1 code implementation • 14 Feb 2024 • Qiongyi Zhou, Changde Du, Shengpei Wang, Huiguang He
Although prior multi-subject decoding methods have made significant progress, they still suffer from several limitations, including difficulty in extracting global neural response features, linear scaling of model parameters with the number of subjects, and inadequate characterization of the relationship between neural responses of different subjects to various stimuli.
1 code implementation • 8 Aug 2023 • Yizhuo Lu, Changde Du, Qiongyi Zhou, Dianpeng Wang, Huiguang He
In Stage 2, we utilize the CLIP visual feature decoded from fMRI as supervisory information, and continually adjust the two feature vectors decoded in Stage 1 through backpropagation to align the structural information.
1 code implementation • 17 Aug 2022 • Haoyu Lu, Qiongyi Zhou, Nanyi Fei, Zhiwu Lu, Mingyu Ding, Jingyuan Wen, Changde Du, Xin Zhao, Hao Sun, Huiguang He, Ji-Rong Wen
Further, from the perspective of neural encoding (based on our foundation model), we find that both visual and lingual encoders trained multimodally are more brain-like compared with unimodal ones.