Potential Mechanisms and Functions of Intermittent Neural Synchronization

Ahn, Sungwoo; Rubchinsky, Leonid L.

doi:10.3389/fncom.2017.00044

Cited by 16 publications

(44 citation statements)

References 26 publications

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“…Earlier studies showed that one can change parameters of oscillators in such a way that the distribution of desynchronization duration is changed independently of the average synchrony strength (e.g., Ahn et al, 2011;Rubchinsky et al, 2014;Ahn and Rubchinsky, 2017). In the coupled ecological oscillators considered here, changes in the temporal patterning of synchronization can be independent of the synchronization strength too, as evidenced by the results presented above (although they may co-vary together as well).…”

Section: Changing Desynchronization Durations Independently Of Frequesupporting

confidence: 55%

Temporal patterns of dispersal-induced synchronization in population dynamics

Ahn

Rubchinsky

2020

Journal of Theoretical Biology

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Highlights• Predator-prey oscillating populations with weak dispersal exhibit intermittent synchrony • Temporal patterns of this synchrony depend on the properties of predator-prey interactions in individual patches and may be independent of synchrony strength • The study identifies the properties of the predator-prey oscillators responsible for dynamics with numerous short desynchronizations vs. few long desynchronizations AbstractThe mechanisms and properties of synchronization of oscillating ecological populations attract attention because it is a fairly common phenomenon and because spatial synchrony may elevate a risk of extinction and may lead to other environmental impacts. Conditions for stable synchronization in a system of linearly coupled predator-prey oscillators have been considered in the past. However, the spatial dispersion coupling may be relatively weak and may not necessarily lead to a stable, complete synchrony. If the coupling between oscillators is too weak to induce a stable synchrony, oscillators may be engaged into intermittent synchrony, when episodes of synchronized dynamics are interspersed with the episodes of desynchronized dynamics. In the present study we consider the temporal patterning of this kind of intermittent synchronized dynamics in a system of two dispersal-coupled Rosenzweig-MacArthur predator-prey oscillators. We consider the properties of the distributions of durations of desynchronized intervals and their dependence on the model parameters. We show that the temporal patterning of synchronous dynamics (an ecological network phenomenon) may depend on the properties of individual predator-prey patch (individual oscillator) and may vary independently of the strength of dispersal. We also show that if the dynamics of predator is slow relative to the dynamics of the prey (a situation that may promote brief but large outbreaks), dispersal-coupled predator-prey oscillating populations exhibit numerous short desynchronizations (as opposed to few long desynchronizations) and may require weaker dispersal in order to reach strong synchrony.

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Section: Changing Desynchronization Durations Independently Of Frequesupporting

confidence: 55%

Temporal patterns of dispersal-induced synchronization in population dynamics

Ahn

Rubchinsky

2020

Journal of Theoretical Biology

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“…Synchronous states are more prominent during slow-wave sleep and anesthesia whereas asynchronous firing activity is prevalent in the states of wakefulness and REM sleep (Steriade et al 2001 ; El Boustani et al 2007 ; Greenberg et al 2008 ; Sanchez-Vives et al 2017 ). Notably, the degree of synchrony in cortical and subcortical regions varies with time, often with intermittent switches between synchronous and asynchronous states (Ahn and Rubchinsky 2013 , 2017 ; Hahn et al 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise

2018

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Spontaneous cortical population activity exhibits a multitude of oscillatory patterns, which often display synchrony during slow-wave sleep or under certain anesthetics and stay asynchronous during quiet wakefulness. The mechanisms behind these cortical states and transitions among them are not completely understood. Here we study spontaneous population activity patterns in random networks of spiking neurons of mixed types modeled by Izhikevich equations. Neurons are coupled by conductance-based synapses subject to synaptic noise. We localize the population activity patterns on the parameter diagram spanned by the relative inhibitory synaptic strength and the magnitude of synaptic noise. In absence of noise, networks display transient activity patterns, either oscillatory or at constant level. The effect of noise is to turn transient patterns into persistent ones: for weak noise, all activity patterns are asynchronous non-oscillatory independently of synaptic strengths; for stronger noise, patterns have oscillatory and synchrony characteristics that depend on the relative inhibitory synaptic strength. In the region of parameter space where inhibitory synaptic strength exceeds the excitatory synaptic strength and for moderate noise magnitudes networks feature intermittent switches between oscillatory and quiescent states with characteristics similar to those of synchronous and asynchronous cortical states, respectively. We explain these oscillatory and quiescent patterns by combining a phenomenological global description of the network state with local descriptions of individual neurons in their partial phase spaces. Our results point to a bridge from events at the molecular scale of synapses to the cellular scale of individual neurons to the collective scale of neuronal populations.Electronic supplementary materialThe online version of this article (10.1007/s10827-018-0688-6) contains supplementary material, which is available to authorized users.

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“…The dynamics of the non-plastic variant of this system was studied in Ahn and Rubchinsky (2017). Based on that study, we vary values of three parameters of the potassium current in such a way as to change the dynamics of the non-plastic network from exhibiting predominantly short desynchronizations (i.e., those observed in experiments) to one with a large mode of desynchronization durations.…”

Section: Resultsmentioning

confidence: 99%

Spike-Timing Dependent Plasticity Effect on the Temporal Patterning of Neural Synchronization

Zirkle

Rubchinsky

2020

Front. Comput. Neurosci.

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Neural synchrony in the brain at rest is usually variable and intermittent, thus intervals of predominantly synchronized activity are interrupted by intervals of desynchronized activity. Prior studies suggested that this temporal structure of the weakly synchronous activity might be functionally significant: many short desynchronizations may be functionally different from few long desynchronizations even if the average synchrony level is the same. In this study, we used computational neuroscience methods to investigate the effects of spike-timing dependent plasticity (STDP) on the temporal patterns of synchronization in a simple model. We employed a small network of conductance-based model neurons that were connected via excitatory plastic synapses. The dynamics of this network was subjected to the time-series analysis methods used in prior experimental studies. We found that STDP could alter the synchronized dynamics in the network in several ways, depending on the time scale that plasticity acts on. However, in general, the action of STDP in the simple network considered here is to promote dynamics with short desynchronizations (i.e., dynamics reminiscent of that observed in experimental studies). Complex interplay of the cellular and synaptic dynamics may lead to the activity-dependent adjustment of synaptic strength in such a way as to facilitate experimentally observed short desynchronizations in the intermittently synchronized neural activity.

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Potential Mechanisms and Functions of Intermittent Neural Synchronization

Cited by 16 publications

References 26 publications

Temporal patterns of dispersal-induced synchronization in population dynamics

Temporal patterns of dispersal-induced synchronization in population dynamics

Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise

Spike-Timing Dependent Plasticity Effect on the Temporal Patterning of Neural Synchronization

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