mlsanityData to 14 Oct 2026

Linguistics and Human Brain: A Perspective of Computational Neuroscience

arXiv 2602.08275 ↗First posted 9 Feb 2026q-bio.NC cs.CLcited by 4

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Abstract (arXiv)

Elucidating the language-brain relationship requires bridging the methodological gap between the abstract theoretical frameworks of linguistics and the empirical neural data of neuroscience. Serving as an interdisciplinary cornerstone, computational neuroscience formalizes the hierarchical and dynamic structures of language into testable neural models through modeling, simulation, and data analysis. This enables a computational dialogue between linguistic hypotheses and neural mechanisms. Recent advances in deep learning, particularly large language models (LLMs), have powerfully advanced this pursuit. Their high-dimensional representational spaces provide a novel scale for exploring the neural basis of linguistic processing, while the "model-brain alignment" framework offers a methodology to evaluate the biological plausibility of language-related theories.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 66 of its 302 references that are in the map. First posted on arXiv 9 Feb 2026.

Builds on

66 of 302 references.

References · 66

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38 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026HDND: Hierarchical Dynamic Neural Decoding for Multilingual Word/Character Retrieval from Non-Invasive Brain Recordingscited by 1
  2. 2026TGSD: Topology-Guided State-Space Diffusion Framework for EEG Spatial Super-Resolutioncited by 0
  3. 2026SUP-MCRL: Subject-aware Unified Pseudo-feature Coded Multimodal Contrastive Representation Learning for EEG Visual Decodingcited by 0
  4. 2026The Score Is Not the Structure: Brain Alignment and Cross-Lingual Transfercited by 0