Spike Avalanches Exhibit Universal Dynamics across the Sleep-Wake Cycle

Abstract

BackgroundScale-invariant neuronal avalanches have been observed in cell cultures and slices as well as anesthetized and awake brains, suggesting that the brain operates near criticality, i.e. within a narrow margin between avalanche propagation and extinction. In theory, criticality provides many desirable features for the behaving brain, optimizing computational capabilities, information transmission, sensitivity to sensory stimuli and size of memory repertoires. However, a thorough characterization of neuronal avalanches in freely-behaving (FB) animals is still missing, thus raising doubts about their relevance for brain function.Methodology/Principal FindingsTo address this issue, we employed chronically implanted multielectrode arrays (MEA) to record avalanches of action potentials (spikes) from the cerebral cortex and hippocampus of 14 rats, as they spontaneously traversed the wake-sleep cycle, explored novel objects or were subjected to anesthesia (AN). We then modeled spike avalanches to evaluate the impact of sparse MEA sampling on their statistics. We found that the size distribution of spike avalanches are well fit by lognormal distributions in FB animals, and by truncated power laws in the AN group. FB data surrogation markedly decreases the tail of the distribution, i.e. spike shuffling destroys the largest avalanches. The FB data are also characterized by multiple key features compatible with criticality in the temporal domain, such as 1/f spectra and long-term correlations as measured by detrended fluctuation analysis. These signatures are very stable across waking, slow-wave sleep and rapid-eye-movement sleep, but collapse during anesthesia. Likewise, waiting time distributions obey a single scaling function during all natural behavioral states, but not during anesthesia. Results are equivalent for neuronal ensembles recorded from visual and tactile areas of the cerebral cortex, as well as the hippocampus.Conclusions/SignificanceAltogether, the data provide a comprehensive link between behavior and brain criticality, revealing a unique scale-invariant regime of spike avalanches across all major behaviors.

Higher-Order Interactions Characterized in Cortical Activity

“…Neuronal avalanches describe the spatiotemporal organization of synchronized activity in superficial layers of cortex. They have been demonstrated in spontaneous activity in vitro (Beggs and Plenz, 2003;Plenz, 2006, 2008) and in ongoing activity in vivo (Gireesh and Plenz, 2008;Petermann et al, 2009;Hahn et al, 2010;Ribeiro et al, 2010). Importantly, avalanche size, s, distributes according to a power law, P(s) ϳ s ␣ with exponent ␣ close to Ϫ1.5, a hallmark of critical state dynamics (Plenz and Thiagarajan, 2007;Klaus et al, 2011).…”

Suppressing cascades of load in interdependent networks

“…We study Bak-TangWiesenfeld sandpile dynamics (23,24) on networks derived from real, interdependent power grids and on sparsely coupled, random regular graphs that approximate the real topologies. Sandpile dynamics are paradigms for the cascades of load, self-organized criticality, and power law distributions of event sizes that pervade disciplines, from neuronal avalanches (25)(26)(27) to cascades among banks (28) to earthquakes (29), landslides (30), forest fires (31,32), solar flares (33,34), and electrical blackouts (15). Sandpile cascades have been extensively studied on isolated networks (35)(36)(37)(38)(39)(40)(41).…”

Critical avalanches and subsampling in map-based neural networks coupled with noisy synapses

“…In particular, the observation of neuronal avalanches motivated the search for computational models presenting this phenomenon [8][9][10][11][12][13]. The key interest in these simulations is to find what are the conditions for the occurrence of power laws in the size and duration distributions of avalanches.…”

“…Moreover, some authors showed that the critical state may optimize the dynamical (input) range [9,14], the memory and learning processes [10], and the computational power of the brain [5][6][7]. However, up to now, the computational models rely on very simplified neuron models like branching processes [8], cellular automata [9,13] or integrate-and-fire neurons [12].…”