9 papers
BrainTaskonomy: Learning How to Pretrain and What to Transfer in fMRI Foundation Models
Junfeng Xia, Wenhao Ye, Junxiang Zhang +3
fMRI foundation models increasingly aggregate heterogeneous data across brain states, cohorts, and acquisition settings, yet pretraining domains are commonly treated as a flat mixt…
Complexity and Stability of Neural Activity Across Aging and Neurodegenerative Disease
Junjie Yu, Jianyu Zhang, Zian Pei +5
Objective: EEG signals fluctuate continuously even within a fixed cognitive state, but an important question is whether the brain still reuses similar activity patterns to represen…
Understanding and Correcting Low-Frequency Bias in EEG Foundation Model
Junjie Yu, Zihan Deng, Jianyu Zhang +8
Increasing EEG pretraining data scale or model capacity does not consistently improve downstream performance. We identify a persistent low-frequency bias in representations learned…
NeuroOnline: Bridging Pretraining and Online Adaptation for EEG Foundation Models
Weibin Li, Wendu Li, Yushan You +2
EEG foundation models have shown strong potential in learning generalized representations across subjects and tasks. However, most existing approaches follow a pretraining-static d…
Pretraining Induces a Reusable Spectral Basis for Downstream Task Adaptation
Junjie Yu, Yue Wang, Zihan Deng +3
Finetuning pretrained models occurs in a low-dimensional subspace of the full parameter space. Prior work has focused on characterizing this optimization subspace, but largely igno…
Omni-fMRI: A Universal Atlas-Free fMRI Foundation Model
Mo Wang, Wenhao Ye, Junfeng Xia +6
Self-supervised fMRI foundation models have shown promising transfer performance, yet most rely on predefined region-level parcellations that discard fine-grained voxel information…
Understanding Generalization from Embedding Dimension and Distributional Convergence
Junjie Yu, Zhuoli Ouyang, Haotian Deng +5
Deep neural networks often generalize well despite heavy over-parameterization, challenging classical parameter-based analyses. We study generalization from a representation-centri…
Local Intrinsic Dimension of Representations Predicts Alignment and Generalization in AI Models and Human Brain
Junjie Yu, Wenxiao Ma, Chen Wei +4
Recent work has found that neural networks with stronger generalization tend to exhibit higher representational alignment with one another across architectures and training paradig…
Scale-Invariance Drives Convergence in AI and Brain Representations
Junjie Yu, Wenxiao Ma, Jianyu Zhang +4
Despite variations in architecture and pretraining strategies, recent studies indicate that large-scale AI models often converge toward similar internal representations that also a…