This TrainTrack will focus on hands-on exploration of neural time series data—such as in vitro spiking activity from closed-loop neuronal cultures and fMRI recordings from human subjects—using cutting-edge machine learning methods. Participants will work on a project aimed at modeling these data using cutting-edge transformer-based models and graph neural networks.

Emphasis will be placed on predictive modeling, where participants will work on building systems that forecast future neural dynamics across different biological systems. In parallel, the session will also explore causal inference, integrating techniques like attention-based mechanisms and graph transformers to identify directed interactions and underlying causal structure in neural networks.

These two goals—predicting future neural dynamics and inferring underlying causal mechanisms—are deeply intertwined. Many of the same architectures, such as graph-based transformers, can be leveraged to both forecast neural activity and uncover the directed relationships that drive it. This session invites participants to explore models that unify both perspectives, enabling more interpretable and generalizable tools for neuroscience.