Real-world graphs or networks are usually heterogeneous, involving multiple types of nodes and relationships. Heterogeneous graph neural networks (HGNNs) can effectively handle these diverse nodes and edges, capturing heterogeneous information within the graph, thus exhibiting outstanding performance. However, most methods of HGNNs usually involve complex structural designs, leading to problems such as high memory usage, long inference time, and extensive consumption of computing resources. These limitations pose certain challenges for the practical application of HGNNs, especially for resource-constrained devices. To mitigate this issue, we propose the Spiking Heterogeneous Graph Attention Networks (SpikingHAN), which incorporates the brain-inspired and energy-saving properties of Spiking Neural Networks (SNNs) into heterogeneous graph learning to reduce the computing cost without compromising the performance. Specifically, SpikingHAN aggregates metapath-based neighbor information using a single-layer graph convolution with shared parameters. It then employs a semantic-level attention mechanism to capture the importance of different meta-paths and performs semantic aggregation. Finally, it encodes the heterogeneous information into a spike sequence through SNNs, simulating bioinformatic processing to derive a binarized 1-bit representation of the heterogeneous graph. Comprehensive experimental results from three real-world heterogeneous graph datasets show that SpikingHAN delivers competitive node classification performance. It achieves this with fewer parameters, quicker inference, reduced memory usage, and lower energy consumption. Code is available at https://github.com/QianPeng369/SpikingHAN.
SpikeGraphormer: A High-Performance Graph Transformer with Spiking Graph Attention
School of Astronautics, Harbin Institute of Technology（哈尔滨工业大学航天学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；HebeiGejun Science & Technology Company（河北格钧科技公司）；School of Computer Science & Statistics, Trinity College Dublin（都柏林三一学院计算机与统计学院）
Center for Brain-Inspired Computing Research (CBICR)（类脑计算研究中心）；Tsinghua University（清华大学）
Biological spiking neurons with intrinsic dynamics underlie the powerful representation and learning capabilities of the brain for processing multimodal information in complex environments. Despite recent tremendous progress in spiking neural networks (SNNs) for handling Euclidean-space tasks, it still remains challenging to exploit SNNs in processing non-Euclidean-space data represented by graph data, mainly due to the lack of effective modeling framework and useful training techniques. Here we present a general spike-based modeling framework that enables the direct training of SNNs for graph…
Graph Attention Networks (GATs) have been intensively studied and widely used in graph data learning tasks. Existing GATs generally adopt the self-attention mechanism to conduct graph edge attention learning, requiring expensive computation. It is known that Spiking Neural Networks (SNNs) can perform inexpensive computation by transmitting the input signal data into discrete spike trains and can also return sparse outputs. Inspired by the merits of SNNs, in this work, we propose a novel Graph Spiking Attention Network (GSAT) for graph data representation and learning. In contrast to self-atten…
Spiking Neural Networks are often touted as brain-inspired learning models for the third wave of Artificial Intelligence. Although recent SNNs trained with supervised backpropagation show classification accuracy comparable to deep networks, the performance of unsupervised learning-based SNNs remains much lower. This paper presents a heterogeneous recurrent spiking neural network (HRSNN) with unsupervised learning for spatio-temporal classification of video activity recognition tasks on RGB (KTH, UCF11, UCF101) and event-based datasets (DVS128 Gesture). The key novelty of the HRSNN is that the …
BiSpikCLM: A Spiking Language Model integrating Softmax-Free Spiking Attention and Spike-Aware Alignment Distillation
School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, China（电子工程学院，深圳研究生院，北京大学，深圳，中国）；Peng Cheng Laboratory, Shenzhen, China（鹏城实验室，深圳，中国）；Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学，深圳，中国）
Breaking Global Self-Attention Bottlenecks in Transformer-based Spiking Neural Networks with Local Structure-Aware Self-Attention
School of Future Technology, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University（未来技术学院、天津认知计算与应用重点实验室、天津大学）；School of Artificial Intelligence, Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University（人工智能学院、天津认知计算与应用重点实验室、智能与计算学院、天津大学）
Neural Dynamics Self-Attention for Spiking Transformers
University of Electronic Science and Technology of China（电子科技大学）；Shenzhen Loop Area Institute（深圳河套学院）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））
Lapis: Laplacian Spiking Attention via First-Spike Timing and Membrane Leakage
National University of Singapore（新加坡国立大学）；Sea AI Lab（海智实验室）；Shanghai Jiaotong University（上海交通大学）；Shanghai Advanced Research Institute, Chinese Academy of Sciences（中国科学院上海高等研究院）；University of Chinese Academy of Sciences, Beijing（中国科学院大学）
Laboratory of Biological Modeling, NIDDK/NIH（美国国立糖尿病、消化系统疾病和肾病研究所（NIDDK）/美国国立卫生研究院（NIH）生物建模实验室）
Spiking activity of neurons engaged in learning and performing a task show complex spatiotemporal dynamics. While the output of recurrent network models can learn to perform various tasks, the possible range of recurrent dynamics that emerge after learning remains unknown. Here we show that modifying the recurrent connectivity with a recursive least squares algorithm provides sufficient flexibility for synaptic and spiking rate dynamics of spiking networks to produce a wide range of spatiotemporal activity. We apply the training method to learn arbitrary firing patterns, stabilize irregular sp…
Peking University（北京大学）；Peng Cheng Laboratory（鹏城实验室）；Sea AI Lab（Sea人工智能实验室）；Shenzhen EEGSmart Technology Co., Ltd.（深圳脑陆科技有限公司）
We consider two biologically plausible structures, the Spiking Neural Network (SNN) and the self-attention mechanism. The former offers an energy-efficient and event-driven paradigm for deep learning, while the latter has the ability to capture feature dependencies, enabling Transformer to achieve good performance. It is intuitively promising to explore the marriage between them. In this paper, we consider leveraging both self-attention capability and biological properties of SNNs, and propose a novel Spiking Self Attention (SSA) as well as a powerful framework, named Spiking Transformer (Spik…
Visual attention can be defined as the behavioral and cognitive process of selectively focusing on a discrete aspect of sensory cues while disregarding other perceivable information. This biological mechanism, more specifically saliency detection, has long been used in multimedia indexing to drive the analysis only on relevant parts of images or videos for further processing. The recent advent of silicon retinas (or event cameras -- sensors that measure pixel-wise changes in brightness and output asynchronous events accordingly) raises the question of how to adapt attention and saliency to the…
Attention via Synaptic Plasticity is All You Need: A Biologically Inspired Spiking Neuromorphic Transformer
Department of Electronics and Communication Engineering, National Institute of Technology Allahabad, Prayagraj, 211004, India（国家理工学院阿姆利达分校电子与通信工程系）；Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India（印度理工学院罗奥基分校纳米技术中心）
FSTA-SNN:Frequency-based Spatial-Temporal Attention Module for Spiking Neural Networks
Zhejiang University-University of Illinois Urbana Champaign Institute（浙江大学-伊利诺伊大学厄巴纳-香槟分校研究院）；The College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）
University of the West of England（西英格兰大学）；Politecnico di Milano（米兰理工大学）
Learning Classifier Systems (LCS) are population-based reinforcement learners used in a wide variety of applications. This paper presents a LCS where each traditional rule is represented by a spiking neural network, a type of network with dynamic internal state. We employ a constructivist model of growth of both neurons and dendrites that realise flexible learning by evolving structures of sufficient complexity to solve a well-known problem involving continuous, real-valued inputs. Additionally, we extend the system to enable temporal state decomposition. By allowing our LCS to chain together …
Differentiable architecture search with multi-dimensional attention for spiking neural networks
Nanjing Institute of Intelligent Technology（南京智能技术研究院）；School of Integrated Circuits, University of Chinese Academy of Sciences（中国科学院大学集成电路学院）；University of Chinese Academy of Sciences (UCAS)（中国科学院大学）；Institute of Microelectronics, Chinese Academy of Sciences（中国科学院微电子研究所）
SpiLiFormer: Enhancing Spiking Transformers with Lateral Inhibition
Zhejiang University（浙江大学）；Westlake University（西湖大学）；Nanjing University（南京大学）；Donghua University（东华大学）；University of Electronic Science and Technology of China（电子科技大学）；Peking University（北京大学）
Parallel Time-Aligned Spiking Self-Attention for Consistent Integer-Valued Training and Spike-Driven Inference
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Pengcheng Laboratory（鹏城实验室）；Shenzhen Graduate School, Peking University（北京大学深圳研究生院）；School of Computer Science, Peking University（北京大学计算机学院）；University of Chinese Academy of Sciences（中国科学院大学）
Loss shaping enhances exact gradient learning with Eventprop in spiking neural networks
School of Engineering and Informatics, University of Sussex（萨塞克斯大学工程与信息学院）；Information & Communication Technologies, Imperial College London（伦敦帝国理工学院信息与通信技术系）
Purdue University（普渡大学）；Nanoelectronics Research Laboratory（纳米电子学研究实验室）
Stochastic gradient descent requires that training samples be drawn from a uniformly random distribution of the data. For a deployed system that must learn online from an uncontrolled and unknown environment, the ordering of input samples often fails to meet this criterion, making lifelong learning a difficult challenge. We exploit the locality of the unsupervised Spike Timing Dependent Plasticity (STDP) learning rule to target local representations in a Spiking Neural Network (SNN) to adapt to novel information while protecting essential information in the remainder of the SNN from catastroph…
Spiking neural networks (SNNs) have received significant attention for their biological plausibility. SNNs theoretically have at least the same computational power as traditional artificial neural networks (ANNs). They possess potential of achieving energy-efficiency while keeping comparable performance to deep neural networks (DNNs). However, it is still a big challenge to train a very deep SNN. In this paper, we propose an efficient approach to build a spiking version of deep residual network (ResNet). ResNet is considered as a kind of the state-of-the-art convolutional neural networks (CNNs…
Peter Grünberg Institute（彼得·格林贝格研究所）；Forschungszentrum Jülich（于利希研究中心）；RWTH（亚琛工业大学）
Transformers are state-of-the-art networks for most sequence processing tasks. However, the self-attention mechanism often used in Transformers requires large time windows for each computation step and thus makes them less suitable for online signal processing compared to Recurrent Neural Networks (RNNs). In this paper, instead of the self-attention mechanism, we use a sliding window attention mechanism. We show that this mechanism is more efficient for continuous signals with finite-range dependencies between input and target, and that we can use it to process sequences element-by-element, th…
