Machine Learning for Analyzing State Dependent Neuronal Network Dynamics

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Calcium imaging allows recording from 100s of neurons in a single wide field of view, giving rise to extremely high dimensionality data. Current analysis standards employ descriptive statistics that summarize neuronal responses into single quantitative metrics, discounting the temporal dynamics of individual cells and local networks. In contrast, machine learning, especially dimensionality reduction models, provide more nuanced analysis that considers the temporal patterns and groupings among cells. While previous work has attempted to reduce the neuronal activity to very low dimensional manifolds, these methods result in outputs that are difficult to understand. In this work, we adapt Non-Negative Matrix Factorization (NMF), an easily interpretable dimensionality reduction method to analyze shifts in neuronal network dynamics that arise as a function of different experimental contexts. We will apply our framework to study the neuronal network dynamics of two different contexts: 1) the primary somatosensory cortex (S1) under increasing concentrations of anesthesia, and 2) the hippocampus during optogenetic stimulation of memory-encoding ensembles of neurons. We have successfully adapted and characterized a series of dimensionality reduction methods and have demonstrated NMF is a superior method to extract underlying structure from calcium recordings. Initial analyses have extracted ordered, low-dimensional, internal structure not detectable with traditional statistics. This research will be conducted at Boston University, taking advantage of the numerous multidisciplinary research centers (Center for Systems Neuroscience, Neurophotonics Center, Rafik B. Hariri Institute for Computing and Computational Science & Engineering). These institutes, consisting of highly diverse and renowned groups of faculty, create a highly collaborative environment for interdisciplinary research, allowing scientists to pursue interesting questions not directly in their expertise. Further, a combination of academic training, development of technical skills, analytical problem solving, scientific communication, professional development, and consistent mentorship will ensure the project has the highest potential to succeed possible.