87 citations · 147 across the 24 of their papers we have counts for
25 papers
Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence
Xiang He, Dongcheng Zhao, Yang Li +3
Multimodal learning enhances the perceptual capabilities of cognitive systems by integrating information from different sensory modalities. However, existing multimodal fusion rese…
Similarity-based context aware continual learning for spiking neural networks
Bing Han, Feifei Zhao, Yang Li +3
Biological brains have the capability to adaptively coordinate relevant neuronal populations based on the task context to learn continuously changing tasks in real-world environmen…
CACE-Net: Co-guidance Attention and Contrastive Enhancement for Effective Audio-Visual Event Localization
Xiang He, Xiangxi Liu, Yang Li +5
The audio-visual event localization task requires identifying concurrent visual and auditory events from unconstrained videos within a network model, locating them, and classifying…
Directly Training Temporal Spiking Neural Network with Sparse Surrogate Gradient
Yang Li, Feifei Zhao, Dongcheng Zhao +1
Brain-inspired Spiking Neural Networks (SNNs) have attracted much attention due to their event-based computing and energy-efficient features. However, the spiking all-or-none natur…
Spiking Neural Networks with Consistent Mapping Relations Allow High-Accuracy Inference
Yang Li, Xiang He, Qingqun Kong +1
Spike-based neuromorphic hardware has demonstrated substantial potential in low energy consumption and efficient inference. However, the direct training of deep spiking neural netw…
EventZoom: A Progressive Approach to Event-Based Data Augmentation for Enhanced Neuromorphic Vision
Yiting Dong, Xiang He, Guobin Shen +3
Dynamic Vision Sensors (DVS) capture event data with high temporal resolution and low power consumption, presenting a more efficient solution for visual processing in dynamic and r…
Matrix-Transformation Based Low-Rank Adaptation (MTLoRA): A Brain-Inspired Method for Parameter-Efficient Fine-Tuning
Yao Liang, Yuwei Wang, Yang Li +1
Fine-tuning techniques based on Large Pretrained Language Models (LPLMs) have been proven to significantly enhance model performance on a variety of downstream tasks and effectivel…
Brain-inspired and Self-based Artificial Intelligence
Yi Zeng, Feifei Zhao, Yuxuan Zhao +17
The question "Can machines think?" and the Turing Test to assess whether machines could achieve human-level intelligence is one of the roots of AI. With the philosophical argument…
Parallel Spiking Unit for Efficient Training of Spiking Neural Networks
Yang Li, Yinqian Sun, Xiang He +3
Efficient parallel computing has become a pivotal element in advancing artificial intelligence. Yet, the deployment of Spiking Neural Networks (SNNs) in this domain is hampered by…
Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling
Guobin Shen, Dongcheng Zhao, Yiting Dong +4
Within the complex neuroarchitecture of the brain, astrocytes play crucial roles in development, structure, and metabolism. These cells regulate neural activity through tripartite…
Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks
Guobin Shen, Dongcheng Zhao, Yiting Dong +3
The evolution of the human brain has led to the development of complex synaptic plasticity, enabling dynamic adaptation to a constantly evolving world. This progress inspires our e…
Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition
Yiting Dong, Yang Li, Dongcheng Zhao +2
The prevalence of violence in daily life poses significant threats to individuals' physical and mental well-being. Using surveillance cameras in public spaces has proven effective…
Improving Stability and Performance of Spiking Neural Networks through Enhancing Temporal Consistency
Dongcheng Zhao, Guobin Shen, Yiting Dong +2
Spiking neural networks have gained significant attention due to their brain-like information processing capabilities. The use of surrogate gradients has made it possible to train…
Dive into the Power of Neuronal Heterogeneity
Guobin Shen, Dongcheng Zhao, Yiting Dong +2
The biological neural network is a vast and diverse structure with high neural heterogeneity. Conventional Artificial Neural Networks (ANNs) primarily focus on modifying the weight…
MSAT: Biologically Inspired Multi-Stage Adaptive Threshold for Conversion of Spiking Neural Networks
Xiang He, Yang Li, Dongcheng Zhao +2
Spiking Neural Networks (SNNs) can do inference with low power consumption due to their spike sparsity. ANN-SNN conversion is an efficient way to achieve deep SNNs by converting we…
An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain
Xiang He, Dongcheng Zhao, Yang Li +3
Spiking neural networks (SNNs) are rich in spatio-temporal dynamics and are suitable for processing event-based neuromorphic data. However, event-based datasets are usually less an…
Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentation
Yang Li, Xiang He, Yiting Dong +2
Spiking neural network (SNN) has been attached to great importance due to the properties of high biological plausibility and low energy consumption on neuromorphic hardware. As an…
BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation
Yi Zeng, Dongcheng Zhao, Feifei Zhao +16
Spiking neural networks (SNNs) have attracted extensive attentions in Brain-inspired Artificial Intelligence and computational neuroscience. They can be used to simulate biological…
An Unsupervised STDP-based Spiking Neural Network Inspired By Biologically Plausible Learning Rules and Connections
Yiting Dong, Dongcheng Zhao, Yang Li +1
The backpropagation algorithm has promoted the rapid development of deep learning, but it relies on a large amount of labeled data and still has a large gap with how humans learn.…
Solving the Spike Feature Information Vanishing Problem in Spiking Deep Q Network with Potential Based Normalization
Yinqian Sun, Yi Zeng, Yang Li
Brain inspired spiking neural networks (SNNs) have been successfully applied to many pattern recognition domains. The SNNs based deep structure have achieved considerable results i…
Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes
Yang Li, Yi Zeng
Spiking neural network (SNN), as a brain-inspired energy-efficient neural network, has attracted the interest of researchers. While the training of spiking neural networks is still…
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning
Yang Li, Yiting Dong, Dongcheng Zhao +1
Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain. However, the current artificial intelligence systems meet diffi…
Spiking CapsNet: A Spiking Neural Network With A Biologically Plausible Routing Rule Between Capsules
Dongcheng Zhao, Yang Li, Yi Zeng +2
Spiking neural network (SNN) has attracted much attention due to their powerful spatio-temporal information representation ability. Capsule Neural Network (CapsNet) does well in as…
BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons
Dongcheng Zhao, Yi Zeng, Yang Li
Spiking neural networks (SNNs) transmit information through discrete spikes, which performs well in processing spatial-temporal information. Due to the non-differentiable character…