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Dynamic Control of Synchronous Activity in Networks of Spiking Neurons

View Article: PubMed Central - PubMed

ABSTRACT

Oscillatory brain activity is believed to play a central role in neural coding. Accumulating evidence shows that features of these oscillations are highly dynamic: power, frequency and phase fluctuate alongside changes in behavior and task demands. The role and mechanism supporting this variability is however poorly understood. We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. We show that afferent noise, mimicking inputs to the neurons, causes smoothing of the system’s response function, displacing equilibria and altering the stability of oscillatory states. Our analysis further shows that these noise-induced changes cause a shift of the peak frequency of synchronous oscillations that scales with input intensity, leading the network towards critical states. We lastly discuss the extension of these principles to periodic stimulation, in which externally applied driving signals can trigger analogous phenomena. Our results reveal one possible mechanism involved in shaping oscillatory activity in the brain and associated control principles.

No MeSH data available.


As noise increases, global oscillations accelerate and become gradually more linear.Pre-synaptic noise generically results in a linearization of the neurons response function, altering the network stability and further shaping the frequency of ongoing oscillations. The network mean activity  is shown (top panel) with a close up view of a few cycles (middle panel) with the associated response function (bottom panel), for various levels of noise. A. D = 0.001. B. D = 0.01. C. D = 0.1. Other parameters are identical to parameters used in Fig 1.
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pone.0161488.g002: As noise increases, global oscillations accelerate and become gradually more linear.Pre-synaptic noise generically results in a linearization of the neurons response function, altering the network stability and further shaping the frequency of ongoing oscillations. The network mean activity is shown (top panel) with a close up view of a few cycles (middle panel) with the associated response function (bottom panel), for various levels of noise. A. D = 0.001. B. D = 0.01. C. D = 0.1. Other parameters are identical to parameters used in Fig 1.

Mentions: Eq (10) shows that pre-synaptic noise generically results in a linearization of the neurons response function [28,29] cf. Fig 2, bottom panels. This linearization shapes the input-output relationship of driven neurons, but also alters the features displayed by emergent activity patterns such as synchronous oscillations [30]. Fig 2 illustrates that increasing the external noise tunes the frequency of the network mean activity.


Dynamic Control of Synchronous Activity in Networks of Spiking Neurons
As noise increases, global oscillations accelerate and become gradually more linear.Pre-synaptic noise generically results in a linearization of the neurons response function, altering the network stability and further shaping the frequency of ongoing oscillations. The network mean activity  is shown (top panel) with a close up view of a few cycles (middle panel) with the associated response function (bottom panel), for various levels of noise. A. D = 0.001. B. D = 0.01. C. D = 0.1. Other parameters are identical to parameters used in Fig 1.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5036852&req=5

pone.0161488.g002: As noise increases, global oscillations accelerate and become gradually more linear.Pre-synaptic noise generically results in a linearization of the neurons response function, altering the network stability and further shaping the frequency of ongoing oscillations. The network mean activity is shown (top panel) with a close up view of a few cycles (middle panel) with the associated response function (bottom panel), for various levels of noise. A. D = 0.001. B. D = 0.01. C. D = 0.1. Other parameters are identical to parameters used in Fig 1.
Mentions: Eq (10) shows that pre-synaptic noise generically results in a linearization of the neurons response function [28,29] cf. Fig 2, bottom panels. This linearization shapes the input-output relationship of driven neurons, but also alters the features displayed by emergent activity patterns such as synchronous oscillations [30]. Fig 2 illustrates that increasing the external noise tunes the frequency of the network mean activity.

View Article: PubMed Central - PubMed

ABSTRACT

Oscillatory brain activity is believed to play a central role in neural coding. Accumulating evidence shows that features of these oscillations are highly dynamic: power, frequency and phase fluctuate alongside changes in behavior and task demands. The role and mechanism supporting this variability is however poorly understood. We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. We show that afferent noise, mimicking inputs to the neurons, causes smoothing of the system’s response function, displacing equilibria and altering the stability of oscillatory states. Our analysis further shows that these noise-induced changes cause a shift of the peak frequency of synchronous oscillations that scales with input intensity, leading the network towards critical states. We lastly discuss the extension of these principles to periodic stimulation, in which externally applied driving signals can trigger analogous phenomena. Our results reveal one possible mechanism involved in shaping oscillatory activity in the brain and associated control principles.

No MeSH data available.