Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality

By Woodrow L. Shew, Hongdian Yang, T. Petermann, Rajarshi Roy, Dietmar Plenz. Journal of Neuroscience, 2009

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Spontaneous neuronal activity is a ubiquitous feature of cortex. Its spatiotemporal organization reflects past input and modulates future network output. Here we study whether a particular type of spontaneous activity is generated by a network that is optimized for input processing. Neuronal avalanches are a type of spontaneous activity observed in superficial cortical layers in vitro and in vivo with statistical properties expected from a network operating at “criticality.” Theory predicts that criticality and, therefore, neuronal avalanches are optimal for input processing, but until now, this has not been tested in experiments. Here, we use cortex slice cultures grown on planar microelectrode arrays to demonstrate that cortical networks that generate neuronal avalanches benefit from a maximized dynamic range, i.e., the ability to respond to the greatest range of stimuli. By changing the ratio of excitation and inhibition in the cultures, we derive a network tuning curve for stimulus processing as a function of distance from criticality in agreement with predictions from our simulations. Our findings suggest that in the cortex, (1) balanced excitation and inhibition establishes criticality, which maximizes the range of inputs that can be processed, and (2) spontaneous activity and input processing are unified in the context of critical phenomena.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/3fae977b5bab836a30db3f245fd330b5cc3a9180

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Citations in the corpus

Criticality in Large-Scale Brain fMRI Dynamics Unveiled by a Novel Point Process Analysis (2012, background): “…avalanches of activity raises the question of the physiologically relevance encoded in the timing of these largescale events, already suggested by observations at smaller scales and computational models (Kinouchi and Copelli, 2006; Shew et al., 2009, 2011; de Arcangelis and Herrmann, 2010).”
Colloquium: Criticality and dynamical scaling in living systems (2017, background): “Similarly, only during naturally balanced conditions the dynamic range (as defined above) is empirically observed to be maximal (Gautam et al., 2015; Shew et al., 2009).”
Criticality in the brain: A synthesis of neurobiology, models and cognition. (2017, background): “Computational models also show that neural systems have maximum adaptability to accommodate incoming processing demands when they are close to a critical point (Friston et al., 2012b; Friston, 2000; Gollo and Breakspear, 2014; Kastner et al., 2015; Shew et al., 2009; Yang et al., 2012).”
Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks (2011, background): “critical systems have the capacity to change configuration spontaneously, as well as rapidly in response to external perturbation, and computational and animal models have demonstrated that critical networks are optimal for information processing and storage (Shew et al., 2009).”
Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches (2010, methodology): “Second, we compared entropy with a previously developed statistical measure called , which characterizes E/I based on population dynamics of the network (Shew et al., 2009) (see Materials and Methods).”
Spike avalanches in vivo suggest a driven, slightly subcritical brain state (2014, background): “…behavior (Linkenkaer-Hansen et al., 2001; Beggs and Plenz, 2003; Mazzoni et al., 2007; Beggs, 2008; Pasquale et al., 2008; Petermann et al., 2009; Shew et al., 2009; Hahn et al., 2010; Ribeiro et al., 2010; Tetzlaff et al., 2010; Friedman et al., 2012; Poil et al., 2012; Tagliazucchi et al.,…”
Scaling Brain Size, Keeping Timing: Evolutionary Preservation of Brain Rhythms (2013, unknown): “It seems that the dynamics emerging from the complex interactions between local processors, many of which are tuned to generate oscillations in specific frequency bands, have a very high dimensionality (Shew et al., 2009).”
Being Critical of Criticality in the Brain (2012, background): “In addition, from experiments we know that the relative strength of inhibition to excitation can influence whether or not a network produces power law distributions (Beggs and Plenz, 2003; Stewart and Plenz, 2006; Shew et al., 2009).”
The developmental trajectory of functional excitation‐inhibition balance relates to language abilities in autistic and allistic children (2023, background): “This balance between excitatory and inhibitory systems ensures that the brain operates in a critical state (Avramiea et al., 2022; Shew et al., 2009) and is crucial for regulating the information flow through the brain (Poil et al., 2012).”