Building Interpretable, Trust-worthy Systems for Neural Signal Decoding

While deep learning excels at decoding neural signals, the opacity of state-of-the-art models limits their scientific utility and clinical trustworthiness. We propose a research that bridges this gap by integrating high-performance architectures—specifically Transformers and Graph Neural Networks—with mechanistic interpretability and neuro-symbolic reasoning. This proposal aims to uncover verifiable mappings between artificial computational circuits and biological dynamics without compromising decoding accuracy. Validated through rigorous benchmarking and wet-lab experiments, this work establishes a foundation for transparent brain-computer interfaces and accelerates fundamental neuroscience research.

Authors

• Hua Xu Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China Beijing National Research Center for Information Science and Technology (BNRist), Beijing 100084, China

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