which may lose the advantage of eligibility traces and slow down the learning process. Alternatively, some non-probabilistic measurement methods such as General Q(λ) and Naive Q(λ) never cut traces, but face convergence problems in practice. To address the above issues, this paper introduces a new method named TBQ(σ), which effectively unifies the tree-backup algorithm and Naive Q(λ). By introducing a new parameter σ to illustrate the degree of utilizing traces, TBQ(σ) creates an effective integration of TB(λ) and Naive Q(λ) and continuous role shift between them. The contraction property of TB(σ) is theoretically analyzed for both policy evaluation and control settings. We also derive the online version of TBQ(σ) and give the convergence proof. We empirically show that, for ϵ ∈ (0, 1] in ϵ-greedy policies, there exists some degree of utilizing traces for λ ∈ [0, 1], which can improve the efficiency in trace utilization for off-policy reinforcement learning, to both accelerate the learning process and improve the performance.

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IJCAI Conference 2018 Conference Paper

A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning

  • Long Yang
  • Minhao Shi
  • Qian Zheng
  • Wenjia Meng
  • Gang Pan

Recently, a new multi-step temporal learning algorithm Q(σ) unifies n-step Tree-Backup (when σ = 0) and n-step Sarsa (when σ = 1) by introducing a sampling parameter σ. However, similar to other multi-step temporal-difference learning algorithms, Q(σ) needs much memory consumption and computation time. Eligibility trace is an important mechanism to transform the off-line updates into efficient on-line ones which consume less memory and computation time. In this paper, we combine the original Q(σ) with eligibility traces and propose a new algorithm, called Qπ(σ, λ), where λ is trace-decay parameter. This new algorithm unifies Sarsa(λ) (when σ = 1) and Qπ (λ) (when σ = 0). Furthermore, we give an upper error bound of Qπ(σ, λ) policy evaluation algorithm. We prove that Qπ (σ, λ) control algorithm converges to the optimal value function exponentially. We also empirically compare it with conventional temporal-difference learning methods. Results show that, with an intermediate value of σ, Qπ(σ, λ) creates a mixture of the existing algorithms which learn the optimal value significantly faster than the extreme end (σ = 0, or 1).

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IJCAI Conference 2018 Conference Paper

CSNN: An Augmented Spiking based Framework with Perceptron-Inception

  • Qi Xu
  • Yu Qi
  • Hang Yu
  • Jiangrong Shen
  • Huajin Tang
  • Gang Pan

Spiking Neural Networks (SNNs) represent and transmit information in spikes, which is considered more biologically realistic and computationally powerful than the traditional Artificial Neural Networks. The spiking neurons encode useful temporal information and possess highly anti-noise property. The feature extraction ability of typical SNNs is limited by shallow structures. This paper focuses on improving the feature extraction ability of SNNs in virtue of powerful feature extraction ability of Convolutional Neural Networks (CNNs). CNNs can extract abstract features resorting to the structure of the convolutional feature maps. We propose a CNN-SNN (CSNN) model to combine feature learning ability of CNNs with cognition ability of SNNs. The CSNN model learns the encoded spatial temporal representations of images in an event-driven way. We evaluate the CSNN model on the handwritten digits images dataset MNIST and its variational databases. In the presented experimental results, the proposed CSNN model is evaluated regarding learning capabilities, encoding mechanisms, robustness to noisy stimuli and its classification performance. The results show that CSNN behaves well compared to other cognitive models with significantly fewer neurons and training samples. Our work brings more biological realism into modern image classification models, with the hope that these models can inform how the brain performs this high-level vision task.

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IJCAI Conference 2018 Conference Paper

Jointly Learning Network Connections and Link Weights in Spiking Neural Networks

  • Yu Qi
  • Jiangrong Shen
  • Yueming Wang
  • Huajin Tang
  • Hang Yu
  • Zhaohui Wu
  • Gang Pan

Spiking neural networks (SNNs) are considered to be biologically plausible and power-efficient on neuromorphic hardware. However, unlike the brain mechanisms, most existing SNN algorithms have fixed network topologies and connection relationships. This paper proposes a method to jointly learn network connections and link weights simultaneously. The connection structures are optimized by the spike-timing-dependent plasticity (STDP) rule with timing information, and the link weights are optimized by a supervised algorithm. The connection structures and the weights are learned alternately until a termination condition is satisfied. Experiments are carried out using four benchmark datasets. Our approach outperforms classical learning methods such as STDP, Tempotron, SpikeProp, and a state-of-the-art supervised algorithm. In addition, the learned structures effectively reduce the number of connections by about 24%, thus facilitate the computational efficiency of the network.

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IJCAI Conference 2018 Conference Paper

Knowledge-Guided Agent-Tactic-Aware Learning for StarCraft Micromanagement

  • Yue Hu
  • Juntao Li
  • Xi Li
  • Gang Pan
  • Mingliang Xu

As an important and challenging problem in artificial intelligence (AI) game playing, StarCraft micromanagement involves a dynamically adversarial game playing process with complex multi-agent control within a large action space. In this paper, we propose a novel knowledge-guided agent-tactic-aware learning scheme, that is, opponent-guided tactic learning (OGTL), to cope with this micromanagement problem. In principle, the proposed scheme takes a two-stage cascaded learning strategy which is capable of not only transferring the human tactic knowledge from the human-made opponent agents to our AI agents but also improving the adversarial ability. With the power of reinforcement learning, such a knowledge-guided agent-tactic-aware scheme has the ability to guide the AI agents to achieve high winning-rate performances while accelerating the policy exploration process in a tactic-interpretable fashion. Experimental results demonstrate the effectiveness of the proposed scheme against the state-of-the-art approaches in several benchmark combat scenarios.

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IS Journal 2016 Journal Article

Cyborg Intelligence: Recent Progress and Future Directions

  • Zhaohui Wu
  • Yongdi Zhou
  • Zhongzhi Shi
  • Changshui Zhang
  • Guanglin Li
  • Xiaoxiang Zheng
  • Nenggan Zheng
  • Gang Pan

The combination of biological and artificial intelligence is a promising methodology to construct a novel intelligent modality, proposed as cyborg intelligence. The hierarchical conceptual framework is based on the interaction and combination of comparable components of biological cognitive units and computing intelligent units. The authors extend the previous conceptual framework and focus on sensorimotor circuits to explore the representation and integration of sensation. They then present a cognitive computing model for brain-computer integration and design efficient machine-learning algorithms for neural signal decoding. They also propose biological reconstruction methods for sensorimotor circuits to not only restore but enhance functionalities with AI. They develop a series of demonstrating systems to validate the conceptual framework of cyborg intelligence and possibly herald bright prospects and high values in diversified aspects of theoretical research, engineering techniques, and clinical applications. Finally, the authors summarize the latest research trends and challenges, which they believe will further boost new scientific frontiers in cyborg intelligence.

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IS Journal 2015 Journal Article

A Cauchy-Based State-Space Model for Seizure Detection in EEG Monitoring Systems

  • Yueming Wang
  • Yu Qi
  • Junming Zhu
  • Jianmin Zhang
  • Yiwen Wang
  • Gang Pan
  • Xiaoxiang Zheng
  • Zhaohui Wu

This article proposes a state-space model with Cauchy observation noise (SSMC) to detect seizure onset in a long-term EEG monitoring system. Facing the challenge of high false detection rates (FDRs) in many existing methods caused by impulsive EOG/EMG artifacts, the SSMC uses a nonlinear state-space model to encode the gradual changes of epileptic seizure signals and reject abrupt changes brought by artifacts. The Cauchy distribution is proposed to model impulsive observation noises because this heavy-tailed distribution is better at capturing abrupt changes in noise than Gaussian, thus reducing false alarms. Experiments are carried out on a dataset collected from an EEG-monitoring brain-machine interface system that contains 10 patients and 367 hours of EEG data. The authors' method achieves a high sensitivity of 100 percent with a low FDR of 0. 08 per hour and a median time delay of 8. 10 seconds, demonstrating the method's effectiveness.

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TIST Journal 2015 Journal Article

City-Scale Social Event Detection and Evaluation with Taxi Traces

  • Wangsheng Zhang
  • Guande Qi
  • Gang Pan
  • Hua Lu
  • Shijian Li
  • Zhaohui Wu

A social event is an occurrence that involves lots of people and is accompanied by an obvious rise in human flow. Analysis of social events has real-world importance because events bring about impacts on many aspects of city life. Traditionally, detection and impact measurement of social events rely on social investigation, which involves considerable human effort. Recently, by analyzing messages in social networks, researchers can also detect and evaluate country-scale events. Nevertheless, the analysis of city-scale events has not been explored. In this article, we use human flow dynamics, which reflect the social activeness of a region, to detect social events and measure their impacts. We first extract human flow dynamics from taxi traces. Second, we propose a method that can not only discover the happening time and venue of events from abnormal social activeness, but also measure the scale of events through changes in such activeness. Third, we extract traffic congestion information from traces and use its change during social events to measure their impact. The results of experiments validate the effectiveness of both the event detection and impact measurement methods.

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IS Journal 2014 Journal Article

Pervasive Service Bus: Smart SOA Infrastructure for Ambient Intelligence

  • Gang Pan
  • Li Zhang
  • Zhaohui Wu
  • Shijian Li
  • Laurence Yang
  • Man Lin
  • Yuanchun Shi

Ambient intelligence (AmI) aims to make our everyday environments intelligent--that is, sensitive, adaptive, and responsive to the presence of people--in a transparent manner. Several challenges exist to building an efficient infrastructure for AmI, including interoperation of heterogeneous systems, intelligence for anticipatory user assistance, adaptability to dynamic environments for good user experience, and scalability to additional users and spaces. Here, the authors propose Pervasive Service Bus (PSB), a smart service-oriented architecture (SOA) framework for AmI spaces that models all computing activities as unified pervasive services. They present an online planning algorithm to adapt service flows to contexts and user tasks. PSB employs a sub-bus-based layout to maintain efficiency in large-scale service interactions. They also discuss their results in evaluating PSB's performance in a Smart Home testbed.

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IJCAI Conference 2013 Conference Paper

Online Community Detection for Large Complex Networks

  • Wangsheng Zhang
  • Gang Pan
  • Zhaohui Wu
  • Shijian Li

Complex networks describe a wide range of systems in nature and society. To understand the complex networks, it is crucial to investigate their internal structure. In this paper, we propose an online community detection method for large complex networks, which make it possible to process networks edge-by-edge in a serial fashion. We investigate the generative mechanism of complex networks and propose a split mechanism based on the degree of the nodes to create new community. Our method has linear time complexity. The method has been applied to six real-world network datasets and the experimental results show that it is comparable to existing methods in modularity with much less running time.

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IS Journal 2013 Journal Article

The Convergence of Machine and Biological Intelligence

  • Zhaohui Wu
  • Raj Reddy
  • Gang Pan
  • Nenggan Zheng
  • Paul F.M.J. Verschure
  • Qiaosheng Zhang
  • Xiaoxiang Zheng
  • Jose C. Principe

To explore the exciting new domain of brain informatics, we invited several well-known experts to discuss the state of the art, the challenges, the opportunities, and the trends. In "Creating Human-Level AI by Educating a Child Machine, " Raj Reddy proposes an architecture for a "child machine" that can learn and is teachable. In "Cyborg Intelligence, " Zhaohui Wu, Gang Pan, and Nenggan Zheng describe a biological-machine system consisting of both an organic and a computing part. In "Formal Minds and Biological Brains II: From the Mirage of Intelligence to a Science and Engineering of Consciousness, " Paul F. M. J. Verschure discusses human-like cognitive architectures and describes the Distributed Adaptive Control (DAC) architecture for perception, cognition, and action. In "The Challenges of Closed-Loop Invasive Brain-Machine Interfaces, " Qiaosheng Zhang and Xiaoxiang Zheng discuss the challenges and trends in closed-loop brain-machine interfaces. In "Neural Signal Processing in Brain-Machine Interfaces, " Jose C. Principe takes a critical look at the challenges and opportunities of performing computation with pulses, as neurons do. In "Neuroprosthesis Control via a Noninvasive Hybrid Brain-Computer Interface, " Alex Kreilinger, Martin Rohm, Vera Kaiser, Robert Leeb, Rüdiger Rupp, and Gernot R. Müller-Putz describe an example of the convergence of biological intelligence and machine intelligence in a hand-elbow neuroprosthesis control unit.

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IS Journal 2011 Journal Article

TaskShadow: Toward Seamless Task Migration across Smart Environments

  • Gang Pan
  • Yuqiong Xu
  • Zhaohui Wu
  • Shijian Li
  • Laurence Yang
  • Man Lin
  • Zhong Liu

The OSGi-based platform TaskShadow supports seamless task migration across smart environments using a task-to-service mapping algorithm to semantically search for suitable low-level services that achieve high-level tasks.

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v2026.09.13