Modulation of Neuronal Interactions Through Neuronal Synchronization
Open accessAbstract
Brain processing depends on the interactions between neuronal groups. Those interactions are governed by the pattern of anatomical connections and by yet unknown mechanisms that modulate the effective strength of a given connection. We found that the mutual influence among neuronal groups depends on the phase relation between rhythmic activities within the groups. Phase relations supporting interactions between the groups preceded those interactions by a few milliseconds, consistent with a mechanistic role. These effects were specific in time, frequency, and space, and we therefore propose that the pattern of synchronization flexibly determines the pattern of neuronal interactions.
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The results suggest that modulation of interareal synchronization at distinct frequencies is a central mechanism in communicating specific predicted versus unpredicted information.
“They suggest that modulation of interareal synchronization at distinct frequencies is a central mechanism in communicating specific (predicted vs. unpredicted) information (65–68).”
From Layer and rhythm specificity for predictive routing (2020) · cites this paper as [66]
Controlling the degree of synchrony at presynaptic neurons can serve as a gain control mechanism.
“Thus, controlling the degree of synchrony at presynaptic neurons can serve as a gain control mechanism [59,60].”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [60]
Synchronized presynaptic spikes generally make the postsynaptic impact stronger.
“synchronized presynaptic spikes generally make the postsynaptic impact stronger.”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [60]
By adjusting oscillation frequency and phase of anatomically connected cell clusters, effective coupling between these clusters can be enhanced by assuring that the respective excitatory inputs are synchronized and arrive at the peak of susceptibility.
“Thus, by adjusting oscillation frequency and phase of anatomically connected cell clusters, effective coupling between these clusters can be enhanced by assuring that the respective excitatory inputs are synchronized and arrive at the peak of susceptibility while coupling can be virtually abolished if phase relations among the oscillating clusters are such that excitatory volleys arrive during the”
From Neuronal Dynamics and Neuropsychiatric Disorders: Toward a Translational Paradigm for Dysfunctional Large-Scale Networks (2012) · cites this paper as Womelsdorf et al., 2007
Coupling can be virtually abolished if phase relations among the oscillating clusters are such that excitatory volleys arrive during the phase of low susceptibility.
“Thus, by adjusting oscillation frequency and phase of anatomically connected cell clusters, effective coupling between these clusters can be enhanced by assuring that the respective excitatory inputs are synchronized and arrive at the peak of susceptibility while coupling can be virtually abolished if phase relations among the oscillating clusters are such that excitatory volleys arrive during the”
From Neuronal Dynamics and Neuropsychiatric Disorders: Toward a Translational Paradigm for Dysfunctional Large-Scale Networks (2012) · cites this paper as Womelsdorf et al., 2007
Phase preferential firing may aid information processing tasks by providing order and allowing multiple channels of information to be processed in parallel.
“In turn, phase preferential firing may aid information processing tasks by providing order and allowing multiple channels of information to be processed in parallel (Jensen and Lisman, 2000; Hasselmo et al., 2002; Womelsdorf et al., 2007; Schomburg et al., 2014; Jeewajee et al., 2014; Maris et al., 2016).”
From Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit (2016) · cites this paper as Womelsdorf et al., 2007
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“They can facilitate cortical communication by coordinating brief time windows when sending and receiving populations are alternately more or less receptive to inputs (Womelsdorf et al., 2007; Fries, 2015).”
- Authors
- Womelsdorf, T., Schoffelen, J.-M., Oostenveld, R., Singer, W., Desimone, R., Engel, A. K., & Fries, P.
- Affiliations
- Goethe University Frankfurt (DE)
- Corresponding author
- Womelsdorf, Thilo
- Schoffelen, Jan-Mathijs
- ORCID
- Venue
- Science
- Published
- 2007-06-15
- Volume
- 316
- Issue
- 5831
- Pages
- 1609-1612
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2157787057
- Cited by
- 1,367
- References
- 28
- Citations by year
- 2026: 40 · 2025: 38 · 2024: 36 · 2023: 49 · 2022: 60 · 2021: 74 · 2020: 68 · 2019: 110 · 2018: 87 · 2017: 87 · 2016: 69 · 2015: 90 · 2014: 84 · 2013: 83 · 2012: 83
- FWCI
- 36.51
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- National Eye Institute (R01EY017292)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Computer Science › Computer Networks and Communications › Nonlinear Dynamics and Pattern Formation
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- neuronal synchronization, neuronal interactions, phase equilibria, rhythmic activity, effective connectivity, brain processing, neural oscillations, intergroup communication
- MeSH terms
- Action Potentials, Animals, Cats, Electrodes, Implanted, Electrophysiology, Macaca nemestrina, Male, Nerve Net, Neurons, Parietal Lobe, Temporal Lobe, Visual Pathways
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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