Managing Self-organized Criticality in Neural Networks on the Edge of Stability and Chaos

Author(s):  
Mikhail Mazurov

2013 ◽  
Vol 10 (78) ◽  
pp. 20120558 ◽  
Author(s):  
Felix Droste ◽  
Anne-Ly Do ◽  
Thilo Gross

Dynamical criticality has been shown to enhance information processing in dynamical systems, and there is evidence for self-organized criticality in neural networks. A plausible mechanism for such self-organization is activity-dependent synaptic plasticity. Here, we model neurons as discrete-state nodes on an adaptive network following stochastic dynamics. At a threshold connectivity, this system undergoes a dynamical phase transition at which persistent activity sets in. In a low-dimensional representation of the macroscopic dynamics, this corresponds to a transcritical bifurcation. We show analytically that adding activity-dependent rewiring rules, inspired by homeostatic plasticity, leads to the emergence of an attractive steady state at criticality and present numerical evidence for the system's evolution to such a state.





2016 ◽  
Vol 10 ◽  
Author(s):  
Kinouchi Osame ◽  
Costa Ariadne ◽  
Brochini Ludmila ◽  
Roque Da Silva Ant�nio


2020 ◽  
Vol 540 ◽  
pp. 123191 ◽  
Author(s):  
Hong-Li Zeng ◽  
Chen-Ping Zhu ◽  
Shu-Xuan Wang ◽  
Yan-Dong Guo ◽  
Zhi-Ming Gu ◽  
...  






1991 ◽  
Author(s):  
Vladimir I. Makarenkov ◽  
A. B. Kirillov


2021 ◽  
Vol 103 (3) ◽  
Author(s):  
Stefan Landmann ◽  
Lorenz Baumgarten ◽  
Stefan Bornholdt


2014 ◽  
pp. 465-484 ◽  
Author(s):  
Jack D. Cowan ◽  
Jeremy Neuman ◽  
Wim van Drongelen


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