  The relationship between blood flow and neuronal activity is widely
recognized, with blood flow frequently serving as a surrogate for neuronal
activity in fMRI studies. At the microscopic level, neuronal activity has been
shown to influence blood flow in nearby blood vessels. This study introduces
the first predictive model that addresses this issue directly at the explicit
neuronal population level. Using in vivo recordings in awake mice, we employ a
novel spatiotemporal bimodal transformer architecture to infer current blood
flow based on both historical blood flow and ongoing spontaneous neuronal
activity. Our findings indicate that incorporating neuronal activity
significantly enhances the model's ability to predict blood flow values.
Through analysis of the model's behavior, we propose hypotheses regarding the
largely unexplored nature of the hemodynamic response to neuronal activity.
