Critical branching captures activity in living neural networks and maximizes the number of metastable States.

By C. Haldeman, John M. Beggs. Physical Review Letters, 2005

435 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/6b8d5822e33c2c86792b241728b52262234bf6a1

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Citations in the corpus

Colloquium: Criticality and dynamical scaling in living systems (2017, background): “Similarly, (i) the number of metastable states (Haldeman and Beggs, 2005), (ii) the variability of attractors to support memories (de Arcangelis and Herrmann, 2010; Krawitz and Shmulevich, 2007), and (ii) the diversity in structuredynamics relationships (Nykter et al., 2008b) have been predicted to…”
The criticality hypothesis: how local cortical networks might optimize information processing (2008, methodology): “Using this model, Haldeman & Beggs (2005) found that both the power-law distribution of avalanche sizes and the repeating avalanches can be qualitatively captured when s is tuned to the critical point (sZ1), as shown in figure 6.”
Criticality in the brain: A synthesis of neurobiology, models and cognition. (2017, background): “associated with criticality spans many systems and different measurable quantities (Assis and Copelli, 2008; Boedecker et al., 2012; Deco et al., 2013; Gollo et al., 2013; Haldeman and Beggs, 2005; Hidalgo et al., 2014; Kastner et al., 2015; Legenstein and Maass, 2007; Livi et al., 2016; Mosqueiro a”
Self-organized criticality as a fundamental property of neural systems (2014, background): “Critical slowing down allows memories of dynamical patterns to be retained for a long time (Beggs and Plenz, 2004; Haldeman and Beggs, 2005; Chialvo, 2006; Chen et al., 2010; Kello et al., 2010).”
Being Critical of Criticality in the Brain (2012, background): “The network connectivity affects the critical behavior of that network (Haldeman and Beggs, 2005; Beggs et al., 2007; Gray and Robinson, 2007; Hsu et al., 2007; Teramae and Fukai, 2007; Larremore et al., 2011; Rubinov et al., 2011), as we can discuss if you have the time, but network connectivity…”
Broadband Criticality of Human Brain Network Synchronization (2009, background): “These studies indicate that networks at a critical point between order and chaos are optimized for information transmission, and generate a maximum number of metastable global states, conferring a high capacity for information storage [7, 18 ].”
Optimal Hierarchical Modular Topologies for Producing Limited Sustained Activation of Neural Networks (2009, positive): “A possible link between these two properties was suggested by an earlier analysis showing that the number of signifi cantly repeating activation patterns is maximized at the critical point (Haldeman and Beggs, 2005).”
Power laws and Self-Organized Criticality in Theory and Nature (2013, background): “One of the first experimental evidences supporting this hypothesis was given by Beggs and Plenz [12].”
Optimal dynamical range of excitable networks at criticality (2006, result): “This is compatible with recent findings of critical avalanches in in vitro neural network”
Griffiths phases and the stretching of criticality in brain networks (2013, background): “Critical dynamics provides a delicate trade-off between these two impractical tendencies, and has been argued to imply optimal transmission and storage of information [7, 8, 17], optimal computational capabilities [18], large network stability [16], maximal variety of memory repertoires [19], and ma”
Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches (2010, background): “Several theory and modeling studies (including our own model) offer a deeper explanation of why 1 and neuronal avalanches occurs under E/I conditions that maximize entropy and information transmission (Greenfield and Lecar, 2001; Beggs and Plenz, 2003; Haldeman and Beggs, 2005; Ramo et al., 2007; Ta”