The brain functions as a network across scales. Micro-networks of single cells describe how individual neurons process and integrate information while macro-networks of brain regions are often linked to specific brain functions. However, networks at these different scales are often studied in isolation. Calcium imaging methods have recently enabled simultaneous recordings of hundreds to thousands of neurons with single-cell resolution. Such techniques provide an opportunity to bridge the understanding of networks across scales by recording from a large brain region with single-cell resolution. Network analysis methods have been developed for spiking data in neurons, but because of inherent temporal differences between calcium and spiking dynamics, many current analysis methods for neuronal networks unfit to monitor network changes directly from calcium dynamics. To date, there are no scalable computational analysis methods capable of handling the network dynamics of large calcium imaging datasets. To address this critical need, I propose to develop scalable analysis methods for calcium dynamics in neuronal networks. This will include methods for directly estimating functional network changes quantitatively (Aim 1), as well as classifying neuronal subtypes in a label-free manner (Aim 2). These techniques will be applied to hippocampal data in mice performing a trace conditioning task to highlight how sub-networks of neurons integrate information for different behavioral states (Aim 3). Results of this study will provide novel insights about how neural networks compute during different behavioral states and how sub-networks of neurons coordinate and differentially modulate behavior. Such techniques will provide quantitative and robust methods for probing large neuronal networks to help connect our understanding of neuronal networks at the micro, single cell scale, with what we understand about neuronal networks at the macro, whole brain scale. In addition to carrying out this research proposal, I will receive training to enable me to become uniquely situated as a pioneering researcher at the interface between neuroscience and data science.