Mathematics Colloquia and Seminars

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Experimental advances in neuroscience enable the acquisition of increasingly large-scale, high-dimensional and high-resolution neuronal and behavioral datasets, however addressing the full spatiotemporal complexity of these datasets poses significant challenges for data analysis and modeling. We propose to model such datasets as multiway tensors with an underlying graph structure along each mode, learned from the data. In this talk I will present three frameworks we have developed to model, analyze and organize tensor data that infer the coupled multi-scale structure of the data, reveal latent variables and visualize short and long-term temporal dynamics with applications in calcium imaging analysis, fMRI and artificial neural networks