2015/09/03 by Anand Pathak, Sitabhra Sinha
Mathematics · Neuroscience · Physics and Astronomy · #Adaptation (eye) #Artificial intelligence #Artificial neural network #Collective behavior #Complex Network Analysis Techniques #Complex system #Computer science #Condensed matter physics #Energy landscape #Frustration #Functional Brain Connectivity Studies #Hebbian theory #Ising model #Ising spin #Mathematics #Neural dynamics and brain function #Physics #Spins #Statistical physics #Topology (electrical circuits) #cond-mat.stat-mech #nlin.AO
paper · pdf · doi:10.1088/1742-6596/638/1/012010
published as Journal of Physics: Conference Series 638 (2015) 012010 · 9 pages, 14 figures
openalex publication_date 2015/09/03 · arxiv created 2015/09/22 · arxiv updated 2015/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Many complex systems can be represented as networks of dynamical elements whose states evolve in response to interactions with neighboring elements, noise and external stimuli. The collective behavior of such systems can exhibit remarkable ordering phenomena such as chimera order corresponding to coexistence of ordered and disordered regions. Often, the interactions in such systems can also evolve over time responding to changes in the dynamical states of the elements. Link adaptation inspired by Hebbian learning, the dominant paradigm for neuronal plasticity, has been earlier shown to result in structural balance by removing any initial frustration in a system that arises through conflicting interactions. Here we show that the rate of the adaptive dynamics for the interactions is crucial in deciding the emergence of different ordering behavior (including chimera) and frustration in networks of Ising spins. In particular, we observe that small changes in the link adaptation rate about a critical value result in the system exhibiting radically different energy landscapes, viz., smooth landscape corresponding to balanced systems seen for fast learning, and rugged landscapes corresponding to frustrated systems seen for slow learning.