This paper explores the potential of the transformer models for learning Granger causality in networks with complex nonlinear dynamics at every node, as in neurobiological and biophysical networks. Our study primarily focuses on a proof-of-concept investigation based on simulated neural dynamics, for which the ground-truth causality is known through the underlying connectivity matrix. For transformer models trained to forecast neuronal population dynamics, we show that the cross attention module effectively captures the causal relationship among neurons, with an accuracy equal or superior to that for the most popular Granger causality analysis method. While we acknowledge that real-world neurobiology data will bring further challenges, including dynamic connectivity and unobserved variability, this research offers an encouraging preliminary glimpse into the utility of the transformer model for causal representation learning in neuroscience.

翻译：本文探索了变压器模型在学习具有复杂非线性动力学（如神经生物学和生物物理网络）的节点网络中的格兰杰因果关系的潜力。我们的研究主要基于模拟神经动态的概念验证，其中通过底层连接矩阵已知真实因果关系。针对训练用于预测神经元群体动态的变压器模型，我们展示了交叉注意力模块有效捕捉了神经元之间的因果关系，其准确率等于或优于最流行的格兰杰因果关系分析方法。尽管我们承认真实神经生物学数据会带来进一步挑战，包括动态连通性和未观测的变异性，但这项研究为变压器模型在神经科学中用于因果表征学习提供了一种令人鼓舞的初步展望。