In vitro 2 In vivo：in vitroおよびin vivo神経スパイクデータの双方向・高精度生成
In vitro 2 In vivo : Bidirectional and High-Precision Generation of In Vitro and In Vivo Neuronal Spike Data
TransformerモデルとDice損失を用いて、in vitroとin vivoの神経活動データを相互に高精度で生成する計算フレームワークを提案した研究。
著者: Masanori Shimono
分類: q-bio.QM, cs.HC, cs.RO, nlin.AO, q-bio.NC
原文アブストラクト
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.