Universal Critical Dynamics in High Resolution Neuronal Avalanche Data

Experiments on suspensions of charged colloidal rods (fd-virus particles) in external electric fields are performed, which show that a non-equilibrium critical point can be identified. Several transition lines of field-induced phases and states meet at this point and it is shown that there is a length- and time-scale which diverge at the non-equilibrium critical point. The off-critical and critical behavior is characterized, with both power law and logarithmic divergencies. These experiments show that analogous features of the classical, critical divergence of correlation lengths and relaxation times in equilibrium systems are also exhibited by driven systems that are far out of equilibrium, related to phases/states that do not exist in the absence of the external field.

We show that in networks with a hierarchical architecture, critical dynamical behaviors can emerge even when the underlying dynamical processes are not critical. This finding provides explicit insight into current studies of the brain's neuronal network showing power-law avalanches in neural recordings, and provides a theoretical justification of recent numerical findings. Our analysis shows how the hierarchical organization of a network can itself lead to power-law distributions of avalanche sizes and durations, scaling laws between anomalous exponents, and universal functions-even in the absence of self-organized criticality or critical points. This hierarchy-induced phenomenon is independent of, though can potentially operate in conjunction with, standard dynamical mechanisms for generating power laws.

We find novel phase transitions and critical phenomena that occur only outside the linear-response regime of current-driven nonequilibrium states. We consider the strongly interacting (3+1)-dimensional N = 4 large-N(c) SU(N(c)) supersymmetric Yang-Mills theory with a single flavor of fundamental N = 2 hypermultiplet as a microscopic theory. We compute its nonlinear nonballistic quark-charge conductivity by using the AdS/CFT correspondence. We find that the system exhibits a novel nonequilibrium first-order phase transition where the conductivity jumps and the sign of the differential conductivity flips at finite current density. A nonequilibrium critical point is discovered at the end point of the first-order regime. We propose a nonequilibrium steady-state analogue of thermodynamic potential in terms of the gravity-dual theory in order to define the transition point. Nonequilibrium analogues of critical exponents are proposed as well. The critical behavior of the conductivity is numerically confirmed on the basis of these proposals. The present work provides a new example of nonequilibrium phase transitions and nonequilibrium critical points.

Recent experiments have detected a novel form of spontaneous neuronal activity both in vitro and in vivo: neuronal avalanches. The statistical properties of this activity are typical of critical phenomena, with power laws characterizing the distributions of avalanche size and duration. A critical behaviour for the spontaneous brain activity has important consequences on stimulated activity and learning. Very interestingly, these statistical properties can be altered in significant ways in epilepsy and by pharmacological manipulations. In particular, there can be an increase in the number of large events anticipated by the power law, referred to herein as dragon-king avalanches. This behaviour, as verified by numerical models, can originate from a number of different mechanisms. For instance, it is observed experimentally that the emergence of a critical behaviour depends on the subtle balance between excitatory and inhibitory mechanisms acting in the system. Perturbing this balance, by increasing either synaptic excitation or the incidence of depolarized neuronal up-states causes frequent dragon-king avalanches. Conversely, an unbalanced GABAergic inhibition or long periods of low activity in the network give rise to sub-critical behaviour. Moreover, the existence of power laws, common to other stochastic processes, like earthquakes or solar flares, suggests that correlations are relevant in these phenomena. The dragon-king avalanches may then also be the expression of pathological correlations leading to frequent avalanches encompassing all neurons. We will review the statistics of neuronal avalanches in experimental systems. We then present numerical simulations of a neuronal network model introducing within the self-organized criticality framework ingredients from the physiology of real neurons, as the refractory period, synaptic plasticity and inhibitory synapses. The avalanche critical behaviour and the role of dragon-king avalanches will be discussed in relation to different drives, neuronal states and microscopic mechanisms of charge storage and release in neuronal networks.

Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behavior: these avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems. We present a recent model inspired in self-organized criticality, which consists of an electrical network with threshold firing, refractory period, and activity-dependent synaptic plasticity. The model reproduces the critical behavior of the distribution of avalanche sizes and durations measured experimentally. Moreover, the power spectra of the electrical signal reproduce very robustly the power law behavior found in human electroencephalogram (EEG) spectra. We implement this model on a variety of complex networks, i.e., regular, small-world, and scale-free and verify the robustness of the critical behavior.

The brain keeps its overall dynamics in a corridor of intermediate activity and it has been a long standing question what possible mechanism could achieve this task. Mechanisms from the field of statistical physics have long been suggesting that this homeostasis of brain activity could occur even without a central regulator, via self-organization on the level of neurons and their interactions, alone. Such physical mechanisms from the class of self-organized criticality exhibit characteristic dynamical signatures, similar to seismic activity related to earthquakes. Measurements of cortex rest activity showed first signs of dynamical signatures potentially pointing to self-organized critical dynamics in the brain. Indeed, recent more accurate measurements allowed for a detailed comparison with scaling theory of non-equilibrium critical phenomena, proving the existence of criticality in cortex dynamics. We here compare this new evaluation of cortex activity data to the predictions of the earliest physics spin model of self-organized critical neural networks. We find that the model matches with the recent experimental data and its interpretation in terms of dynamical signatures for criticality in the brain. The combination of signatures for criticality, power law distributions of avalanche sizes and durations, as well as a specific scaling relationship between anomalous exponents, defines a universality class characteristic of the particular critical phenomenon observed in the neural experiments. Thus the model is a candidate for a minimal model of a self-organized critical adaptive network for the universality class of neural criticality. As a prototype model, it provides the background for models that may include more biological details, yet share the same universality class characteristic of the homeostasis of activity in the brain.

Phase transitions and critical behavior are crucial issues both in theoretical and experimental neuroscience. We report analytic and computational results about phase transitions and self-organized criticality (SOC) in networks with general stochastic neurons. The stochastic neuron has a firing probability given by a smooth monotonic function Φ(V) of the membrane potential V, rather than a sharp firing threshold. We find that such networks can operate in several dynamic regimes (phases) depending on the average synaptic weight and the shape of the firing function Φ. In particular, we encounter both continuous and discontinuous phase transitions to absorbing states. At the continuous transition critical boundary, neuronal avalanches occur whose distributions of size and duration are given by power laws, as observed in biological neural networks. We also propose and test a new mechanism to produce SOC: the use of dynamic neuronal gains – a form of short-term plasticity probably located at the axon initial segment (AIS) – instead of depressing synapses at the dendrites (as previously studied in the literature). The new self-organization mechanism produces a slightly supercritical state, that we called SOSC, in accord to some intuitions of Alan Turing.

We studied the transition process of antisite arsenic defects in Be-doped low-temperature-grown GaAs layers by measuring the magnetization. This material exhibits bistability at non-equilibrium; at a fixed temperature in a fixed magnetic field a sample relaxes towards two different states, depending on the preceding cooling process. We observed anomalously large magnetization fluctuations in macroscopic samples during the transition from bistability to monostability with gradual change of the temperature. Slowing down of the relaxation of the magnetization is observed as a sample approaches the transition into monostability. Large fluctuations observed from a two-piece sample exhibit intermittent bursts by high-pass filtering and follow a generalized Gumbel probability density distribution. These observations suggest a possibility of the occurrence of a non-equilibrium critical point in this material. Microscopic processes underlying the observed phenomena are discussed with results of first-principles calculations of strain fields.

Biopolymer self-assembly pathways are complicated by the ability of their monomeric subunits to adopt different conformational states. This means nucleation often involves a two-step mechanism where the monomers first condense to form a metastable intermediate, which then converts to a stable polymer by conformational rearrangement of constituent monomers. Nucleation intermediates play a causative role in amyloid diseases such as Alzheimer's and Parkinson's. While existing mathematical models neglect the conversion dynamics, experiments show that conversion events frequently occur on comparable timescales to the condensation of intermediates and growth of mature polymers and thus cannot be ignored. We present a model that explicitly accounts for simultaneous assembly and conversion. To describe conversion, we propose an experimentally motivated initiation-propagation mechanism in which the stable phase arises locally within the intermediate and then spreads by nearest-neighbor interactions, in a manner analogous to one-dimensional Glauber dynamics. Our analysis shows that the competing timescales of assembly and conversion result in a nonequilibrium critical point, separating a regime where intermediates are kinetically unstable from one where conformationally mixed intermediates accumulate. This strongly affects the accumulation rate of the stable biopolymer phase. Our model is uniquely able to explain experimental phenomena such as the formation of mixed intermediates and abrupt changes in the scaling exponent γ, which relates the total monomer concentration to the accumulation rate of the stable phase. This provides a first step toward a general model of two-step biopolymer nucleation, which can quantitatively predict the concentration and composition of biologically crucial intermediates.

In this work, we demonstrate that the thermodynamic potential calculated from the steady-state flow curve using the definition of free energy from irreversible thermodynamics, and the mechanical potential derived from the generalized BMP constitutive equation provide the same information in the unstable and metastable regions of the flow curve. The contribution of normal stresses in both potentials as well as its weight on the position of the stress plateau are explicitly exposed. The plateau stress is univocally defined by the location of the critical shear rates corresponding to the minima in the potential. This demonstration is carried out using experimental data of wormlike micellar solutions for various concentrations and temperatures, including regions close to the non-equilibrium critical point. A method to accurately determine the non-equilibrium critical point (or the critical temperature) in a direct form is provided here. Bifurcation points are defined, notably one located at the high shear-rate branch of the flow curve separating the regions of one real solution and three real solutions. The first normal stress difference exhibits three real solutions as sources of elastic instabilities in the high shear band. The contribution of the second normal-stress difference in both the mechanical and thermodynamic potentials is clearly exposed. These results demonstrate that the non-equilibrium phase transition and the mechanical instability as sources of the banded flow are essentially two manifestations of the same reality.

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.

Course 6 Crackling noise and avalanches: Scaling, critical phenomena, and the renormalization group

Understanding how materials break is still a fundamental problem of science and engineering that has long been empirically investigated, starting from the pioneering works of Leonardo da Vinci and Galileo Galilei. Besides the engineering aspects of the problem, the statistical properties of fracture have attracted a wide interest in the statistical physics community. In this context, fracture is considered an irreversible process ruled by long-range interactions and disorder. Several experiments have revealed that fracture is indeed a complex phenomenon, described by scale invariant laws. Examples notably include the acoustic emission activity prior to fracture, which typically displays an intermittent character and a power law amplitude distribution. This observation suggests the presence of an internal avalanche dynamics possibly ruled by some non-equilibrium critical point. To address this problem, it is conventional to start from the theory of elasticity and consider the elastic stresses associated with a crack. In this context, a single crack propagating through a disordered medium undergoes a depinning transition. Whenever damage is diffusive, however, studying a single crack is not adequate and one usually resorts to lattice models, from simple mean-field like fiber bundles to more complicated and realistic models of disordered elastic media. We conclude making direct analogies between fracture and phase transitions.

Optimization of supercritical phase and combined supercritical/subcritical conversion of lignocellulose for hexose production by using a flow reaction system

We find novel phase transitions and critical phenomena that occur only outside the linear-response regime of current-driven nonequilibrium states, by using the AdS/CFT correspondence. We compute the nonlinear non-ballistic quark-charge conductivity of a strongly-interacting (3 + 1)-dimensional gauge theory, and we find that the system exhibits a nonequilibrium first-order and second-order phase transitions where the sign of the differential conductivity flips at finite current density. The present work provides a new example of nonequilibrium phase transitions and nonequilibrium critical points.