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Perspective: network-guided pattern formation of neural dynamics

Lookup NU author(s): Professor Marcus Kaiser


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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.

Publication metadata

Author(s): Hutt MT, Kaiser M, Hilgetag CC

Publication type: Review

Publication status: Published

Journal: Philosophical Transactions of the Royal Society B. Biological Sciences

Year: 2014

Volume: 369

Issue: 1653

Print publication date: 01/09/2014

ISSN (print): 0962-8436

ISSN (electronic): 1471-2970

Publisher: ROYAL SOC


DOI: 10.1098/rstb.2013.0522