Topic: q-bio.NC

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1. Training-Driven Representational Geometry Modularization Predicts Brain Alignment in Language Models

   Paper • Feb 11, 2026 • arXiv • Yixuan Liu, Zhiyuan Ma, Likai Tang, Runmin Gan, Xinche Zhang, Jinhao Li, Chao Xie, Sen Song

   The degree to which Large Language Models (LLMs) align with human brain activity during language processing remains a central question in both AI and neuroscience. We investigate the impact of trai...

2. MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training

   Paper • Feb 12, 2026 • arXiv • Dulhan Jayalath, Oiwi Parker Jones

   Decoding natural language from non-invasive brain recordings like Magnetoencephalography (MEG) remains a significant challenge due to the low signal-to-noise ratio and the scarcity of paired brain-...

3. Recovering Whole-Brain Causal Connectivity under Indirect Observation with Applications to Human EEG and fMRI

   Paper • Feb 11, 2026 • arXiv • Sangyoon Bae, Miruna Oprescu, David Keetae Park, Shinjae Yoo, Jiook Cha

   Recovering the true causal connectivity of the human brain from indirect observations like EEG and fMRI is a fundamental but ill-posed problem due to the presence of unobserved confounders and the ...

4. Learning Glioblastoma Tumor Heterogeneity Using Brain Inspired Topological Neural Networks

   Paper • Feb 12, 2026 • arXiv • Ankita Paul, Wenyi Wang

   Glioblastoma (GBM) is characterized by high intra-tumoral heterogeneity, which poses a significant challenge for diagnosis and treatment planning. We propose a brain-inspired Topological Neural Net...

5. Linguistics and Human Brain: A Perspective of Computational Neuroscience

   Paper • Feb 10, 2026 • arXiv • Fudong Zhang, Bo Chai, Yujie Wu, Wai Ting Siok, Nizhuan Wang

   This paper provides a comprehensive perspective on the intersection of linguistics and computational neuroscience, exploring how modern language models and neural recording technologies can bridge ...