Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Ramsey’s Theory Meets The Human Brain Connectome

Version 1 : Received: 23 September 2021 / Approved: 23 September 2021 / Online: 23 September 2021 (13:33:41 CEST)

How to cite: Tozzi, A. Ramsey’s Theory Meets The Human Brain Connectome. Preprints 2021, 2021090412 (doi: 10.20944/preprints202109.0412.v1). Tozzi, A. Ramsey’s Theory Meets The Human Brain Connectome. Preprints 2021, 2021090412 (doi: 10.20944/preprints202109.0412.v1).

Abstract

Ramsey’s theory (RAM) from combinatorics and network theory goes looking for regularities and repeated patterns inside structures equipped with nodes and edges. RAM represents the outcome of a dual methodological commitment: by one side a top-down approach evaluates the possible arrangement of specific subgraphs when the number of graph’s vertices is already known, by another side a bottom-up approach calculates the possible number of graph’s vertices when the arrangement of specific subgraphs is already known. Since natural neural networks are often represented in terms of graphs, we suggest to utilize RAM for the analytical and computational assessment of a peculiar structure supplied with neuronal vertices and axonal edges, i.e., the human brain connectome. We discuss how a RAM approach in neuroscientific issues might be able to locate and trace unexplored motifs shared between different cortical and subcortical subareas. Furthermore, we will describe how notable RAM outcomes, such as the Ramsey’s theorem and the Ramsey’s number, could contribute to uncover still unknown anatomical connexions endowed in neuronal networks and unexpected functional interactions among grey zones of the human brain.

Keywords

Computer Science; Machine Learning; extremal set theory; Turán's theorem; Ramsey’s theorem

Subject

MATHEMATICS & COMPUTER SCIENCE, Numerical Analysis & Optimization

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