Modulation of Neuronal Interactions Through Neuronal Synchronization

Womelsdorf, Thilo; Schoffelen, Jan‐Mathijs; Oostenveld, Robert; Singer, Wolf; Desimone, Robert; Engel, Andreas K.; Fries, Pascal

doi:10.1126/science.1139597

Cited by 1,213 publications

(999 citation statements)

References 28 publications

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“…The increase in beta phase-locking in the STS was seen conjointly with an increase in gamma activity locally and in lower-tier sensory areas. This supports the proposal that the beta range is involved in inter-areal coupling 17,[21][22][23] , and more specifically used in feedback projections 24,25 , and further suggests that the gamma range serves to propagate prediction error forward. …”

supporting

confidence: 81%

Transitions in neural oscillations reflect prediction errors generated in audiovisual speech

2011

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According to the predictive coding theory, top-down predictions are conveyed by backward connections and prediction errors are propagated forward across the cortical hierarchy. Using MEG in humans, we show that violating multisensory predictions causes a fundamental and qualitative change in both the frequency and spatial distribution of cortical activity. When visual speech input correctly predicted auditory speech signals, a slow delta regime (3-4 Hz) developed in higher-order speech areas. In contrast, when auditory signals invalidated predictions inferred from vision, a low-beta (14-15 Hz) / high-gamma (60-80 Hz) coupling regime appeared locally in a multisensory area (area STS). This frequency shift in oscillatory responses scaled with the degree of audio-visual congruence and was accompanied by increased gamma activity in lower sensory regions. These findings are consistent with the notion that bottom-up prediction errors are communicated in predominantly high (gamma) frequency ranges, whereas top-down predictions are mediated by slower (beta) frequencies.© 2011 Nature America, Inc. All rights reserved.7 9 8 VOLUME 14 | NUMBER 6 | JUNE 2011 nature neurOSCIenCe a r t I C l e S converge-that is, where multisensory predictions are generatedwhereas gamma activity was seen in lower sensory cortices where prediction errors emerge and are propagated forward. RESULTSWe presented 15 subjects with stimuli in one of three conditions: videos (audio-visual: AV condition) of a speaker pronouncing the syllables /pa/, / a/, /la/, /ta/, /ga/ and /fa/ (International Phonetic Alphabet notation); an auditory track of these videos combined with a still face (auditory: A condition); or a mute visual track (visual: V condition). The videos could be either natural or a random combination of auditory and visual tracks, creating conditions in which auditory and visual tracks were congruent (AVc condition) and ones in which they were incongruent (AVi condition; see Online Methods and Supplementary Fig. 1). Incongruent combinations yielding fusion illusory percepts-that is, McGurk stimuli-were excluded 8,11 . Subjects performed an unrelated target detection task on the syllable /fa/ that was presented in A, V or AVc form in 13% of the trials (97% correct detection). These trials were subsequently excluded from the analyses. The five other syllables were chosen because they yielded graded recognition accuracy when presented visually (Fig. 1a), resulting from an increasing predictiveness 10 . The phonological prediction conveyed by mouth movements (visemes) varies in specificity depending on the pronounced syllable. Typically, syllables beginning with a consonant that is formed at the front of the mouth (/p/, /m/) convey a more specific prediction than those formed at the back (/g/, /k/, /r/, /l/) 8 . Our second experimental factor pertained to the validity of the visual prediction with respect to the auditory input. Physically, the audio-visual stimuli could be either congruent (valid prediction) or incongruent (invalid prediction), w...

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confidence: 81%

Transitions in neural oscillations reflect prediction errors generated in audiovisual speech

2011

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“…Synchronizations of neural populations in the gamma range are largely recognized as essential features of brain function both locally, for instance as a mechanism of feature binding in perceptual processing (Singer 1998), and globally as a large-scale support for cognitive operations (Engel et al 2001). Synchronization of neural populations entails higher order features such as phase between synchronizing neural populations ( Womelsdorf et al 2007), including those neural populations that synchronize in different frequency regimes such as gamma and theta (Freeman 2000;Canolty et al 2006). With respect to time perception, the underlying assumption in positing a 30 ms perceptual unit is that the temporal content of a brain event (one gamma cycle) equates to the brain event itself.…”

Section: Shuffling Time In the Brainmentioning

confidence: 99%

Minding time in an amodal representational space

Wassenhove

2009

Phil. Trans. R. Soc. B

150

105

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How long did it take you to read this sentence? Chances are your response is a ball park estimate and its value depends on how fast you have scanned the text, how prepared you have been for this question, perhaps your mood or how much attention you have paid to these words. Time perception is here addressed in three sections. The first section summarizes theoretical difficulties in time perception research, specifically those pertaining to the representation of time and temporal processing. The second section reviews non-exhaustively temporal effects in multisensory perception. Sensory modalities interact in temporal judgement tasks, suggesting that (i) at some level of sensory analysis, the temporal properties across senses can be integrated in building a time percept and (ii) the representational format across senses is compatible for establishing such a percept. In the last section, a two-step analysis of temporal properties is sketched out. In the first step, it is proposed that temporal properties are automatically encoded at early stages of sensory analysis, thus providing the raw material for the building of a time percept; in the second step, time representations become available to perception through attentional gating of the raw temporal representations and via re-encoding into abstract representations.

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“…Mechanistically, frontal modulatory control of the posterior lobes could be implemented through imposed patterns of synchronization mediated by cortico-cortical connections (Womelsdorf et al, 2007). Visually evoked activity of single neurons is surprisingly quite deterministic (Arieli et al, 1996).…”

Section: Physiological Mechanism Of the Prediction Mapmentioning

confidence: 99%

The frontal eye field as a prediction map

Crapse

Sommer

2008

Progress in Brain Research

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Predictive processes are widespread in the motor and sensory areas of the primate brain. They enable rapid computations despite processing delays and assist in resolving noisy, ambiguous input. Here we propose that the frontal eye field, a cortical area devoted to sensorimotor aspects of eye movement control, implements a prediction map of the postsaccadic visual scene for the purpose of constructing a stable percept despite saccadic eye movements.

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Modulation of Neuronal Interactions Through Neuronal Synchronization

Cited by 1,213 publications

References 28 publications

Transitions in neural oscillations reflect prediction errors generated in audiovisual speech

Transitions in neural oscillations reflect prediction errors generated in audiovisual speech

Minding time in an amodal representational space

The frontal eye field as a prediction map

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