Direct connections assist neurons to detect correlation in small amplitude noises

Bolhasani, Ehsan; Azizi, Yousef; Valizadeh, Alireza

doi:10.3389/fncom.2013.00108

Cited by 10 publications

(7 citation statements)

References 65 publications

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“…In all the systems considered here, the oscillators have similar natural frequency, and the connections are of similar coupling strength and time delay in transmission of pulse to other oscillators. It was previously shown that for any identical pair of symmetrically coupled oscillators with delayed couplings, the stability of inphase or anti-phase state depends on the delay and slope of the coupling function [6,27,65,66]. With the sinusoidal coupling used here, we chose time delay between T /2 and 3T /2, where T is the period of the oscillations.…”

Section: Resultsmentioning

confidence: 99%

Flexible patterns of information transfer in frustrated networks of phase oscillators

et al. 2022

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Brain networks are characterized by flexible patterns of pairwise correlations and information exchange between different brain regions. Such dynamic patterns are crucial for an efficient response of the brain to environmental and cognitive demands. We here propose that the collective oscillations in the brain can provide a mechanism to control dynamical interactions and the exchange of information across brain networks. In particular, we show that the phase difference between oscillatory activities in different brain regions determines the transmission of neural signals. To further corroborate this, we study a network of coupled oscillators with repulsive couplings and show that the amount of information transfer between the nodes is determined

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Section: Resultsmentioning

confidence: 99%

Flexible patterns of information transfer in frustrated networks of phase oscillators

et al. 2022

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“…In the classical models of synchronization, collective state of a system of coupled oscillators is determined by the outcome of rivalry between synchronizing effect of connections and desynchronizing effect of inhomogeneity 2 , but there are examples of the systems in which synchrony is enhanced by inhomogeneity 38 39 40 41 . Recently we have shown that small inhomogeneity can increase correlation between spike trains of two coupled neurons 42 . In this study we develope a framework for the correlation of weakly coupled phase oscillators with a given phase sensitivity when they are driven by small amplitude noises.…”

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confidence: 99%

Stabilizing synchrony by inhomogeneity

Bolhasani

Valizadeh

2015

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We show that for two weakly coupled identical neuronal oscillators with strictly positive phase resetting curve, isochronous synchrony can only be seen in the absence of noise and an arbitrarily weak noise can destroy entrainment and generate intermittent phase slips. Small inhomogeneity–mismatch in the intrinsic firing rate of the neurons–can stabilize the phase locking and lead to more precise relative spike timing of the two neurons. The results can explain how for a class of neuronal models, including leaky integrate-fire model, inhomogeneity can increase correlation of spike trains when the neurons are synaptically connected.

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“…In the classical models of synchronization, collective state of a system of coupled oscillators is determined by outcome of rivalry between synchronizing effect of connections and desynchronizing effect of inhomogeneity [ 2 ]. Yet there are examples of the systems in which inhomogeneity enhances synchrony [ 3 , 4 ]. In this study we have developed a general framework for the correlation of coupled neuronal oscillators with a given phase sensitivity and we have shown that in some cases, for identical neurons, synchronized state is an unstable attractor and arbitrarily weak noise can destroy synchrony.…”

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confidence: 99%