Neutral Theory and Scale-Free Neural Dynamics

Martinello, Matteo; Hidalgo, Jorge; Maritan, Amos; Santo, Serena Di; Plenz, Dietmar; Muñoz, Miguel A.

doi:10.1103/physrevx.7.041071

Cited by 82 publications

(90 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, in contrast with other computational models, causal information is not explicitly needed/employed here to determine avalanches -they are determined from raw data-and results can be straightforwardly compared to experimental ones for neuronal avalanches, without conceptual gaps (40).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Landau–Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization

Santo

Villegas

Burioni

et al. 2018

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

180

177

View full text Add to dashboard Cite

Understanding the origin, nature and functional significance of complex patterns of neural activity, as recorded by diverse electrophysiological and neuroimaging techniques, is a central challenge in Neuroscience. Such patterns include collective oscillations, emerging out of neural synchronization, as well as highly-heterogeneous outbursts of activity interspersed by periods of quiescence, called "neuronal avalanches". Much debate has been generated about the possible scale-invariance or criticality of such avalanches, and its relevance for brain function. Aimed at shedding light onto this, here we analyze the large-scale collective properties of the cortex by using a mesoscopic approach, following the principle of parsimony of Landau-Ginzburg. Our model is similar to that of Wilson-Cowan for neural dynamics but, crucially, including stochasticity and space; synaptic plasticity and inhibition are considered as possible regulatory mechanisms. Detailed analyses uncover a phase diagram including down-states, synchronous, asynchronous, and up-state phases, and reveal that empirical findings for neuronal avalanches are consistently reproduced by tuning our model to the edge of synchronization. This reveals that the putative criticality of cortical dynamics does not correspond to a quiescent-to-active phase transition, as usually assumed in theoretical approaches, but to a synchronization phase transition, at which incipient oscillations and scalefree avalanches coexist. Furthermore, our model also accounts for up and down states as they occur, e.g. during deep sleep. The present approach constitutes a framework to rationalize the possible collective phases and phase transitions of cortical networks in simple terms, thus helping shed light into basic aspects of brain functioning from a very broad perspective.Cortical dynamics | Neuronal avalanches | Criticality | Synaptic plasticity T he cerebral cortex exhibits spontaneous activity even in the absence of any task or external stimuli (1-3). A salient aspect of this, so-called, resting-state dynamics, as revealed by in vivo and in vitro measurements, is that it exhibits outbursts of electrochemical activity, characterized by brief episodes of coherence -during which many neurons fire within a narrow time window-interspaced by periods of relative quiescence, giving rise to collective oscillatory rhythms (4, 5). Shedding light on the origin, nature, and functional meaning of such an intricate dynamics is a fundamental challenge in Neuroscience (6).Upon experimentally enhancing the spatio-temporal resolution of activity recordings, Beggs and Plenz made the remarkable finding that, actually, synchronized outbursts of neural activity could be decomposed into complex spatio-temporal patterns, thereon named "neuronal avalanches" (7). The sizes and durations of such avalanches were reported to be distributed as power-laws, i.e. to be organized in a scale-free way, limited only by network size (7). Furthermore, they obey finite-size scaling (8), a trademark of scale invariance...

show abstract

Section: Conclusion and Discussionmentioning

confidence: 99%

Landau–Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization

Santo

Villegas

Burioni

et al. 2018

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

180

177

View full text Add to dashboard Cite

show abstract

“…In that model, however, criticality emerges only with a definition of avalanches that takes in account the causality of different firings. If one uses a criterion based only on temporal binning and proximity, as done usually in experiments where causality is not observable, one finds exponential distributions of avalanches, and no critical behaviour is observed [38].…”

Section: Discussionmentioning

confidence: 99%

“…As recently pointed out in Ref. [38], two different methods have been used to define avalanches. The first is based on the temporal proximity of neural activity, so that if activity happens in contiguous time bins, it is considered belonging to the same avalanche.…”

Section: B Critical Behaviour At the Edge Of Instabilitymentioning

confidence: 99%

Hysteresis, neural avalanches, and critical behavior near a first-order transition of a spiking neural network

et al. 2018

View full text Add to dashboard Cite

Many experimental results, both in vivo and in vitro, support the idea that the brain cortex operates near a critical point and at the same time works as a reservoir of precise spatiotemporal patterns. However, the mechanism at the basis of these observations is still not clear. In this paper we introduce a model which combines both these features, showing that scale-free avalanches are the signature of a system posed near the spinodal line of a first-order transition, with many spatiotemporal patterns stored as dynamical metastable attractors. Specifically, we studied a network of leaky integrate-and-fire neurons whose connections are the result of the learning of multiple spatiotemporal dynamical patterns, each with a randomly chosen ordering of the neurons. We found that the network shows a first-order transition between a low-spiking-rate disordered state (down), and a high-rate state characterized by the emergence of collective activity and the replay of one of the stored patterns (up). The transition is characterized by hysteresis, or alternation of up and down states, depending on the lifetime of the metastable states. In both cases, critical features and neural avalanches are observed. Notably, critical phenomena occur at the edge of a discontinuous phase transition, as recently observed in a network of glow lamps.

show abstract

“…In the present study, we combined 2-photon imaging with an advanced, yet robust spike density estimation (Pachitariu et al, 2018) providing further support that spatiotemporal activity in spiking pyramidal groups organizes as neuronal avalanches. We also note that spiking between pyramidal neurons is significantly correlated, excluding models for the generation of power laws that feature no interaction between neurons (Martinello et al, 2017;Touboul and Destexhe, 2017). Our findings of robust power laws even under nonstationary conditions, i.e.…”

Section: Discussionmentioning

confidence: 56%

Neuronal avalanches in input and associative layers of auditory cortex

Bowen

Winkowski

Seshadri³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

The primary auditory cortex processes acoustic sequences for the perception of behaviorally meaningful sounds such as speech. Sound information arrives at its input layer 4 from where activity propagates to associative layer 2/3. It is currently not known whether there is a particular organization of neuronal population activity that is stable across layers and sound levels during sound processing. We used in vivo 2-photon imaging of pyramidal neurons in cortical layers L4 and L2/3 of mouse A1 to characterize the populations of neurons that were active spontaneously, i.e. in the absence of a sound stimulus, and those recruited by single-frequency tonal stimuli at different sound levels.Single-frequency sounds recruited neurons of widely ranging frequency selectivity in both layers. We defined neural ensembles as neurons being active within or during successive temporal windows at the temporal resolution of our imaging. For both layers, neuronal ensembles were highly variable in size during spontaneous activity as well as during sound presentation. Ensemble sizes distributed according to power laws, the hallmark of neuronal avalanches, and were similar across sound levels. Avalanches activated by sound were composed of neurons with diverse tuning preference, yet with selectivity independent of avalanche size. Thus, spontaneous and evoked activity in both L4 and L2/3 of A1 are composed of neuronal avalanches with similar power law relationships.Our results demonstrate network principles linked to maximal dynamic range, optimal information transfer and matching complexity between L4 and L2/3 to shape population activity in auditory cortex.

show abstract

Neutral Theory and Scale-Free Neural Dynamics

Cited by 82 publications

References 52 publications

Landau–Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization

Landau–Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization

Hysteresis, neural avalanches, and critical behavior near a first-order transition of a spiking neural network

Neuronal avalanches in input and associative layers of auditory cortex

Contact Info

Product

Resources

About