用Transformer模型双向生成体外与体内神经元放电数据，提升研究精度与伦理水平。
In vitro 2 In vivo : Bidirectional and High-Precision Generation of In Vitro and In Vivo Neuronal Spike Data
- 采用Transformer结合Dice损失函数，实现体外与体内神经数据的高精度双向生成。
- 通过结构分析揭示关键脑区作用，验证了该方法在跨域生成中的有效性。
- 适用于神经科学研究中的替代动物实验，推动更伦理高效的研究方法。
神经元以二进制方式编码信息并处理复杂信号，但预测或生成多样化的神经活动模式仍具挑战性。体外与体内研究各有优势，但缺乏能无缝整合两类数据的计算框架。本文引入广泛应用于大语言模型的Transformer模型处理神经数据，并针对二进制特性提出Dice损失函数，实现跨域神经活动的精准生成。结构分析揭示了该损失函数如何促进学习，并识别出支持高精度生成的关键脑区。研究结果支持动物实验中的3R原则（尤其替代原则），建立连接动物实验与人类临床研究的数学框架。本工作推进数据驱动型神经科学与神经活动建模，为更伦理、高效的实验方法铺平道路。
原文摘要 · Abstract (English)
Neurons encode information in a binary manner and process complex signals. However, predicting or generating diverse neural activity patterns remains challenging. In vitro and in vivo studies provide distinct advantages, yet no robust computational framework seamlessly integrates both data types. We address this by applying the Transformer model, widely used in large-scale language models, to neural data. To handle binary data, we introduced Dice loss, enabling accurate cross-domain neural activity generation. Structural analysis revealed how Dice loss enhances learning and identified key brain regions facilitating high-precision data generation. Our findings support the 3Rs principle in animal research, particularly Replacement, and establish a mathematical framework bridging animal experiments and human clinical studies. This work advances data-driven neuroscience and neural activity modeling, paving the way for more ethical and effective experimental methodologies.
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