Universal Critical Dynamics in High Resolution Neuronal Avalanche Data

Friedman, Nir; Ito, Setsuro; Brinkman, Braden A. W.; Shimono, Masanori; Deville, Robert; Dahmen, Karin A.; Beggs, John M.; Butler, Thomas

doi:10.1103/physrevlett.108.208102

Cited by 449 publications

(689 citation statements)

References 31 publications

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“…1(B)) [29]. This sharply contrasts with the SI state, in which we can define avalanches which display the same statistics assumed to reveal criticality in cultures [25]. Fig 1(A) represents the raster plot with typical avalanches taking place.…”

Section: Avalanches In Spiking Network Modelsmentioning

confidence: 99%

“…The poor statistical significance of LFP avalanche analysis and the ambiguous results it yields has motivated an in-depth exploration of in vitro spiking data of cultures of neurons [25]. In this remarkable work, the authors used multi-unit data from in vitro cultures and addressed a number of properties of critical systems reported by Sethna et al unified theory of criticality of statistical systems [26].…”

Section: Introductionmentioning

confidence: 99%

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Power-law statistics and universal scaling in the absence of criticality

2017

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Critical states are sometimes identified experimentally through power-law statistics or universal scaling functions. We show here that such features naturally emerge from networks in self-sustained irregular regimes away from criticality. In these regimes, statistical physics theory of large interacting systems predict a regime where the nodes have independent and identically distributed dynamics. We thus investigated the statistics of a system in which units are replaced by independent stochastic surrogates, and found the same power-law statistics, indicating that these are not sufficient to establish criticality. We rather suggest that these are universal features of large-scale networks when considered macroscopically. These results put caution on the interpretation of scaling laws found in nature.

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Section: Avalanches In Spiking Network Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Power-law statistics and universal scaling in the absence of criticality

2017

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“…Therefore, additional tests are needed to determine whether criticality underlies the experimentally observed power laws. Two such tests arise from a particular relationship between the size and duration of avalanches, which is predicted to occur at criticality 4,11 and confirmed by our model (Fig. 3d-f and Supplementary Information 1 and 7).…”

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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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“…Our main motivation is due to the recent observation of neuronal avalanches and their presumed relation to SOC. In a wide range of recent experiments [14][15][16][17][18][19][20][21] , neuronal avalanches have been shown to exhibit power law behavior with mean-field exponents. Whether neural dynamics [22] is dissipative or not is still debated, but their noisy dynamics [23] is a certainty, as the post-synaptic neurons receive more or less than their fair share of the ion distributed by the pre-synaptic neuron in the ionic plasma, which permeates the space between synapses.…”

Section: Introductionmentioning

confidence: 99%

Mean-field behavior as a result of noisy local dynamics in self-organized criticality: Neuroscience implications

Moosavi

Montakhab

2014

Phys. Rev. E

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Motivated by recent experiments in neuroscience which indicate that neuronal avalanches exhibit scale invariant behavior similar to self-organized critical systems, we study the role of noisy (nonconservative) local dynamics on the critical behavior of a sandpile model which can be taken to mimic the dynamics of neuronal avalanches. We find that despite the fact that noise breaks the strict local conservation required to attain criticality, our system exhibit true criticality for a wide range of noise in various dimensions, given that conservation is respected on the average. Although the system remains critical, exhibiting finite-size scaling, the value of critical exponents change depending on the intensity of local noise. Interestingly, for sufficiently strong noise level, the critical exponents approach and saturate at their mean-field values, consistent with empirical measurements of neuronal avalanches. This is confirmed for both two and three dimensional models. However, addition of noise does not affect the exponents at the upper critical dimension (D = 4). In addition to extensive finite-size scaling analysis of our systems, we also employ a useful time-series analysis method in order to establish true criticality of noisy systems. Finally, we discuss the implications of our work in neuroscience as well as some implications for general phenomena of criticality in non-equilibrium systems.

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Universal Critical Dynamics in High Resolution Neuronal Avalanche Data

Cited by 449 publications

References 31 publications

Power-law statistics and universal scaling in the absence of criticality

Power-law statistics and universal scaling in the absence of criticality

Adaptation to sensory input tunes visual cortex to criticality

Mean-field behavior as a result of noisy local dynamics in self-organized criticality: Neuroscience implications

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