Between perfectly critical and fully irregular: a reverberating model captures and predicts cortical spike propagation

Abstract

Knowledge about the collective dynamics of cortical spiking is very informative about the underlying coding principles. However, even most basic properties are not known with certainty, because their assessment is hampered by spatial subsampling, i.e. the limitation that only a tiny fraction of all neurons can be recorded simultaneously with millisecond precision. Building on a novel, subsampling-invariant estimator, we fit and carefully validate a minimal model for cortical spike propagation. The model interpolates between two prominent states: asynchronous and critical. We find neither of them in cortical spike recordings across various species, but instead identify a narrow reverberating regime. This approach enables us to predict yet unknown properties from very short recordings and for every circuit individually, including responses to minimal perturbations, intrinsic network timescales, and the strength of external input compared to recurrent activation - thereby informing about the underlying coding principles for each circuit, area, state and task.

27 pages + supplementary information and supplementary figures

References in corpus (9)

• Power-law distributions in empirical data
• Emergent complex neural dynamics
• Dynamical synapses causing self-organized criticality in neural networks
• Colloquium: Criticality and dynamical scaling in living systems
• Does the 1/f frequency-scaling of brain signals reflect self-organized critical states?
• Spike Avalanches Exhibit Universal Dynamics across the Sleep-Wake Cycle
• Quasi-Critical Brain Dynamics on a Non-Equilibrium Widom Line
• Growing Critical: Self-Organized Criticality in a Developing Neural System
• Coexistence of critical sensitivity and subcritical specificity can yield optimal population coding

Cited by in corpus (13)

• Self-organization toward criticality by synaptic plasticity
• Subsampled directed-percolation models explain scaling relations experimentally observed in the brain
• Theoretical foundations of studying criticality in the brain
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• Assessing criticality in pre-seizure single-neuron activity of human epileptic cortex
• Tackling the subsampling problem to infer collective properties from limited data
• Description of spreading dynamics by microscopic network models and macroscopic branching processes can differ due to coalescence
• MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity
• A Microscopic Theory of Intrinsic Timescales in Spiking Neural Networks
• Sampling effects and measurement overlap can bias the inference of neuronal avalanches
• Critical drift in a neuro-inspired adaptive network
• When to be critical? Performance and evolvability in different regimes of neural Ising agents
• 25 years of criticality in neuroscience -- established results, open controversies, novel concepts