Temporal correlations in neuronal avalanche occurrence

Abstract

Ongoing cortical activity consists of sequences of synchronized bursts, named neuronal avalanches, whose size and duration are power law distributed. These features have been observed in a variety of systems and conditions, at all spatial scales, supporting scale invariance, universality and therefore criticality. However, the mechanisms leading to burst triggering, as well as the relationship between bursts and quiescence, are still unclear. The analysis of temporal correlations constitutes a major step towards a deeper understanding of burst dynamics. Here, we investigate the relation between avalanche sizes and quiet times, as well as between sizes of consecutive avalanches recorded in cortex slice cultures. We show that quiet times depend on the size of preceding avalanches and, at the same time, influence the size of the following one. Moreover we evidence that sizes of consecutive avalanches are correlated. In particular, we show that an avalanche tends to be larger or smaller than the following one for short or long time separation, respectively. Our analysis represents the first attempt to provide a quantitative estimate of correlations between activity and quiescence in the framework of neuronal avalanches and will help to enlighten the mechanisms underlying spontaneous activity.

Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity

“…2C, upper panel). The large variability in the burst sizes and the durations of the inter burst intervals is related to the research of neuronal avalanches1920, which is beyond the scope of the current work.…”

Criticality Maximizes Complexity in Neural Tissue

“…Concurrently, the field of criticality in neural systems has received extensive treatment, primarily via studies of cotemporal sequences of neural activity, otherwise known as “neural avalanches” (Beggs and Plenz, 2003 , 2004 ; Beggs and Timme, 2012 ). Many recent studies have produced evidence that suggests neural systems are poised at or near a critical point (Beggs and Plenz, 2003 ; Petermann et al, 2006 ; Mazzoni et al, 2007 ; Gireesh and Plenz, 2008 ; Pasquale et al, 2008 ; Hahn et al, 2010 ; Friedman et al, 2012 ; Priesemann et al, 2013 ; Lombardi et al, 2014 , 2016 ; Priesemann et al, 2014 ; Williams-Garcia et al, 2014 ; Shew et al, 2015 ). Furthermore, many studies (see Beggs, 2008 ; Chialvo, 2010 ; Beggs and Timme, 2012 for reviews) have found important implications for the brain if it is indeed operating at or near a critical point, such as optimal communication (Beggs and Plenz, 2003 ; Bertschinger and Natschlager, 2004 ; Ramo et al, 2007 ; Tanaka et al, 2009 ; Shew et al, 2011 ), information storage (Socolar and Kauffman, 2003 ; Kauffman et al, 2004 ; Haldeman and Beggs, 2005 ), computational power (Bertschinger and Natschlager, 2004 ), dynamic range (Kinouchi and Copelli, 2006 ; Shew et al, 2009 ), and phase sychrony (Yang et al, 2012 ).…”

Correlations induced by depressing synapses in critically self-organized networks with quenched dynamics

“…In order to explain the self-organization around the critical point, Arcangelis et al [10] introduced a model with synaptic depression and synaptic recovery (see also [11,12]). They obtained Self-Organized Criticality (SOC) and other very interesting results but their synaptic depression mechanism has the undesirable feature of depending on non-local information.…”