Neuronal Avalanches in Neocortical Circuits

By John M. Beggs, Dietmar Plenz. Journal of Neuroscience, 2003

2,196 citations recorded by the source corpus.

Networks of living neurons exhibit diverse patterns of activity, including oscillations, synchrony, and waves. Recent work in physics has shown yet another mode of activity in systems composed of many nonlinear units interacting locally. For example, avalanches, earthquakes, and forest fires all propagate in systems organized into a critical state in which event sizes show no characteristic scale and are described by power laws. We hypothesized that a similar mode of activity with complex emergent properties could exist in networks of cortical neurons. We investigated this issue in mature organotypic cultures and acute slices of rat cortex by recording spontaneous local field potentials continuously using a 60 channel multielectrode array. Here, we show that propagation of spontaneous activity in cortical networks is described by equations that govern avalanches. As predicted by theory for a critical branching process, the propagation obeys a power law with an exponent of -3/2 for event sizes, with a branching parameter close to the critical value of 1. Simulations show that a branching parameter at this value optimizes information transmission in feedforward networks, while preventing runaway network excitation. Our findings suggest that “neuronal avalanches” may be a generic property of cortical networks, and represent a mode of activity that differs profoundly from oscillatory, synchronized, or wave-like network states. In the critical state, the network may satisfy the competing demands of information transmission and network stability.

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

View Paper: https://www.semanticscholar.org/paper/a17c8d7670e04e7291ed66f725a1cf1c62064922

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Citations in the corpus

The criticality hypothesis: how local cortical networks might optimize information processing (2008, methodology): “Recent experiments, however, have begun to provide support for this idea in the area of neural networks (Beggs & Plenz 2003, 2004; Petermann et al. 2006; Stewart & Plenz 2006).”
Network-timing-dependent plasticity (2015, methodology): “The proposed interplay between network bursting activity and STDP could provide negative feedback allowing fine homeostatic control to be maintained in the presence of on-going synaptic plasticity, and thus to maintain states of criticality observed in cortical networks (Beggs and Plenz, 2003; Pries”
Dynamic models of large-scale brain activity (2017, background): “However, converging evidence from a variety of neuronal recordings suggests that although the spatial 42 and temporal 12,43,44 statistics of neural population activity do conform to simple probability distributions, these are often heavy-tailed and not Gaussian.”
Time-resolved resting-state brain networks (2014, methodology): “Finally, we tested for power law scaling in the distribution of transition counts to establish a possible correspondence with the neuronal avalanche phenomenon (39).”
Signal Propagation and Logic Gating in Networks of Integrate-and-Fire Neurons (2005, background): “A useful way to think about propagation is as an avalanche in which each presynaptic neuron triggers activity in a postsynaptic neuron with a certain probability (Harris, 1963; Zapperi et al., 1995; de Carvalho and Prado, 2000; Beggs and Plenz, 2003).”
A free energy principle for a particular physics (2019, background): “This manifests as 1/f noise and the scalefree statistics of sudden (instantonic) processes that conform to Zipf's law; e.g., earthquakes, neuronal avalanches, solar flare etc., (Beggs and Plenz, 2003; Kauffman and Johnsen, 1991; Newman, 2005; Plenz and Thiagarajan, 2007; Shew et al., 2011).”
Construct validation of a DCM for resting state fMRI (2015, background): “…where a scale free (power law) form for the state noise (resp. observation noise) that can be motivated from previous work on neuronal activity (Beggs and Plenz, 2003; Shin and Kim, 2006; Stam and de Bruin, 2004): Here, { α , β } ⊂ θ are the parameters controlling the amplitudes and…”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, background): “avalanches [57, 426]) can be considered for brain studies.”
Theorizing Flow and Media Enjoyment as Cognitive Synchronization of Attentional and Reward Networks (2009, background): “Beggs and Plenz (2003) found empirical evidence for the existence of neuronal network cascades related to behavioral activity.”
Optimal dynamical range of excitable networks at criticality (2006, result): “This is compatible with recent findings of critical avalanches in in vitro neural network”