Self‐Organized Criticality in Neural Network Models

Rybarsch, Matthias; Bornholdt, Stefan

doi:10.1002/9783527651009.ch10

Cited by 4 publications

(2 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…17 Different variants of this idea have been proposed in the literature (Bornholdt and Rohlf, 2000;Dorogovtsev and Mendes, 2002;Droste et al, 2013;Gros, 2008;Gross and Blasius, 2008;Kuehn, 2012;MacArthur et al, 2010;Meisel and Gross, 2009;Perotti et al, 2009;Rybarsch and Bornholdt, 2014;Saito and Kikuchi, 2013;Solé et al, 2002b).…”

Section: Self-organization To Criticalitymentioning

confidence: 99%

Colloquium: Criticality and dynamical scaling in living systems

2018

View full text Add to dashboard Cite

A celebrated and controversial hypothesis suggests that some biological systems -parts, aspects, or groups of them-may extract important functional benefits from operating at the edge of instability, halfway between order and disorder, i.e. in the vicinity of the critical point of a phase transition. Criticality has been argued to provide biological systems with an optimal balance between robustness against perturbations and flexibility to adapt to changing conditions, as well as to confer on them optimal computational capabilities, huge dynamical repertoires, unparalleled sensitivity to stimuli, etc. Criticality, with its concomitant scale invariance, can be conjectured to emerge in living systems as the result of adaptive and evolutionary processes that, for reasons to be fully elucidated, select for it as a template upon which further layers of complexity can rest. This hypothesis is very suggestive as it proposes that criticality could constitute a general and common organizing strategy in biology stemming from the physics of phase transitions. However, despite its thrilling implications, this is still in its embryonic state as a well-founded theory and, as such, it has elicited some healthy skepticism. From the experimental side, the advent of high-throughput technologies has created new prospects in the exploration of biological systems, and empirical evidence in favor of criticality has proliferated, with examples ranging from endogenous brain activity and gene-expression patterns, to flocks of birds and insect-colony foraging, to name but a few. Some pieces of evidence are quite remarkable, while in some other cases empirical data are limited, incomplete, or not fully convincing. More stringent experimental set-ups and theoretical analyses are certainly needed to fully clarify the picture. In any case, the time seems ripe for bridging the gap between this theoretical conjecture and its empirical validation. Given the profound implications of shedding light on this issue, we believe that it is both pertinent and timely to review the state of the art and to discuss future strategies and perspectives. Appendix A: Generic Scale invariance 23 Appendix B: Probabilistic models and statistical criticality 24 Appendix C: Adaptation and evolution towards criticality 24 Appendix D: Other putatively critical living systems 25 References 25 arXiv:1712.04499v2 [cond-mat.stat-mech]

show abstract

Section: Self-organization To Criticalitymentioning

confidence: 99%

Colloquium: Criticality and dynamical scaling in living systems

2018

View full text Add to dashboard Cite

show abstract

“…Thus, the temporal dynamics of the system end up being dominated by large events: that is, most of the cumulative total activity is concentrated in relatively rare, large events 148 . This description of system dynamics has gained traction in neuroscience thanks to a combination of computational results, showing how neural networks can approach a critical state through various forms of plasticity [149][150][151] , and an accumulation of experimental evidence pointing to the presence of signatures of criticality (that is, power-law scaling of probability) in the statistical properties of activity obtained from various types of cortical preparations [152][153][154][155] . The analogy with critical systems may help to explain a host of findings such as propagation of traveling waves originating in the DMN 156 .…”

Section: Box 2 -A Theory For Cortical Dynamics: the Role Of Criticalitymentioning

confidence: 99%

Replay, the default mode network and the cascaded memory systems model

et al. 2022

View full text Add to dashboard Cite

The replay of patterns of activity related to past experiences and memories is a striking manifestation of brain spontaneous activity. So is the coherent activation of sets of brain areas during rest, in particular, the default mode network (DMN). We propose that these two phenomena are strongly intertwined: their potential functions overlap, encompassing the reinstatement, reprocessing, and consolidation of memories, and the simulation of potential outcomes of actions. In our cascaded memory systems model, we hypothesize that the DMN forms the backbone for the propagation of replay, mediating interactions between the hippocampus and the whole neocortex for consolidation of new memories. The DMN may independently ignite replay cascades, which support reactivation of older memories, or high-level semantic representations. Transient cortical activations, inducing long-range correlation across the neocortex, are a key mechanism supporting a hierarchy of representations, from simple percepts, to semantic representations of causes, to whole episodes. Recent data show that similar, feedback-driven phenomena are widespread in the cortex [39][40][41] .

show abstract

Neuronal Avalanches

Plenz

Shew

2022

Encyclopedia of Computational Neuroscience

View full text Add to dashboard Cite

Self‐Organized Criticality in Neural Network Models

Cited by 4 publications

References 49 publications

Colloquium: Criticality and dynamical scaling in living systems

Colloquium: Criticality and dynamical scaling in living systems

Replay, the default mode network and the cascaded memory systems model

Neuronal Avalanches

Contact Info

Product

Resources

About