12 papers
Spiking Neural Networks for fMRI-Based Visual Semantic Decoding
Jiahong Zhang, Jinning Zhao, Sijun Shen +3
Functional magnetic resonance imaging (fMRI)-based visual decoding aims to recover visual information from measured brain activity, commonly by mapping fMRI responses into latent v…
Beyond Transformers: Linear Attention Policy for Open-Vocabulary Object Goal Navigation
Jiahong Zhang, Yifan Lin, Yandong Zhang +6
Open-Vocabulary Object Goal Navigation (OVON) requires agents to operate under partial observability, making effective internal state updates critical for navigation performance. T…
SpikingBrain: Spiking Brain-inspired Large Models
Yuqi Pan, Yupeng Feng, Jinghao Zhuang +16
Mainstream Transformer-based large language models face major efficiency bottlenecks: training computation scales quadratically with sequence length, and inference memory grows lin…
SpikingBrain2.0: Brain-Inspired Foundation Models for Efficient Long-Context and Cross-Platform Inference
Yuqi Pan, Jinghao Zhuang, Yupeng Feng +16
Scaling context length is reshaping large-model development, yet full-attention Transformers suffer from prohibitive computation and inference bottlenecks at long sequences. A key…
SpikeMLLM: Spike-based Multimodal Large Language Models via Modality-Specific Temporal Scales and Temporal Compression
Han Xu, Zhiyong Qin, Di Shang +6
Multimodal Large Language Models (MLLMs) have achieved remarkable progress but incur substantial computational overhead and energy consumption during inference, limiting deployment…
Spike-driven Large Language Model
Han Xu, Xuerui Qiu, Baiyu Chen +7
Current Large Language Models (LLMs) are primarily based on large-scale dense matrix multiplications. Inspired by the brain's information processing mechanism, we explore the funda…