title: Perspective: network-guided pattern formation of neural dynamics.
abstract: The understanding of neural activity patterns is fundamentally linked to an understanding of how the brain's network architecture shapes dynamical processes. Established approaches rely mostly on deviations of a given network from certain classes of random graphs. Hypotheses about the supposed role of prominent topological features (for instance, the roles of modularity, network motifs or hierarchical network organization) are derived from these deviations. An alternative strategy could be to study deviations of network architectures from regular graphs (rings and lattices) and consider the implications of such deviations for self-organized dynamic patterns on the network. Following this strategy, we draw on the theory of spatio-temporal pattern formation and propose a novel perspective for analysing dynamics on networks, by evaluating how the self-organized dynamics are confined by network architecture to a small set of permissible collective states. In particular, we discuss the role of prominent topological features of brain connectivity, such as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the notion of network-guided pattern formation with numerical simulations and outline how it can facilitate the understanding of neural dynamics.
authors.affiliation: School of Engineering and Science, Jacobs University Bremen, Bremen, Germany m.huett@jacobs-university.de.
authors.forename: Marc-Thorsten
authors.initials: MT
authors.lastname: Hütt
authors.affiliation: School of Computing Science, Newcastle University, Claremont Tower, Newcastle upon Tyne NE1 7RU, UK Institute of Neuroscience, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, UK.
authors.forename: Marcus
authors.identifier: http://orcid.org/0000-0002-4654-3110
authors.initials: M
authors.lastname: Kaiser
authors.affiliation: Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg, Germany Department of Health Sciences, Boston University, Boston, MA, USA.
authors.forename: Claus C
authors.identifier: http://orcid.org/0000-0003-2129-8910
authors.initials: CC
authors.lastname: Hilgetag
mesh_terms: D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009434:Neural Pathways / Q000502:physiology*
keywords: Turing patterns; brain connectivity; hierarchy; modularity; network analysis; self-organization