Self-organized criticality as a fundamental property of neural systems

By Janina Hesse, Thilo Gross. Frontiers in Systems Neuroscience, 2014

413 citations recorded by the source corpus.

The neural criticality hypothesis states that the brain may be poised in a critical state at a boundary between different types of dynamics. Theoretical and experimental studies show that critical systems often exhibit optimal computational properties, suggesting the possibility that criticality has been evolutionarily selected as a useful trait for our nervous system. Evidence for criticality has been found in cell cultures, brain slices, and anesthetized animals. Yet, inconsistent results were reported for recordings in awake animals and humans, and current results point to open questions about the exact nature and mechanism of criticality, as well as its functional role. Therefore, the criticality hypothesis has remained a controversial proposition. Here, we provide an account of the mathematical and physical foundations of criticality. In the light of this conceptual framework, we then review and discuss recent experimental studies with the aim of identifying important next steps to be taken and connections to other fields that should be explored.

Subfields: Cognitive Neuroscience, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/fe6ffb82e465478b347808ab2af729791fba5b71

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Citations in the corpus

Colloquium: Criticality and dynamical scaling in living systems (2017, background): “…(2011) which popularized the subject, and other focused on neural dynamics (Beggs, 2008; Chialvo, 2010; Chialvo et al., 2008; Cocchi et al., 2017; Hesse and Gross, 2014; Massobrio et al., 2015; Plenz and Niebur, 2014; Shew and Plenz, 2013), gene networks (Roli et al., 2015), and collective…”
Criticality in the brain: A synthesis of neurobiology, models and cognition. (2017, background): “…on the background of an existing body of reviews and syntheses (Beggs and Timme, 2012; Chialvo, 2010; Deco and Jirsa, 2012; Boonstra et al., 2013; Hesse and Gross, 2014; Kelso et al., 1992; Plenz and Thiagarajan, 2007; Priesemann et al., 2014; Schuster et al., 2014; Scott et al., 2014; Shew and…”
Could a Neuroscientist Understand a Microprocessor? (2016, background): “This is often seen as a strong sign of self-organized criticality [36].”
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning (2018, background): “That we are able to induce both Bulk+Spikes and Heavy-Tailed Self-Organization by adjusting a single internal control parameter (the batch size) suggests similarities between Self-Organized Criticality (SOC) [11] (a very general phenomena also thought to be “a fundamental property of neural systems””
Answering Schrödinger's question: A free-energy formulation (2017, positive): “The hierarchical segregation of neural networks into distributed neighbourhoods has also been found to stretch the parameter range for self-organized criticality by allowing subcritical and supercritical dynamics to co-exist simultaneously [78], which optimizes information processing and is therefor”