Subcritical Dynamics Research Articles

Model-based machine learning of critical brain dynamics

Criticality can be exactly demonstrated in certain models of brain activity, yet it remains challenging to identify in empirical data. We trained a fully connected deep neural network to learn the phases of an excitable model unfolding on the anatomical connectome of human brain. This network was then applied to brain-wide fMRI data acquired during the descent from wakefulness to deep sleep. We report high correlation between the predicted proximity to the critical point and the exponents of cluster size distributions, indicative of subcritical dynamics. This result demonstrates that conceptual models can be leveraged to identify the dynamical regime of real neural systems.

Understanding earthquake precursors: from subcritical instabilities to catastrophic events

The collapse of man-made and natural structures is a complex phenomenon that has been studied for centuries. Existing models often focus on a ‘critical point’ where failure becomes imminent. This work presents a radically different perspective: large earthquakes may not arise from critical states, but instead develop dynamically from the subcritical regime as rare, extreme events. Our approach hinges on an extension of Onsager’s reciprocal theorem, allowing us to delve into this subcritical realm. We demonstrate that within such a regime, excitable systems, like those underlying earthquakes, are dynamically renormalised towards a nonlocal equilibrium. For these systems, the maximum entropy production of at least two interacting phases is used to replace the local equilibrium assumption for the subcritical state. Typically, dissipative processes at larger scales arrest these self-amplifying feedbacks. However, in rare instances, they can morph into intricate tensor networks of instabilities that ripple from microscopic scales to the entire system, culminating in an extreme event like a catastrophic earthquake. This novel framework offers a potentially deeper understanding of earthquake precursors and paves the way for exploring earthquake prediction based on the statistics of subcritical dynamics.

Antiepileptic drugs induce subcritical dynamics in human cortical networks

Cortical network functioning critically depends on finely tuned interactions to afford neuronal activity propagation over long distances while avoiding runaway excitation. This importance is highlighted by the pathological consequences and impaired performance resulting from aberrant network excitability in psychiatric and neurological diseases, such as epilepsy. Theory and experiment suggest that the control of activity propagation by network interactions can be adequately described by a branching process. This hypothesis is partially supported by strong evidence for balanced spatiotemporal dynamics observed in the cerebral cortex; however, evidence of a causal relationship between network interactions and cortex activity, as predicted by a branching process, is missing in humans. Here this cause-effect relationship is tested by monitoring cortex activity under systematic pharmacological reduction of cortical network interactions with antiepileptic drugs. This study reports that cortical activity cascades, presented by the propagating patterns of epileptic spikes, as well as temporal correlations decline precisely as predicted for a branching process. The results provide a missing link to the branching process theory of cortical network function with implications for understanding the foundations of cortical excitability and its monitoring in conditions like epilepsy.

Spike-Timing-Dependent Plasticity With Axonal Delay Tunes Networks of Izhikevich Neurons to the Edge of Synchronization Transition With Scale-Free Avalanches.

Critical brain hypothesis has been intensively studied both in experimental and theoretical neuroscience over the past two decades. However, some important questions still remain: (i) What is the critical point the brain operates at? (ii) What is the regulatory mechanism that brings about and maintains such a critical state? (iii) The critical state is characterized by scale-invariant behavior which is seemingly at odds with definitive brain oscillations? In this work we consider a biologically motivated model of Izhikevich neuronal network with chemical synapses interacting via spike-timing-dependent plasticity (STDP) as well as axonal time delay. Under generic and physiologically relevant conditions we show that the system is organized and maintained around a synchronization transition point as opposed to an activity transition point associated with an absorbing state phase transition. However, such a state exhibits experimentally relevant signs of critical dynamics including scale-free avalanches with finite-size scaling as well as critical branching ratios. While the system displays stochastic oscillations with highly correlated fluctuations, it also displays dominant frequency modes seen as sharp peaks in the power spectrum. The role of STDP as well as time delay is crucial in achieving and maintaining such critical dynamics, while the role of inhibition is not as crucial. In this way we provide possible answers to all three questions posed above. We also show that one can achieve supercritical or subcritical dynamics if one changes the average time delay associated with axonal conduction.

Critical Dynamics Mediate Learning of New Distributed Memory Representations in Neuronal Networks

We explore the possible role of network dynamics near a critical point in the storage of new information in silico and in vivo, and show that learning and memory may rely on neuronal network features mediated by the vicinity of criticality. Using a mean-field, attractor-based model, we show that new information can be consolidated into attractors through state-based learning in a dynamical regime associated with maximal susceptibility at the critical point. Then, we predict that the subsequent consolidation process results in a shift from critical to sub-critical dynamics to fully encapsulate the new information. We go on to corroborate these findings using analysis of rodent hippocampal CA1 activity during contextual fear memory (CFM) consolidation. We show that the dynamical state of the CA1 network is inherently poised near criticality, but the network also undergoes a shift towards sub-critical dynamics due to successful consolidation of the CFM. Based on these findings, we propose that dynamical features associated with criticality may be universally necessary for storing new memories.

Hamiltonian Analysis of Subcritical Stochastic Epidemic Dynamics.

We extend a technique of approximation of the long-term behavior of a supercritical stochastic epidemic model, using the WKB approximation and a Hamiltonian phase space, to the subcritical case. The limiting behavior of the model and approximation are qualitatively different in the subcritical case, requiring a novel analysis of the limiting behavior of the Hamiltonian system away from its deterministic subsystem. This yields a novel, general technique of approximation of the quasistationary distribution of stochastic epidemic and birth-death models and may lead to techniques for analysis of these models beyond the quasistationary distribution. For a classic SIS model, the approximation found for the quasistationary distribution is very similar to published approximations but not identical. For a birth-death process without depletion of susceptibles, the approximation is exact. Dynamics on the phase plane similar to those predicted by the Hamiltonian analysis are demonstrated in cross-sectional data from trachoma treatment trials in Ethiopia, in which declining prevalences are consistent with subcritical epidemic dynamics.

Cascades and cognitive state: focused attention incurs subcritical dynamics.

The analysis of neuronal avalanches supports the hypothesis that the human cortex operates with critical neural dynamics. Here, we investigate the relationship between cascades of activity in electroencephalogram data, cognitive state, and reaction time in humans using a multimodal approach. We recruited 18 healthy volunteers for the acquisition of simultaneous electroencephalogram and functional magnetic resonance imaging during both rest and during a visuomotor cognitive task. We compared distributions of electroencephalogram-derived cascades to reference power laws for task and rest conditions. We then explored the large-scale spatial correspondence of these cascades in the simultaneously acquired functional magnetic resonance imaging data. Furthermore, we investigated whether individual variability in reaction times is associated with the amount of deviation from power law form. We found that while resting state cascades are associated with approximate power law form, the task state is associated with subcritical dynamics. Furthermore, we found that electroencephalogram cascades are related to blood oxygen level-dependent activation, predominantly in sensorimotor brain regions. Finally, we found that decreased reaction times during the task condition are associated with increased proximity to power law form of cascade distributions. These findings suggest that the resting state is associated with near-critical dynamics, in which a high dynamic range and a large repertoire of brain states may be advantageous. In contrast, a focused cognitive task induces subcritical dynamics, which is associated with a lower dynamic range, which in turn may reduce elements of interference affecting task performance.

Marginally subcritical dynamics explain enhanced stimulus discriminability under attention.

Recent experimental and theoretical work has established the hypothesis that cortical neurons operate close to a critical state which describes a phase transition from chaotic to ordered dynamics. Critical dynamics are suggested to optimize several aspects of neuronal information processing. However, although critical dynamics have been demonstrated in recordings of spontaneously active cortical neurons, little is known about how these dynamics are affected by task-dependent changes in neuronal activity when the cortex is engaged in stimulus processing. Here we explore this question in the context of cortical information processing modulated by selective visual attention. In particular, we focus on recent findings that local field potentials (LFPs) in macaque area V4 demonstrate an increase in γ-band synchrony and a simultaneous enhancement of object representation with attention. We reproduce these results using a model of integrate-and-fire neurons where attention increases synchrony by enhancing the efficacy of recurrent interactions. In the phase space spanned by excitatory and inhibitory coupling strengths, we identify critical points and regions of enhanced discriminability. Furthermore, we quantify encoding capacity using information entropy. We find a rapid enhancement of stimulus discriminability with the emergence of synchrony in the network. Strikingly, only a narrow region in the phase space, at the transition from subcritical to supercritical dynamics, supports the experimentally observed discriminability increase. At the supercritical border of this transition region, information entropy decreases drastically as synchrony sets in. At the subcritical border, entropy is maximized under the assumption of a coarse observation scale. Our results suggest that cortical networks operate at such near-critical states, allowing minimal attentional modulations of network excitability to substantially augment stimulus representation in the LFPs.

Coupled longitudinal–transverse dynamics of an axially accelerating beam

The coupled longitudinal–transverse nonlinear dynamics of an axially accelerating beam is numerically investigated; this problem is classified as a parametrically excited gyroscopic system. The axial speed is assumed to be comprised of a constant mean value along with harmonic fluctuations. Hamilton’s principle is employed to derive the equations of motion of the system which are in the form of two coupled partial differential equations. The equations are discretized using the Galerkin method, which yields a set of coupled second-order nonlinear ordinary differential equations with time-dependent coefficients. The sub-critical dynamics of the system is examined via the pseudo-arclength continuation technique, while the global dynamics is investigated using direct time integration. The mean axial speed and the amplitude of the speed variations are varied so as to construct the bifurcation diagrams of Poincaré maps. The vibration specifications of the system are investigated more detailed via plotting time histories, phase-plane portraits, and fast Fourier transforms (FFTs).

Neurobiologically Realistic Determinants of Self-Organized Criticality in Networks of Spiking Neurons

Self-organized criticality refers to the spontaneous emergence of self-similar dynamics in complex systems poised between order and randomness. The presence of self-organized critical dynamics in the brain is theoretically appealing and is supported by recent neurophysiological studies. Despite this, the neurobiological determinants of these dynamics have not been previously sought. Here, we systematically examined the influence of such determinants in hierarchically modular networks of leaky integrate-and-fire neurons with spike-timing-dependent synaptic plasticity and axonal conduction delays. We characterized emergent dynamics in our networks by distributions of active neuronal ensemble modules (neuronal avalanches) and rigorously assessed these distributions for power-law scaling. We found that spike-timing-dependent synaptic plasticity enabled a rapid phase transition from random subcritical dynamics to ordered supercritical dynamics. Importantly, modular connectivity and low wiring cost broadened this transition, and enabled a regime indicative of self-organized criticality. The regime only occurred when modular connectivity, low wiring cost and synaptic plasticity were simultaneously present, and the regime was most evident when between-module connection density scaled as a power-law. The regime was robust to variations in other neurobiologically relevant parameters and favored systems with low external drive and strong internal interactions. Increases in system size and connectivity facilitated internal interactions, permitting reductions in external drive and facilitating convergence of postsynaptic-response magnitude and synaptic-plasticity learning rate parameter values towards neurobiologically realistic levels. We hence infer a novel association between self-organized critical neuronal dynamics and several neurobiologically realistic features of structural connectivity. The central role of these features in our model may reflect their importance for neuronal information processing.

Non-modal dynamics before flow-induced instability in fluid–structure interactions

A theoretical investigation of the subcritical dynamics of typical fluid–structure interactions is carried out by applying the linear non-modal operators theory combined with direct numerical simulations. The cases of thin panel exposed to subsonic and supersonic fluid flows and pipe conveying a fluid are considered. In order to make the problem tractable for the rather wide range of cases, simple pressure models are employed, where the relevant instability is essentially of modal-coalescence type. Transient energy amplifications, here termed optimal and global, are found, which exhibit low-frequency oscillations corresponding to the continuous extraction/release of energy from/to the fluid. The low frequency is always related to the spacing between the imaginary parts of the coupling eigenvalues, which represent essentially the non-normal part of the spectrum. This suggests that the periodic transfers of energy along the route to the instability can be predicted by a simple model of damped forced vibrations. Although the major findings are obtained with crude pressure models, the authors’ opinion is that most of the effects discussed in the paper should survive to the use of flow dynamics modeling based on full Navier–Stokes equations, at least at high Reynolds number flows.

Probing a subcritical instability with an amplitude expansion: An exploration of how far one can get

We explore methods to locate subcritical branches of spatially periodic solutions in pattern forming systems with a nonlinear finite-wavelength instability. We do so by means of a direct expansion in the amplitude of the linearly least stable mode about the appropriate reference state which one considers. This is motivated by the observation that for some equations fully nonlinear chaotic dynamics has been found to be organized around periodic solutions that do not simply bifurcate from the basic (laminar) state. We apply the method to two model equations, a subcritical generalization of the Swift–Hohenberg equation and a novel extension of the Kuramoto–Sivashinsky equation that we introduce to illustrate the abovementioned scenario in which weakly chaotic subcritical dynamics is organized around periodic states that bifurcate “from infinity” and that can nevertheless be probed perturbatively. We explore the reliability and robustness of such an expansion, with a particular focus on the use of these methods for determining the existence and approximate properties of finite-amplitude stationary solutions. Such methods obviously are to be used with caution: the expansions are often only asymptotic approximations, and if they converge their radius of convergence may be small. Nevertheless, expansions to higher order in the amplitude can be a useful tool to obtain qualitatively reliable results.

Turbulent dynamics of pipe flow captured in a reduced model: puff relaminarization and localized ‘edge’ states

Fully three-dimensional computations of flow through a long pipe demand a huge number of degrees of freedom, making it very expensive to explore parameter space and difficult to isolate the structure of the underlying dynamics. We therefore introduce a ‘2+ε-dimensional’ model of pipe flow, which is a minimal three-dimensionalization of the axisymmetric case: only sinusoidal variation in azimuth plus azimuthal shifts are retained; yet the same dynamics familiar from experiments are found. In particular the model retains the subcritical dynamics of fully resolved pipe flow, capturing realistic localized ‘puff-like’ structures which can decay abruptly after long times, as well as global ‘slug’ turbulence. Relaminarization statistics of puffs reproduce the memoryless feature of pipe flow and indicate the existence of a Reynolds number about which lifetimes diverge rapidly, provided that the pipe is sufficiently long. Exponential divergence of the lifetime is prevalent in shorter periodic domains. In a short pipe, exact travelling-wave solutions are found near flow trajectories on the boundary between laminar and turbulent flow. In a long pipe, the attracting state on the laminar–turbulent boundary is a localized structure which resembles a smoothened puff. This ‘edge’ state remains localized even for Reynolds numbers at which the turbulent state is global.

Subcritical behavior in the alternating supercritical Domany-Kinzel dynamics

Cellular automata are widely used to model real-world dynamics. We show using the Domany-Kinzel probabilistic cellular automata that alternating two supercritical dynamics can result in subcritical dynamics in which the population dies out. The analysis of the original and reduced models reveals generality of this paradoxical behavior, which suggests that autonomous or man-made periodic or random environmental changes can cause extinction in otherwise safe population dynamics. Our model also realizes another scenario for the Parrondo's paradox to occur, namely, spatial extensions.

Application of Burnable Absorbers in an Accelerator-Driven System

The application of burnable absorbers (BAs) to minimize power peaking, reactivity loss, and capture-to-fission probabilities in an accelerator-driven waste transmutation system has been investigated. Boron-10-enriched B4C absorber rods were introduced into a lead-bismuth-cooled core fueled with transuranic (TRU) discharges from light water reactors to achieve the smallest possible power peakings at beginning-of-life (BOL) subcriticality level of 0.97. Detailed Monte Carlo simulations show that a radial power peaking equal to 1.2 at BOL is attainable using a four-zone differentiation in BA content. Using a newly written Monte Carlo burnup code, reactivity losses were calculated to be 640 pcm per percent TRU burnup for unrecycled TRU discharges. Comparing to corresponding values in BA-free cores, BA introduction diminishes reactivity losses in TRU-fueled subcritical cores by ~20%. Radial power peaking after 300 days of operation at 1200-MW thermal power was <1.75 at a subcriticality level of ~0.92, which appears to be acceptable, with respect to limitations in cladding and fuel temperatures. In addition, the use of BAs yields significantly higher fission-to-capture probabilities in even-neutron-number nuclides. Fission-to-absorption probability ratio for 241Am equal to 0.33 was achieved in the configuration studied. Hence, production of the strong alpha-emitter 242Cm is reduced, leading to smaller fuel-swelling rates and pin pressurization. Disadvantages following BA introduction, such as increase of void worth and decrease of Doppler feedback in conjunction with small values of βeff, need to be addressed by detailed studies of subcritical core dynamics.

Landau-Ginzburg theory of self-organized criticality.

Self-organized criticality occurs through a nonlinear feedback mechanism triggering transitions between different metastable states. These transitions take the form of intermittent avalanchelike events distributed according to a power law. We present the first and simplest fully continuous partial differential formalism of this phenomenon, based on the introduction of a subcritical dynamics. SOC is identified as the regime where diffusive relaxation is faster than the instability growth rate. In the other limit of slow diffusion, avalanches comparable to the system size become dominant. This provides a general correspondence between SOC and synchronization of threshold oscillators.

Stability and subcritical dynamics of structures with spatially disordered travelling parametric excitation

Spatially periodic travelling loading may lead sometimes to parametric excitation of structures. However, random spatial nonuniformity of the loading pattern, which leads in time domain to fluctuations of the excitation frequency, makes this phenomenon less severe. This effect is considered for the case of a single-mode resonant response. Using stochastic averaging, stability boundaries are found both with respect to probability and in the mean square; subcritical response to an external broadband random excitation is also obtained.