Linguistics and Human Brain: A Perspective of Computational Neuroscience

• URL: http://arxiv.org/abs/2602.08275v2
• Date: Fri, 13 Feb 2026 13:15:58 GMT
• Title: Linguistics and Human Brain: A Perspective of Computational Neuroscience
• Authors: Fudong Zhang, Bo Chai, Yujie Wu, Wai Ting Siok, Nizhuan Wang,
• Abstract summary: Computational neuroscience formalizes the hierarchical and dynamic structures of language into testable neural models.<n>Recent advances in deep learning have powerfully advanced this pursuit.<n>"Model-brain alignment" framework offers a methodology to evaluate the biological plausibility of language-related theories.
• Score: 2.8285202282959268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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