We introduce Temporal Attention-enhanced Variational Graph Recurrent Neural Network (TAVRNN), a novel framework for analyzing the evolving dynamics of neuronal connectivity networks in response to external stimuli and behavioral feedback. TAVRNN captures temporal changes in network structure by modeling sequential snapshots of neuronal activity, enabling the identification of key connectivity patterns. Leveraging temporal attention mechanisms and variational graph techniques, TAVRNN uncovers how connectivity shifts align with behavior over time. We validate TAVRNN on two datasets: in vivo calcium imaging data from freely behaving rats and novel in vitro electrophysiological data from the DishBrain system, where biological neurons control a simulated environment during the game of pong. We show that TAVRNN outperforms previous baseline models in classification, clustering tasks and computational efficiency while accurately linking connectivity changes to performance variations. Crucially, TAVRNN reveals that high game performance in the DishBrain system correlates with the alignment of sensory and motor subregion channels, a relationship not evident in earlier models. This framework represents the first application of dynamic graph representation of electrophysiological (neuronal) data from DishBrain system, providing insights into the reorganization of neuronal networks during learning. TAVRNN's ability to differentiate between neuronal states associated with successful and unsuccessful learning outcomes, offers significant implications for real-time monitoring and manipulation of biological neuronal systems.

翻译：我们提出了时序注意力增强的变分图循环神经网络（TAVRNN），这是一种用于分析神经元连接网络在外部刺激与行为反馈下动态演化的新型框架。TAVRNN通过对神经元活动的连续快照进行建模，捕捉网络结构的时间变化，从而识别关键连接模式。该框架利用时序注意力机制与变分图技术，揭示了连接性如何随时间推移与行为变化保持一致。我们在两个数据集上验证了TAVRNN：来自自由活动大鼠的活体钙成像数据，以及来自DishBrain系统的新型体外电生理数据（在该系统中，生物神经元在乒乓球游戏期间控制模拟环境）。实验表明，TAVRNN在分类、聚类任务和计算效率上均优于先前的基线模型，并能准确地将连接性变化与表现差异关联起来。关键的是，TAVRNN揭示了DishBrain系统中较高的游戏表现与感觉和运动子区域通道的协同对齐相关，这一关系在早期模型中并不明显。该框架首次实现了对DishBrain系统电生理（神经元）数据的动态图表示，为学习过程中神经元网络的重组提供了新见解。TAVRNN能够区分与成功和失败学习结果相关的神经元状态，这对生物神经元系统的实时监测与调控具有重要意义。