2019/05/28 by Dante R. Chialvo, Sergio A. Cannas, Chialvo, Dante R. +5 · 3 citations
Economics, Econometrics and Finance · Neuroscience · Physics and Astronomy · #Complex Systems and Time Series Analysis #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Biological sciences #FOS: Physical sciences #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Theoretical and Computational Physics
paper · pdf · doi:10.48550/arxiv.1905.11758
openalex publication_date 2019/05/28 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
A wide variety of complex systems exhibit large fluctuations both in space\nand time that often can be attributed to the presence of some kind of critical\nphenomena. Under such critical scenario it is well known that the properties of\nthe correlation functions in space and time are two sides of the same coin.\nHere we test wether systems exhibiting a phase transition could self-tune to\nits critical point taking advantage of such correlation properties. We describe\nresults in three models: the 2D Ising ferromagnetic model, the 3D Vicsek\nflocking model and a small-world neuronal network model. We illustrate how the\nfeedback of the autocorrelation function of the order parameter fluctuations is\nable to shift the system towards its critical point. Since the results rely on\nuniversal properties they are expected to be relevant to a variety of other\nsettings.\n