5 papers
FPED: A Functional-Network Prior-Guided Mixture-of-Experts Framework for Interpretable Brain Decoding
Yudan Ren, Pengcheng Shi, Zihan Ma +2
Visual image reconstruction from functional Magnetic Resonance Imaging (fMRI) is a fundamental task in brain decoding, providing a crucial pathway for understanding human perceptua…
HyNeuralMap: Hyperbolic Mapping of Visual Semantics to Neural Hierarchies
Zihan Ma, Tian Xia, Kexin Wang +3
Understanding the intricate mappings between visual stimuli and neural responses is a fundamental challenge in cognitive neuroscience. While current approaches predominantly align…
BrainMCLIP: Brain Image Decoding with Multi-Layer feature Fusion of CLIP
Tian Xia, Zihan Ma, Xinlong Wang +4
Decoding images from fMRI often involves mapping brain activity to CLIP's final semantic layer. To capture finer visual details, many approaches add a parameter-intensive VAE-based…
Uncovering Brain-Like Hierarchical Patterns in Vision-Language Models through fMRI-Based Neural Encoding
Yudan Ren, Xinlong Wang, Kexin Wang +6
While brain-inspired artificial intelligence(AI) has demonstrated promising results, current understanding of the parallels between artificial neural networks (ANNs) and human brai…
Analyzing Nobel Prize Literature with Large Language Models
Zhenyuan Yang, Zhengliang Liu, Jing Zhang +19
This study examines the capabilities of advanced Large Language Models (LLMs), particularly the o1 model, in the context of literary analysis. The outputs of these models are compa…