Coherent resonance in the distributed cortical network during sensory information processing

Pisarchik, Alexander N.; Maksimenko, Vladimir A.; Андреев, А. В.; Frolov, Nikita S.; Макаров, В. В.; Zhuravlev, Maksim O.; Runnova, Anastasiya E.; Hramov, Alexander E.

doi:10.1038/s41598-019-54577-1

Cited by 62 publications

(29 citation statements)

References 40 publications

(35 reference statements)

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“…Interestingly, higher attention performance leads to shorter dominance time. This is in accordance with our hypothesis that higher attention requires a larger neuronal network to process information and make a decision , this in turn increases neural noise since a larger number of synapses and neurons are involved (Pisarchik et al, 2019b ). Finally, stronger brain noise causes more frequent switching between perceptual states or more frequent response selection and hence shorter dominance times.…”

Section: Resultssupporting

confidence: 92%

Voluntary and Involuntary Attention in Bistable Visual Perception: A MEG Study

Chholak

Maksimenko

Hramov

et al. 2020

Front. Hum. Neurosci.

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In this study, voluntary and involuntary visual attention focused on different interpretations of a bistable image, were investigated using magnetoencephalography (MEG). A Necker cube with sinusoidally modulated pixels' intensity in the front and rear faces with frequencies 6.67 Hz (60/9) and 8.57 Hz (60/7), respectively, was presented to 12 healthy volunteers, who interpreted the cube as either left- or right-oriented. The tags of these frequencies and their second harmonics were identified in the average Fourier spectra of the MEG data recorded from the visual cortex. In the first part of the experiment, the subjects were asked to voluntarily control their attention by interpreting the cube orientation as either being on the left or right. Accordingly, we observed the dominance of the corresponding spectral component, and voluntary attention performance was measured. In the second part of the experiment, the subjects were asked to focus their gaze on a red marker at the center of the cube image without putting forth effort in its interpretation. The alternation of the dominant spectral energies at the second harmonics of the stimulation frequencies was treated as changes in the cube orientation. Based on the results of the first experimental stage and using a wavelet analysis, we developed a method which allowed us to identify the currently perceived cube orientation. Finally, we characterized involuntary attention using the distribution of dominance times when focusing attention on one of the cube orientations, which was related to voluntary attention performance and brain noise. In particular, we confirmed our hypothesis that higher attention performance is associated with stronger brain noise.

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Section: Resultssupporting

confidence: 92%

Voluntary and Involuntary Attention in Bistable Visual Perception: A MEG Study

Chholak

Maksimenko

Hramov

et al. 2020

Front. Hum. Neurosci.

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“…It has thus been hypothesized that synchronization in the visual system represents a CR effect triggered by ARASmediated drive. This hypothesis has been supported recently by [22] showing in numerical simulations that an intermediate intensity of noise maximizes the interaction in a neural network of Hodgkin-Huxley neurons. Furthermore, recent theoretical work [21] has provided key insights on how human occipital electrocorticographic γ-activity commonly observed with open eyes [21] is closely linked to CR.…”

Section: Introductionmentioning

confidence: 67%

“…Furthermore, recent theoretical work [21] has provided key insights on how human occipital electrocorticographic γ-activity commonly observed with open eyes [21] is closely linked to CR. Coherence resonance has further been associated with states of elevated information processing and transfer [22], which are difficult to assess in the absence of mean-field descriptions. For illustration, Figure 1 (upper panel) shows average network activity for increasing noise intensities D 1 and one observes a jump from non-oscillatory to oscillatory activity.…”

Section: Introductionmentioning

confidence: 99%

Coherence Resonance in Random Erdös-Rényi Neural Networks: Mean-Field Theory

Hutt

Wahl

Voges

et al. 2021

Front. Appl. Math. Stat.

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Additive noise is known to tune the stability of nonlinear systems. Using a network of two randomly connected interacting excitatory and inhibitory neural populations driven by additive noise, we derive a closed mean-field representation that captures the global network dynamics. Building on the spectral properties of Erdös-Rényi networks, mean-field dynamics are obtained via a projection of the network dynamics onto the random network’s principal eigenmode. We consider Gaussian zero-mean and Poisson-like noise stimuli to excitatory neurons and show that these noise types induce coherence resonance. Specifically, the stochastic stimulation induces coherent stochastic oscillations in the γ-frequency range at intermediate noise intensity. We further show that this is valid for both global stimulation and partial stimulation, i.e. whenever a subset of excitatory neurons is stimulated only. The mean-field dynamics exposes the coherence resonance dynamics in the γ-range by a transition from a stable non-oscillatory equilibrium to an oscillatory equilibrium via a saddle-node bifurcation. We evaluate the transition between non-coherent and coherent state by various power spectra, Spike Field Coherence and information-theoretic measures.

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“…The generic mechanism by which additive noise stabilizes the dynamics of non-linear systems is seen in systems with threshold-like non-linearities such as digital audio, image processing, optics, communications systems and stock market fluctuations (see 112 and related literature on dithering). In neuroscience specifically, this occurs in individual neurons 113 , delayed-feedback systems 56 , 79 and plays a role in stochastic 87 - and coherence-resonance 114 , which are widespread in across physical and neural systems.…”

Section: Discussionmentioning

confidence: 99%

Neurostimulation stabilizes spiking neural networks by disrupting seizure-like oscillatory transitions

Rich

Hutt

Skinner

et al. 2020

Sci Rep

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An improved understanding of the mechanisms underlying neuromodulatory approaches to mitigate seizure onset is needed to identify clinical targets for the treatment of epilepsy. Using a Wilson–Cowan-motivated network of inhibitory and excitatory populations, we examined the role played by intrinsic and extrinsic stimuli on the network’s predisposition to sudden transitions into oscillatory dynamics, similar to the transition to the seizure state. Our joint computational and mathematical analyses revealed that such stimuli, be they noisy or periodic in nature, exert a stabilizing influence on network responses, disrupting the development of such oscillations. Based on a combination of numerical simulations and mean-field analyses, our results suggest that high variance and/or high frequency stimulation waveforms can prevent multi-stability, a mathematical harbinger of sudden changes in network dynamics. By tuning the neurons’ responses to input, stimuli stabilize network dynamics away from these transitions. Furthermore, our research shows that such stabilization of neural activity occurs through a selective recruitment of inhibitory cells, providing a theoretical undergird for the known key role these cells play in both the healthy and diseased brain. Taken together, these findings provide new vistas on neuromodulatory approaches to stabilize neural microcircuit activity.

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Coherent resonance in the distributed cortical network during sensory information processing

Cited by 62 publications

References 40 publications

Voluntary and Involuntary Attention in Bistable Visual Perception: A MEG Study

Voluntary and Involuntary Attention in Bistable Visual Perception: A MEG Study

Coherence Resonance in Random Erdös-Rényi Neural Networks: Mean-Field Theory

Neurostimulation stabilizes spiking neural networks by disrupting seizure-like oscillatory transitions

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