7 papers
UniBCI: Towards a Unified Pretrained Model for Invasive Brain-Computer Interfaces
Binjie Hong, Rui Xiong, Liyuan Han +1
Modeling invasive neural spike data is fundamental to advancing high-performance brain-computer interfaces (BCIs). However, existing approaches face critical challenges, including…
TaiBai: A fully programmable brain-inspired processor with topology-aware efficiency
Qianpeng Li, Yu Song, Xin Liu +6
Brain-inspired computing has emerged as a promising paradigm to overcome the energy-efficiency limitations of conventional intelligent systems by emulating the brain's partitioned…
Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding
Yu Song, Liyuan Han, Bo Xu +1
Brain-computer interfaces (BCIs) are an advanced fusion of neuroscience and artificial intelligence, requiring stable and long-term decoding of neural signals. Spiking Neural Netwo…
Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks
Tianyu Zheng, Liyuan Han, Tielin Zhang
Spiking neural networks (SNNs) are gaining popularity in the computational simulation and artificial intelligence fields owing to their biological plausibility and computational ef…
Motif-topology improved Spiking Neural Network for the Cocktail Party Effect and McGurk Effect
Shuncheng Jia, Tielin Zhang, Ruichen Zuo +1
Network architectures and learning principles are playing key in forming complex functions in artificial neural networks (ANNs) and spiking neural networks (SNNs). SNNs are conside…
Motif-topology and Reward-learning improved Spiking Neural Network for Efficient Multi-sensory Integration
Shuncheng Jia, Ruichen Zuo, Tielin Zhang +2
Network architectures and learning principles are key in forming complex functions in artificial neural networks (ANNs) and spiking neural networks (SNNs). SNNs are considered the…