Statistical Analyses Support Power Law Distributions Found in Neuronal Avalanches

Klaus, Andreas; Yu, Shan; Plenz, Dietmar

doi:10.1371/journal.pone.0019779

Cited by 220 publications

(256 citation statements)

References 29 publications

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“…1 E; top, temporal resolution ⌬t ϭ 2 ms; see also Materials and Methods), the cluster size s distributed according to a power law with an exponent of Ϫ1.5 and the distribution showed finite-size scaling, i.e., the cutoff changed systematically with array size (Fig. 1 F) (Klaus et al, 2011). The power law and corresponding exponent identify the ongoing activity as neuronal avalanche dynamics.…”

Section: Higher-order Interactions Are Essential For Neuronal Avalancmentioning

confidence: 86%

“…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). Both theoretical (Kinouchi and Copelli, 2006;Rämö et al, 2007;Tanaka et al, 2009) and empirical studies (Shew et al, 2009(Shew et al, , 2011 suggest that avalanches optimize various aspects of information processing in cortical networks.…”

Section: Higher-order Interactions Are Essential For Neuronal Avalancmentioning

confidence: 99%

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Higher-Order Interactions Characterized in Cortical Activity

Yu¹,

Yang²,

Nakahara³

et al. 2011

J. Neurosci.

200

267

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In the cortex, the interactions among neurons give rise to transient coherent activity patterns that underlie perception, cognition, and action. Recently, it was actively debated whether the most basic interactions, i.e., the pairwise correlations between neurons or groups of neurons, suffice to explain those observed activity patterns. So far, the evidence reported is controversial. Importantly, the overall organization of neuronal interactions and the mechanisms underlying their generation, especially those of high-order interactions, have remained elusive. Here we show that higher-order interactions are required to properly account for cortical dynamics such as ongoing neuronal avalanches in the alert monkey and evoked visual responses in the anesthetized cat. A Gaussian interaction model that utilizes the observed pairwise correlations and event rates and that applies intrinsic thresholding identifies those higher-order interactions correctly, both in cortical local field potentials and spiking activities. This allows for accurate prediction of large neuronal population activities as required, e.g., in brain-machine interface paradigms. Our results demonstrate that higher-order interactions are inherent properties of cortical dynamics and suggest a simple solution to overcome the apparent formidable complexity previously thought to be intrinsic to those interactions.

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Section: Higher-order Interactions Are Essential For Neuronal Avalancmentioning

confidence: 86%

Section: Higher-order Interactions Are Essential For Neuronal Avalancmentioning

confidence: 99%

Higher-Order Interactions Characterized in Cortical Activity

Yu¹,

Yang²,

Nakahara³

et al. 2011

J. Neurosci.

200

267

View full text Add to dashboard Cite

show abstract

“…1g) over a wide range of sizes and durations. This fact is supported by rigorous maximum likelihood fitting methods 10,18 and strict statistical criteria for fit quality (q > 0.1, Methods).…”

mentioning

confidence: 77%

“…Using maximum likelihood methods 10,18 , we fit a truncated power law (truncated at both the head and tail) to the avalanche distributions during visually driven steady state (Supplementary Information 12). The fitting function for the avalanche size distribution was f (S) = S −τ ( xM x=x0 x −τ ) −1 , where the maximum size x M was assumed to be the largest observed size.…”

Section: Methodsmentioning

confidence: 99%

Adaptation to sensory input tunes visual cortex to criticality

et al. 2015

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A long-standing hypothesis at the interface of physics and neuroscience is that neural networks self-organize to the critical point of a phase transition, thereby optimizing aspects of sensory information processing 1-3 . This idea is partially supported by strong evidence for critical dynamics observed in the cerebral cortex 4-10 , but the impact of sensory input on these dynamics is largely unknown. Thus, the foundations of this hypothesis-the self-organization process and how it manifests during strong sensory input-remain unstudied experimentally. Here we show in visual cortex and in a computational model that strong sensory input initially elicits cortical network dynamics that are not critical, but adaptive changes in the network rapidly tune the system to criticality. This conclusion is based on observations of multifaceted scaling laws predicted to occur at criticality 4,11 . Our findings establish sensory adaptation as a self-organizing mechanism that maintains criticality in visual cortex during sensory information processing.Sensory nervous systems adapt, dynamically tuning interactions among large networks of neurons, to cope with a changing environment 12,13 . The principles governing such adaptation at the macroscopic level of neuronal network dynamics are not well understood. Computational models and theory suggest that such adaptation can maintain critical network dynamics [14][15][16] , but these previous studies did not consider the strongly driven regime that is expected during intense sensory input. Indeed, sufficiently strong input may increase the overall excitability of a network by bringing neurons closer to their firing thresholds and potentially tipping the network into a high firing rate regime that is inconsistent with critical dynamics (Supplementary Information 1). Thus, the question remains: does strong sensory input drive cortical network dynamics away from criticality or can adaptation counteract this tendency and maintain the critical regime?Here we addressed this question in turtle visual cortex and in a companion computational model. In our experiments, we obtained long-duration recordings of population neural activity (local field potential, LFP) using a microelectrode array inserted into the geniculo-recipient dorsal cortex (visual cortex) of the turtle eyeattached whole-brain ex vivo preparation 17 (Fig. 1a and Supplementary Information 2). We measured multi-scale spatiotemporal patterns of neural activity while visually stimulating the retina. Similarly, in our model we studied changes in neural network activity in response to changes in external input. Experimentally, and in the model, we assessed whether the measured dynamics were near or far from criticality. For this, we examined statistics and spatiotemporal scaling laws of 'neuronal avalanches' , which are bouts of elevated population activity with correlations in space and time 5 (Fig. 1b).In brief, a neuronal avalanche is defined as a group of LFP peaks, occurring on any electrode, irrespective of location, and sep...

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“…EEG signals themselves have been reported to be highly nonstationary [9]. Direct recording of cortical neurons in animal cortices has provided convincing evidence for the presence of scale-free (self-affine) dynamics in the patterns of neuronal avalanches in cortical neurons [10][11][12][13]. Indeed, neuronal avalanches recorded in the cortex were also found to correlate with beta/gamma band EEG recordings in rodents [14].…”

Section: Introductionmentioning

confidence: 97%