Title: Data-driven analysis of neuronal activity



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.