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.

翻译：神经元以二进制方式编码信息并处理复杂信号。然而，预测或生成多样化的神经活动模式仍然具有挑战性。体外和体内研究各具优势，但目前缺乏能够无缝整合这两种数据类型的稳健计算框架。我们通过将广泛应用于大规模语言模型的Transformer架构应用于神经数据来解决这一问题。为处理二进制数据，我们引入了Dice损失函数，实现了精确的跨域神经活动生成。结构分析揭示了Dice损失如何增强学习能力，并识别出促进高精度数据生成的关键脑区。我们的研究结果支持动物研究中的3R原则（特别是替代原则），并建立了连接动物实验与人类临床研究的数学框架。这项工作推动了数据驱动神经科学和神经活动建模的发展，为更符合伦理且高效的实验方法铺平了道路。