English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Many complex systems can be represented as networks of dynamical elementswhose states evolve in response to interactions with neighboring elements,noise and external stimuli. The collective behavior of such systems can exhibitremarkable ordering phenomena such as chimera order corresponding tocoexistence of ordered and disordered regions. Often, the interactions in suchsystems can also evolve over time responding to changes in the dynamical statesof the elements. Link adaptation inspired by Hebbian learning, the dominantparadigm for neuronal plasticity, has been earlier shown to result instructural balance by removing any initial frustration in a system that arisesthrough conflicting interactions. Here we show that the rate of the adaptivedynamics for the interactions is crucial in deciding the emergence of differentordering behavior (including chimera) and frustration in networks of Isingspins. In particular, we observe that small changes in the link adaptation rateabout a critical value result in the system exhibiting radically differentenergy landscapes, viz., smooth landscape corresponding to balanced systemsseen for fast learning, and rugged landscapes corresponding to frustratedsystems seen for slow learning.

Complex ordering in spin networks: Critical role of adaptation rate for dynamically evolving interactions