Cortical oscillations and speech processing: emerging computational principles and operations

By Anne-Lise Giraud, David Poeppel. Nature Neuroscience, 2012

1,717 citations recorded by the source corpus.

Neuronal oscillations are ubiquitous in the brain and may contribute to cognition in several ways: for example, by segregating information and organizing spike timing. Recent data show that delta, theta and gamma oscillations are specifically engaged by the multi-timescale, quasi-rhythmic properties of speech and can track its dynamics. We argue that they are foundational in speech and language processing, 'packaging' incoming information into units of the appropriate temporal granularity. Such stimulus-brain alignment arguably results from auditory and motor tuning throughout the evolution of speech and language and constitutes a natural model system allowing auditory research to make a unique contribution to the issue of how neural oscillatory activity affects human cognition.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/88613469cb9faf3cc5f5676169b9e558957f24ab

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Citations in the corpus

Acoustic landmarks drive delta-theta oscillations to enable speech comprehension by facilitating perceptual parsing (2013, methodology): “One plausible hypothesis generated from the Giraud and Poeppel (2012) model suggests that it is the onsets of syllables that produce temporal fluctuations, which entrain slow neural oscillations at the syllabic rate.”
Embodied Cognition is Not What you Think it is (2013, background): “This work focuses on the production of syllables and models that in terms of oscillator dynamics which can then be coupled to the oscillator dynamics of the cortex (Luo and Poeppel, 2007; Giraud and Poeppel, 2012; Peelle and Davis, 2012).”
Hierarchical representation and estimation of prosody using continuous wavelet transform (2017, background): “In speech and auditory research there is also a long history going back to the 1970’s [27, 28, 29, 30, 31, 32, 33, 34, 35].”
Conditional-Computation-Based Recurrent Neural Networks for Computationally Efficient Acoustic Modelling (2018, background): “Additionally, the multi-timescale structure of speech [12, 13] along with the multiple levels of abstraction learned by deep neural networks [14] suggests that the deeper layers may encode features with a characteristic timescale longer than that of the lower layers.”
Consequences of the Now-or-Never bottleneck for signed versus spoken languages (2015, background): “The temporal cortex processes speech at different scales: at shorter windows (25–50 ms) in the left STC, and at longer windows (150–250 ms) in the right STC (Boemio et al. 2005; Giraud et al. 2007; Giraud & Poeppel 2012).”
The Theta-Gamma Neural Code (2013, neutral): “In the auditory system, a model has been proposed in which ongoing syllables and words are segmented by an oscillatory cortical decoder based on nested theta and gamma oscillations (Ghitza, 2011; Giraud and Poeppel, 2012; Peelle and Davis, 2012).”
A free energy principle for a particular physics (2019, background): “…(and beyond) is occupied with oscillations and their ontology (Breakspear et al., 2010; Burgess et al., 2007; Buzsaki, 1998; Buzsaki and Moser, 2013; Giraud and Poeppel, 2012; Jensen et al., 2014; Lisman, 2012; Lopes da Silva, 1991; Sejnowski and Paulsen, 2006; Uhlhaas and Singer, 2010).”
Multisensory integration: flexible use of general operations (2014, neutral): “Complementary cues from different sensory modalities, or motor cues, may be particularly important as biological rhythmicity is typically not entirely regular, such as in speech (Giraud and Poeppel, 2012).”
Mechanisms Underlying Selective Neuronal Tracking of Attended Speech at a ‘Cocktail Party’ (2013, background): “…Health System, New Hyde Park, NY 11040, USA 8Feinstein Institute for Medical Research, Hofstra University School of Medicine, Manhasset, NY 11549, USA 9Department of Psychology, New York University, NY 10003, USA *Correspondence: schrod@nki.rfmh.org http://dx.doi.org/10.1016/j.neuron.2012.12.037”