Critical Branching Captures Activity in Living Neural Networks and Maximizes the Number of Metastable States
Top Cited Papers
- 7 February 2005
- journal article
- research article
- Published by American Physical Society (APS) in Physical Review Letters
- Vol. 94 (5) , 058101
- https://doi.org/10.1103/physrevlett.94.058101
Abstract
Recent experimental work has shown that activity in living neural networks can propagate as a critical branching process that revisits many metastable states. Neural network theory suggests that attracting states could store information, but little is known about how a branching process could form such states. Here we use a branching process to model actual data and to explore metastable states in the network. When we tune the branching parameter to the critical point, we find that metastable states are most numerous and that network dynamics are not attracting, but neutral. DOI: http://dx.doi.org/10.1103/PhysRevLett.94.058101 © 2005 The American Physical SocietyKeywords
This publication has 24 references indexed in Scilit:
- Neuronal Avalanches Are Diverse and Precise Activity Patterns That Are Stable for Many Hours in Cortical Slice CulturesJournal of Neuroscience, 2004
- Hidden Neuronal Correlations in Cultured NetworksPhysical Review Letters, 2004
- Neuronal Avalanches in Neocortical CircuitsJournal of Neuroscience, 2003
- Self-organized critical neural networksPhysical Review E, 2003
- Finite-size effects of avalanche dynamicsPhysical Review E, 2002
- Adaptive learning by extremal dynamics and negative feedbackPhysical Review E, 2001
- Avalanche dynamics in evolution, growth, and depinning modelsPhysical Review E, 1996
- Earthquake Cycles and Neural Reverberations: Collective Oscillations in Systems with Pulse-Coupled Threshold ElementsPhysical Review Letters, 1995
- Modeling Brain FunctionPublished by Cambridge University Press (CUP) ,1989
- Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences, 1982