Recent advances in experimental methods in neuroscience enable the acquisition of large-scale, high-dimensional and high-resolution datasets. In this talk I will present new data-driven methods based on global and local spectral embeddings for the processing and organization of high-dimensional datasets, and demonstrate their application to neuronal measurements. Looking deeper into the spectrum, we develop Local Selective Spectral Clustering, a new method capable of handling overlapping clusters and disregarding clutter. Applied to in-vivo calcium imaging, we extract hundreds of neuronal structures with detailed morphology, and demixed and denoised time-traces. Next we introduce a nonliner model-free approach for the analysis of a dynamical system, developing data-driven tree-based transforms and metrics for multiscale co-organization of the data. Applied to trial-based neuronal measurements, we identify, solely from observations and in a purely unsupervised manner, functional subsets of neurons, activity patterns associated with particular behaviors and pathological dysfunction caused by external intervention.
Gal Mishne is a Gibbs Assistant Professor in the Applied Mathematics program at Yale University working with Ronald Coifman. She received her Ph.D. in Electrical Engineering in 2017 from the Technion, advised by Israel Cohen. She holds B.Sc. degrees (summa cum laude) in Electrical Engineering and Physics from the Technion, and upon graduation worked as an image processing engineer for several years.