Excitatory-inhibitory branching process: A parsimonious view of cortical asynchronous states, excitability, and criticality

Corral, López, Roberto; Buendía, Victor; Muñoz, Miguel A.

doi:10.1103/physrevresearch.4.l042027

Cited by 6 publications

(1 citation statement)

References 71 publications

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“…Furthermore, external sources can move the system away from criticality [34,35] or tune it to other classes, like the isotropic percolation [16,36] or to tricritical points [37]. More complex models than the two-state branching process, can also exhibit hybrid type of phase transitions, like threshold models [38], models with inhibitory nodes [39] or models with oscillatory units [40]. Subsystems can also show different scaling behavior and may be within different distances from criticality [41].…”

Section: Introductionmentioning

confidence: 99%

Synchronization transitions on connectome graphs with external force

Ódor

Papp²,

Deng³

et al. 2023

Front. Phys.

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We investigate the synchronization transition of the Shinomoto-Kuramoto model on networks of the fruit-fly and two large human connectomes. This model contains a force term, thus is capable of describing critical behavior in the presence of external excitation. By numerical solution we determine the crackling noise durations with and without thermal noise and show extended non-universal scaling tails characterized by the exponent 2 < τt < 2.8, in contrast with the Hopf transition of the Kuramoto model, without the force τt = 3.1(1). Comparing the phase and frequency order parameters we find different synchronization transition points and fluctuation peaks as in case of the Kuramoto model, related to a crossover at Widom lines. Using the local order parameter values we also determine the Hurst (phase) and β (frequency) exponents and compare them with recent experimental results obtained by fMRI. We show that these exponents, characterizing the auto-correlations are smaller in the excited system than in the resting state and exhibit module dependence.

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Section: Introductionmentioning

confidence: 99%

Synchronization transitions on connectome graphs with external force

Ódor

Papp²,

Deng³

et al. 2023

Front. Phys.

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Existence of multiple transitions of the critical state due to anesthetics

Curic,

Ashby,

McGirr

et al. 2024

Nat Commun

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Scale-free statistics of coordinated neuronal activity, suggesting a universal operating mechanism across spatio-temporal scales, have been proposed as a necessary condition of healthy resting-state brain activity. Recent studies have focused on anesthetic agents to induce distinct neural states in which consciousness is altered to understand the importance of critical dynamics. However, variation in experimental techniques, species, and anesthetics, have made comparisons across studies difficult. Here we conduct a survey of several common anesthetics (isoflurane, pentobarbital, ketamine) at multiple dosages, using calcium wide-field optical imaging of the mouse cortex. We show that while low-dose anesthesia largely preserves scale-free statistics, surgical plane anesthesia induces multiple dynamical modes, most of which do not maintain critical avalanche dynamics. Our findings indicate multiple pathways away from default critical dynamics associated with quiet wakefulness, not only reflecting differences between these common anesthetics but also showing significant variations in individual responses. This is suggestive of a non-trivial relationship between criticality and the underlying state of the subject.

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Less is different: Why sparse networks with inhibition differ from complete graphs

Menesse,

Kinouchi

2023

Phys. Rev. E

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Excitatory-inhibitory branching process: A parsimonious view of cortical asynchronous states, excitability, and criticality

Cited by 6 publications

References 71 publications

Synchronization transitions on connectome graphs with external force

Synchronization transitions on connectome graphs with external force

Existence of multiple transitions of the critical state due to anesthetics

Less is different: Why sparse networks with inhibition differ from complete graphs

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