用神经数据解码雀鸟鸣叫，提出新方法提升准确性。
neuro2voc: Decoding Vocalizations from Neural Activity
- 用XGBoost+SHAP分析脉冲率，发现鸣叫分类的关键神经互动模式。
- 用GPT2分词与Mamba2架构，首次实现基于脉冲的鸣叫解码。
- 结合对比学习与变分自编码器，成功从神经数据生成声谱图。
由于神经脉冲的稀疏性与脑回路的复杂性，准确解码神经脉冲序列并关联其与运动输出是一项挑战。本硕士项目研究了从入侵式神经记录（使用Neuropixels）中解码斑胸草雀运动输出（包括离散音节和连续声谱图）的实验方法。主要成果包括：(1) 使用XGBoost结合SHAP分析脉冲率，揭示了音节分类中关键的神经元相互作用模式；(2) 提出一种新型方法（用GPT2对神经数据进行分词）与架构（Mamba2），展示了基于脉冲解码音节的潜力；(3) 构建联合对比学习-变分自编码器框架，成功从分箱神经数据生成声谱图。该工作为复杂运动输出的神经解码奠定了良好基础，并提供了处理稀疏神经数据的新方法。
原文摘要 · Abstract (English)
Accurate decoding of neural spike trains and relating them to motor output is a challenging task due to the inherent sparsity and length in neural spikes and the complexity of brain circuits. This master project investigates experimental methods for decoding zebra finch motor outputs (in both discrete syllables and continuous spectrograms), from invasive neural recordings obtained from Neuropixels. There are three major achievements: (1) XGBoost with SHAP analysis trained on spike rates revealed neuronal interaction patterns crucial for syllable classification. (2) Novel method (tokenizing neural data with GPT2) and architecture (Mamba2) demonstrated potential for decoding of syllables using spikes. (3) A combined contrastive learning-VAE framework successfully generated spectrograms from binned neural data. This work establishes a promising foundation for neural decoding of complex motor outputs and offers several novel methodological approaches for processing sparse neural data.
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