Avalanche Size Distribution Research Articles

Failure process of fiber bundles with random misalignment

We investigate the failure process of fiber bundles with structural disorder represented by the random misalignment of fibers. The strength of fibers is assumed to be constant so that misalignment is the only source of disorder, which results in a heterogeneous load distribution over fibers. We show by analytical calculations and computer simulations that increasing the amount of structural disorder a transition occurs from a perfectly brittle behavior with abrupt global failure to a quasibrittle phase where failure is preceded by breaking avalanches. The size distribution of avalanches follows a power-law functional form with a complex dependence of the exponent on the amount of disorder. In the vicinity of the critical point the avalanche exponent is 3/2; however, with increasing disorder a crossover emerges to a higher exponent 5/2. We show analytically that the mechanical behavior of the bundle of misaligned fiber with no strength disorder can be mapped to an equal load sharing fiber bundle of perfectly aligned fibers with properly selected strength disorder. Published by the American Physical Society 2024

Acoustic emission data based modelling of fracture of glassy polymer

Acoustic emission (AE) activity data resulting from the fracture processes of brittle materials is valuable real time information regarding the evolving state of damage in the material. Here, through a combined experimental and computational study we explore the possibility of utilising the statistical signatures of AE activity data for characterisation of disorder parameter in simulation of tensile fracture of epoxy based polymer. For simulations we use a square random spring network model with quasi-brittle spring behaviour and a normally distributed failure strain threshold. We show that the disorder characteristics while have marginal effect on the power law exponent of the avalanche size distribution, are strongly correlated with the waiting time interval between consecutive record breaking avalanches as well as the total number of records. This sensitivity to disorder is exploited in estimating the disorder parameter suitable for the experiments on tensile failure of epoxy based polymer. The disorder parameter is estimated assuming equivalence between the amplitude distribution of AE data and avalanche size distribution of the simulations. The chosen disorder parameter is shown to well reproduce the failure characteristics in terms of the peak load of the macroscopic response, the power-law behaviour with avalanche dominated fracture type as well as realistic fracture paths.

Non-trivial relationship between behavioral avalanches and internal neuronal dynamics in a recurrent neural network.

Neuronal activity gives rise to behavior, and behavior influences neuronal dynamics, in a closed-loop control system. Is it possible then, to find a relationship between the statistical properties of behavior and neuronal dynamics? Measurements of neuronal activity and behavior have suggested a direct relationship between scale-free neuronal and behavioral dynamics. Yet, these studies captured only local dynamics in brain sub-networks. Here, we investigate the relationship between internal dynamics and output statistics in a mathematical model system where we have access to the dynamics of all network units. We train a recurrent neural network (RNN), initialized in a high-dimensional chaotic state, to sustain behavioral states for durations following a power-law distribution as observed experimentally. Changes in network connectivity due to training affect the internal dynamics of neuronal firings, leading to neuronal avalanche size distributions approximating power-laws over some ranges. Yet, randomizing the changes in network connectivity can leave these power-law features largely unaltered. Specifically, whereas neuronal avalanche duration distributions show some variations between RNNs with trained and randomized decoders, neuronal avalanche size distributions are invariant, in the total population and in output-correlated sub-populations. This is true independent of whether the randomized decoders preserve power-law distributed behavioral dynamics. This demonstrates that a one-to-one correspondence between the considered statistical features of behavior and neuronal dynamics cannot be established and their relationship is non-trivial. Our findings also indicate that statistical properties of the intrinsic dynamics may be preserved, even as the internal state responsible for generating the desired output dynamics is perturbed.

Avalanche scaling law for heterogeneous interfacial fracture

A new approach is proposed to study the statistical law of avalanches due to the fracture of a heterogeneous interface. Firstly, a discrete interface is considered as a bundle of fibers clamped with two elastic circular plates, fiber strength being either a random variable or a stochastic field. Based on the theory of solid mechanics, equations governing the dynamic fracture process of fibers under tension are exactly derived and solved. The statistical law of fracture avalanches is accurately obtained. By tuning parameters, the model covers the whole scheme of stress-transfer mechanism due to fiber breaking from local load sharing to equal load sharing. Results show that the distribution of avalanche size of interfacial fracture follows a power-law relation, with the power exponent in the range [1.5,2.5], depending on both disorders of interface and stiffness of plates. Particularly, the exponent monotonically increases with plate stiffness to the value 2.5, a universal constant obtained by Hemmer and Hansen (1992). Then, the fracture of a laminated interface is analyzed. By discretizing the interface to a set of prismatic elements, the problem reduces to that of a discrete interface. Similar statistical behaviors are also observed. Furthermore, the temporal variation of avalanche scaling law is investigated. It is shown that in the vicinity of collapse point, the exponent of each time window is smaller than the exponent evaluated with the whole time series of event, probably a precursor for imminent catastrophic failure of interface.

A general description of criticality in neural network models

Recent experimental observations have supported the hypothesis that the cerebral cortex operates in a dynamical regime near criticality, where the neuronal network exhibits a mixture of ordered and disordered patterns. However, A comprehensive study of how criticality emerges and how to reproduce it is still lacking. In this study, we investigate coupled networks with conductance-based neurons and illustrate the co-existence of different spiking patterns, including asynchronous irregular (AI) firing and synchronous regular (SR) state, along with a scale-invariant neuronal avalanche phenomenon (criticality). We show that fast-acting synaptic coupling can evoke neuronal avalanches in the mean-dominated regime but has little effect in the fluctuation-dominated regime. In a narrow region of parameter space, the network exhibits avalanche dynamics with power-law avalanche size and duration distributions. We conclude that three stages which may be responsible for reproducing the synchronized bursting: mean-dominated subthreshold dynamics, fast-initiating a spike event, and time-delayed inhibitory cancellation. Remarkably, we illustrate the mechanisms underlying critical avalanches in the presence of noise, which can be explained as a stochastic crossing state around the Hopf bifurcation under the mean-dominated regime. Moreover, we apply the ensemble Kalman filter to determine and track effective connections for the neuronal network. The method is validated on noisy synthetic BOLD signals and could exactly reproduce the corresponding critical network activity. Our results provide a special perspective to understand and model the criticality, which can be useful for large-scale modeling and computation of brain dynamics.

Coupled size and temperature effects on intermittent plasticity of BCC micro-crystals

The intermittent plasticity of pure Mo microcrystals with diameters from 500 to 3500 nm was studied using micro-pillar compression methodology at temperatures ranging from 25 °C to 200 °C, in order to explore its dependence on external size and temperature. The technological background is the potential value of BCC metals in the manufacture of high-temperature components in modern fields requiring miniaturization, such as microelectronics. We analyzed plastic fluctuations in terms of wildness, avalanche size distribution, and burst peak velocity. This reveals coupled size and temperature effects on a mild-to-wild transition: the transition temperature decreases with decreasing sample diameter, while the transition diameter increases with rising temperature. In addition, strain-rate sensitivity tests were conducted, implying that mild plasticity is associated to screw dislocation motions controlled by thermally activated nucleation of kink-pairs, whereas wild plasticity is nearly athermal. This experimental observation is interpreted by a damping effect of the thermally activated motion of screw dislocations on the propagation of avalanches. From this, we propose a controlling parameter based on a simple dislocation source model, which unifies the coupled sample size and temperature effects on the mild-to-wild transition, and which could be of practical significance for microscale applications.

Noise and spike-time-dependent plasticity drive self-organized criticality in spiking neural network: Toward neuromorphic computing

Self-organized criticality (SoC) may optimize information transmission, encoding, and storage in the brain. Therefore, the underlying mechanism of the SoC provides significant insight for large-scale neuromorphic computing. We hypothesized that noise and stochastic spiking plays an essential role in SoC development in spiking neural networks (SNNs). We demonstrated that under appropriate noise levels and spike-time-dependent plasticity (STDP) parameters, an SNN evolves a SoC-like state characterized by a power-law distribution of neuronal avalanche size in a self-organized manner. Consistent with the physiological findings, the development of SNN was characterized by a transition from a subcritical state to a supercritical state and then to a critical state. Excitatory STDP with an asymmetric time window dominated the early phase of development; however, it destabilized the network and transitioned to the supercritical state. Synchronized bursts in the supercritical state enable inhibitory STDP with a symmetric time window, induce the development of inhibitory synapses, and stabilize the network toward the critical state. This sequence of transitions was observed when the appropriate noise level and STDP parameters were set to the initial conditions. Our results suggest that noise or stochastic spiking plays an essential role in SoC development and self-optimizes SNN for computation. Such neural mechanisms of noise harnessing would offer insight into the development of energy-efficient neuromorphic computing.

Analysis of Acoustic Emission Energy Distribution and Avalanche Dynamics of Sandstone with Different Particle Sizes.

The mechanical properties of sandstone have an important impact on the stability of the coal mine roof and floor, sandstone gas mining, and underground engineering safety. In order to study the critical characteristics on the failure process of sandstone with different particle sizes under uniaxial compression conditions, avalanche dynamics theory and a critical model are used to analyze the distribution of acoustic emission (AE) parameters, and the maximum likelihood estimation is used to accurately estimate the critical parameters. The results showed that the AE phenomenon of sandstone can be divided into four stages: initial compaction period, quiet period, crack stable growth period and outbreak period. During the process of compression failure, the larger the particle size is, the more seriously the sandstone is damaged. The AE energy probability density distribution follows single power-law distribution, and the AE energy critical exponent is 1.20 and follows the characteristics of scale-free regarding the power-law distribution on the particle sizes. When the stress runs up to 90% of peak stress, the bifurcation ratio increases sharply and shows the characteristics of the critical state. The waiting time and the avalanche size distribution follow double power-law distribution, and the inflection points are 0.03 and 37. Before and after the inflection point, the waiting time critical exponent and the avalanche size critical exponent are 1.90, 0.40 and 2.40, 1.60. This shows that the dynamic evolution process of sandstone under uniaxial compression condition can be characterized well by the fiber bundle model.

Abelian sandpiles on cylinders

We study the Abelian sandpile model on a special playground, a cylinder of width w and of circumference c. When , we describe a phenomenon which has not been observed in other geometries: the probability distribution of avalanche sizes has a ladder structure, with the first step consisting of avalanches of size up to that are essentially equiprobable, except for a small exponential tail of order about 10 c. We explain this phenomenon and describe subsequent steps.

Recurrent activity in neuronal avalanches

A new statistical analysis of large neuronal avalanches observed in mouse and rat brain tissues reveals a substantial degree of recurrent activity and cyclic patterns of activation not seen in smaller avalanches. To explain these observations, we adapted a model of structural weakening in materials. In this model, dynamical weakening of neuron firing thresholds closely replicates experimental avalanche size distributions, firing number distributions, and patterns of cyclic activity. This agreement between model and data suggests that a mechanism like dynamical weakening plays a key role in recurrent activity found in large neuronal avalanches. We expect these results to illuminate the causes and dynamics of large avalanches, like those seen in seizures.

Mechanical excitation and marginal triggering during avalanches in sheared amorphous solids.

We study plastic strain during individual avalanches in overdamped particle-scale molecular dynamics (MD) and mesoscale elastoplastic models (EPM) for amorphous solids sheared in the athermal quasistatic limit. We show that the spatial correlations in plastic activity exhibit a short length scale that grows as t^{3/4} in MD and ballistically in EPM, which is generated by mechanical excitation of nearby sites not necessarily close to their stability thresholds, and a longer lengthscale that grows diffusively for both models and is associated with remote marginally stable sites. These similarities in spatial correlations explain why simple EPMs accurately capture the size distribution of avalanches observed in MD, though the temporal profiles and dynamical critical exponents are quite different.

Structural Modularity Tunes Mesoscale Criticality in Biological Neuronal Networks.

Numerous studies suggest that biological neuronal networks self-organize toward a critical state with stable recruitment dynamics. Individual neurons would then statistically activate exactly one further neuron during activity cascades termed neuronal avalanches. Yet, it is unclear if and how this can be reconciled with the explosive recruitment dynamics within neocortical minicolumns in vivo and within neuronal clusters in vitro, which indicates that neurons form supercritical local circuits. Theoretical studies propose that modular networks with a mix of regionally subcritical and supercritical dynamics would create apparently critical dynamics, resolving this inconsistency. Here, we provide experimental support by manipulating the structural self-organization process of networks of cultured rat cortical neurons (either sex). Consistent with the prediction, we show that increasing clustering in neuronal networks developing in vitro strongly correlates with avalanche size distributions transitioning from supercritical to subcritical activity dynamics. Avalanche size distributions approximated a power law in moderately clustered networks, indicating overall critical recruitment. We propose that activity-dependent self-organization can tune inherently supercritical networks toward mesoscale criticality by creating a modular structure in neuronal networks.SIGNIFICANCE STATEMENT Critical recruitment dynamics in neuronal networks are considered optimal for information processing in the brain. However, it remains heavily debated how neuronal networks would self-organize criticality by detailed fine-tuning of connectivity, inhibition, and excitability. We provide experimental support for theoretical considerations that modularity tunes critical recruitment dynamics at the mesoscale level of interacting neuron clusters. This reconciles reports of supercritical recruitment dynamics in local neuron clusters with findings on criticality sampled at mesoscopic network scales. Intriguingly, altered mesoscale organization is a prominent aspect of various neuropathological diseases currently investigated in the framework of criticality. We therefore believe that our findings would also be of interest for clinical scientists searching to link the functional and anatomic signatures of such brain disorders.

Power spectrum and critical exponents in the 2D stochastic Wilson–Cowan model

The power spectrum of brain activity is composed by peaks at characteristic frequencies superimposed to a background that decays as a power law of the frequency, f^{-beta }, with an exponent beta close to 1 (pink noise). This exponent is predicted to be connected with the exponent gamma related to the scaling of the average size with the duration of avalanches of activity. “Mean field” models of neural dynamics predict exponents beta and gamma equal or near 2 at criticality (brown noise), including the simple branching model and the fully-connected stochastic Wilson–Cowan model. We here show that a 2D version of the stochastic Wilson–Cowan model, where neuron connections decay exponentially with the distance, is characterized by exponents beta and gamma markedly different from those of mean field, respectively around 1 and 1.3. The exponents alpha and tau of avalanche size and duration distributions, equal to 1.5 and 2 in mean field, decrease respectively to 1.29pm 0.01 and 1.37pm 0.01. This seems to suggest the possibility of a different universality class for the model in finite dimension.

Inferring the stability of concentrated emulsions from droplet configuration information

When droplets are tightly packed in a 2D microchannel, coalescence of a pair of droplets can trigger an avalanche of coalescence events that propagate through the entire emulsion. This propagation is found to be stochastic, i.e. every coalescence event does not necessarily trigger another. To study how the local probabilistic propagation affects the dynamics of the avalanche, as a whole, a stochastic agent based model is used. Taking as input, i) how the droplets are packed (configuration) and ii) a measure of local probabilistic propagation (experimentally derived; function of fluid and other system parameters), the model predicts the expected size distribution of avalanches. In this article, we investigate how droplet configuration affects the avalanche dynamics. We find the mean size of these avalanches to depend non-trivially on how droplets are packed together. Large variations in the avalanche dynamics are observed when droplet packing are different, even when the other system properties (number of droplets, fluid properties, channel geometry, etc.) are kept constant. Bidisperse emulsions show less variation in the dynamics and they are surprisingly more stable than monodisperse emulsions. To get a systems-level understanding of how a given droplet-configuration either facilitates or impedes the propagation of an avalanche, we employ a graph-theoretic analysis, where emulsions are expressed as graphs. We find that the properties of the underlying graph, namely the mean degree and the algebraic connectivity, are well correlated with the observed avalanche dynamics. We exploit this dependence to derive a data-based model that predicts the expected avalanche sizes from the properties of the graph.

Scale free avalanches in excitatory-inhibitory populations of spiking neurons with conductance based synaptic currents

We investigate spontaneous critical dynamics of excitatory and inhibitory (EI) sparsely connected populations of spiking leaky integrate-and-fire neurons with conductance-based synapses. We use a bottom-up approach to derive a single neuron gain function and a linear Poisson neuron approximation which we use to study mean-field dynamics of the EI population and its bifurcations. In the low firing rate regime, the quiescent state loses stability due to saddle-node or Hopf bifurcations. In particular, at the Bogdanov-Takens (BT) bifurcation point which is the intersection of the Hopf bifurcation and the saddle-node bifurcation lines of the 2D dynamical system, the network shows avalanche dynamics with power-law avalanche size and duration distributions. This matches the characteristics of low firing spontaneous activity in the cortex. By linearizing gain functions and excitatory and inhibitory nullclines, we can approximate the location of the BT bifurcation point. This point in the control parameter phase space corresponds to the internal balance of excitation and inhibition and a slight excess of external excitatory input to the excitatory population. Due to the tight balance of average excitation and inhibition currents, the firing of the individual cells is fluctuation-driven. Around the BT point, the spiking of neurons is a Poisson process and the population average membrane potential of neurons is approximately at the middle of the operating interval [V_{Rest}, V_{th}]. Moreover, the EI network is close to both oscillatory and active-inactive phase transition regimes.

Success of social inequality measures in predicting critical or failure points in some models of physical systems

Statistical physicists and social scientists both extensively study some characteristic features of the unequal distributions of energy, cluster, or avalanche sizes and of income, wealth, etc., among the particles (or sites) and population, respectively. While physicists concentrate on the self-similar (fractal) structure (and the characteristic exponents) of the largest (percolating) cluster or avalanche, social scientists study the inequality indices such as Gini and Kolkata, given by the non-linearity of the Lorenz function representing the cumulative fraction of the wealth possessed by different fractions of the population. Here, using results from earlier publications and some new numerical and analytical results, we reviewed how the above-mentioned social inequality indices, when extracted from the unequal distributions of energy (in kinetic exchange models), cluster sizes (in percolation models), or avalanche sizes (in self-organized critical or fiber bundle models) can help in a major way in providing precursor signals for an approaching critical point or imminent failure point. Extensive numerical and some analytical results have been discussed.

Prediction of imminent failure using supervised learning in a fiber bundle model.

Prediction of a breakdown in disordered solids under external loading is a question of paramount importance. Here we use a fiber bundle model for disordered solids and record the time series of the avalanche sizes and energy bursts. The time series contain statistical regularities that not only signify universality in the critical behavior of the process of fracture, but also reflect signals of proximity to a catastrophic failure. A systematic analysis of these series using supervised machine learning can predict the time to failure. Different features of the time series become important in different variants of training samples. We explain the reasons for such a switch over of importance among different features. We show that inequality measures for avalanche time series play a crucial role in imminent failure predictions, especially for imperfect training sets, i.e., when simulation parameters of training samples differ considerably from those of the testing samples. We also show the variation of predictability of the system as the interaction range and strengths of disorders are varied in the samples, varying the failure mode from brittle to quasibrittle (with interaction range) and from nucleation to percolation (with disorder strength). The effectiveness of the supervised learning is best when the samples just enter the quasibrittle mode of failure showing scale-free avalanche size distributions.

Power-law statistics of synchronous transition in inhibitory neuronal networks

We investigate the relationship between the synchronous transition and the power law behavior in spiking networks which are composed of inhibitory neurons and balanced by dc current. In the region of the synchronous transition, the avalanche size and duration distribution obey a power law distribution. We demonstrate the robustness of the power law for event sizes at different parameters and multiple time scales. Importantly, the exponent of the event size and duration distribution can satisfy the critical scaling relation. By changing the network structure parameters in the parameter region of transition, quasicriticality is observed, that is, critical exponents depart away from the criticality while still hold approximately to a dynamical scaling relation. The results suggest that power law statistics can emerge in networks composed of inhibitory neurons when the networks are balanced by external driving signal.

Near universal values of social inequality indices in self-organized critical models

We have studied few social inequality measures associated with the sub-critical dynamical features (measured in terms of the avalanche size distributions) of four self-organized critical models while the corresponding systems approach their respective stationary critical states. It has been observed that these inequality measures (specifically the Gini and Kolkata indices) exhibit nearly universal values though the models studied here are widely different, namely the Bak–Tang–Wiesenfeld sandpile, the Manna sandpile and the quenched Edwards–Wilkinson interface, and the fiber bundle interface. These observations suggest that the self-organized critical systems have broad similarity in terms of these inequality measures. A comparison with similar earlier observations in the data of socio-economic systems with unrestricted competitions suggest the emergent inequality as a result of the possible proximity to the self-organized critical states.

Yielding transition of a two dimensional glass former under athermal cyclic shear deformation.

We study numerically the yielding transition of a two dimensional model glass subjected to athermal quasi-static cyclic shear deformation, with the aim of investigating the effect on the yielding behavior of the degree of annealing, which in turn depends on the preparation protocol. We find two distinct regimes of annealing separated by a threshold energy. Poorly annealed glasses progressively evolve toward the threshold energy as the strain amplitude is increased toward the yielding value. Well annealed glasses with initial energies below the threshold energy exhibit stable behavior, with a negligible change in energy with increasing strain amplitude, until they yield. Discontinuities in energy and stress at yielding increase with the degree of annealing, consistent with recent results found in three dimensions. We observe a significant structural change with strain amplitude that closely mirrors the changes in energy and stresses. We investigate groups of particles that are involved in plastic rearrangements. We analyze the distributions of avalanche sizes, of clusters of connected rearranging particles, and related quantities, employing finite size scaling analysis. We verify previously investigated relations between exponents characterizing these distributions.