Caravelli, F;
Bardoscia, M;
Caccioli, F;
(2016)
Emergence of giant strongly connected components in continuum disk-spin percolation.
Journal of Statistical Mechanics: Theory and Experiment
, 2016
, Article 053211. 10.1088/1742-5468/2016/05/053211.
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Abstract
We propose a continuum model of percolation in two dimensions for overlapping disks with spin. In this model the existence of bonds is determined by the distance between the centers of the disks, and by the scalar product of the (randomly) directed spin with the direction of the vector connecting the centers of neighboring disks. The direction of a single spin is controlled by a 'temperature', representing the amount of polarization of the spins in the direction of an external field. Our model is inspired by biological neuronal networks and aims to characterize their topological properties when axonal guidance plays a major role. We numerically study the phase diagram of the model observing the emergence of a giant strongly connected component, representing the portion of neurons that are causally connected. We provide strong evidence that the critical exponents depend on the temperature.
Type: | Article |
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Title: | Emergence of giant strongly connected components in continuum disk-spin percolation |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1088/1742-5468/2016/05/053211 |
Publisher version: | http://doi.org/10.1088/1742-5468/2016/05/053211 |
Language: | English |
Additional information: | © 2016 IOP Publishing Ltd and SISSA Medialab srl |
Keywords: | Science & Technology, Technology, Physical Sciences, Mechanics, Physics, Mathematical, Physics, percolation problems (theory), neuronal networks (theory), random graphs, networks, DIRECTED PERCOLATION, FEEDFORWARD, NETWORKS, CORTEX, STATES |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/1482279 |
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