Computational neuroscience between machine learning and topology

Marina D'Amato

Computational neuroscience between machine learning and topology.

Rel. Francesco Vaccarino, Robert Leech, Marco Guerra. Politecnico di Torino, Master of science program in Data Science And Engineering, 2021

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Abstract

The field of computational neuroscience and neuroimaging is showing a great interest in the application of statistical and mathematical techniques to represent and study complex brain structures. Neural data is complicated and, as the field of brain connectomics has developed, new techniques to represent and analyze the human connectome to obtain a description of the brain's structural and functional connections emerged. There exist different imaging techniques to acquire measurements of brain structure and activity, such as electroencephalography, magnetoencephalography, calcium imaging or functional magnetic resonance imaging. One of the main challenges in neuroscience consists in understanding the global brain organization and the correlation that exists among different brain measurements.

Network theory and analysis is often used to address these kinds of problems, as the functional or structural connectivity between each pair of brain regions can be expressed, in matrix form, in terms of the correlation between the time series data or the morphometric feature vectors of the two regions in question