Theoretical foundations of studying criticality in the brain
arXiv:2306.05635 · doi:10.1162/netn_a_00269
Abstract
Criticality is hypothesized as a physical mechanism underlying efficient transitions between cortical states and remarkable information processing capacities in the brain. While considerable evidence generally supports this hypothesis, non-negligible controversies persist regarding the ubiquity of criticality in neural dynamics and its role in information processing. Validity issues frequently arise during identifying potential brain criticality from empirical data. Moreover, the functional benefits implied by brain criticality are frequently misconceived or unduly generalized. These problems stem from the non-triviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data. To help solve these problems, we present a systematic review and reformulate the foundations of studying brain criticality, i.e., ordinary criticality (OC), quasi-criticality (qC), self-organized criticality (SOC), and self-organized quasi-criticality (SOqC), using the terminology of neuroscience. We offer accessible explanations of the physical theories and statistic techniques of brain criticality, providing step-by-step derivations to characterize neural dynamics as a physical system with avalanches. We summarize error-prone details and existing limitations in brain criticality analysis and suggest possible solutions. Moreover, we present a forward-looking perspective on how optimizing the foundations of studying brain criticality can deepen our understanding of various neuroscience questions.
References in corpus (31)
- Power-law distributions in empirical data
- Synchronization in complex networks
- Emergent complex neural dynamics
- Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems
- Dynamical synapses causing self-organized criticality in neural networks
- Criticality in the brain: A synthesis of neurobiology, models and cognition
- Self-Organized Criticality model for Brain Plasticity
- Stochastic Models of Evolution in Genetics, Ecology and Linguistics
- Spike Avalanches Exhibit Universal Dynamics across the Sleep-Wake Cycle
- Landau-Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization
- Parameter estimation for power-law distributions by maximum likelihood methods
- Thermodynamic costs of information processing in sensory adaption
- Balance of excitation and inhibition determines 1/f power spectrum in neuronal networks
- Optimal hierarchical modular topologies for producing limited sustained activation of neural networks
- Percolation in living neural networks
- Evidence for quasicritical brain dynamics
- Quasi-Critical Brain Dynamics on a Non-Equilibrium Widom Line
- Non-equilibrium brain dynamics as a signature of consciousness
- The effect of thresholding on temporal avalanche statistics
- noise and avalanche scaling in plastic deformation
- A simple unified view of branching process statistics: random walks in balanced logarithmic potentials
- Hybrid-type synchronization transitions: where marginal coherence, scale-free avalanches, and bistability live together
- Large Associative Memory Problem in Neurobiology and Machine Learning
- Subsampled directed-percolation models explain scaling relations experimentally observed in the brain
- Confirming and extending the hypothesis of universality in sandpiles
- Mean Field Residual Networks: On the Edge of Chaos
- Bridging the information and dynamics attributes of neural activities
- Unconsciousness reconfigures modular brain network dynamics
- Self-organized criticality in neural networks
- Stochastic Models of Neural Plasticity: A Scaling Approach
- Thermodynamics of Encoding and Encoders