Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,917

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Cortical oscillations and speech processing: emerging computational principles and operations

Open access
Giraud, Anne-Lise, Poeppel, David·2012·Nature Neuroscience

Abstract

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/nn.3063

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Research works printed but not linked: 46

  1. Heimbauer, L.A., Beran, M.J. & Owren, M.J. A chimpanzee recognizes synthetic speech with significantly reduced acoustic cues to phonetic content. Curr. Biol. 21, 1210–1214 (2011). · DOI
  2. Liberman, A.M. & Mattingly, I.G. The motor theory of speech perception revised. Cognition 21, 1–36 (1985). · DOI · Paper page
  3. Poeppel, D. The analysis of speech in different temporal integration windows: cerebral lateralization as 'asymmetric sampling in time'. Speech Commun. 41, 245–255 (2003). · DOI · Paper page
  4. Shannon, R.V. et al. Speech recognition with primarily temporal cues. Science 270, 303–304 (1995). · DOI · Paper page
  5. Lorenzi, C. et al. Speech perception problems of the hearing impaired reflect inability to use temporal fine structure. Proc. Natl. Acad. Sci. USA 103, 18866–18869 (2006). · doi:10.1073/pnas.0607364103
  6. Adank, P. & Janse, E. Perceptual learning of time-compressed and natural fast speech. J. Acoust. Soc. Am. 126, 2649–2659 (2009). · DOI
  7. Giraud, A.L. et al. Endogenous cortical rhythms determine cerebral specialization for speech perception and production. Neuron 56, 1127–1134 (2007). · DOI · Paper page
  8. Ghitza, O. Linking speech perception and neurophysiology: speech decoding guided by cascaded oscillators locked to the input rhythm. Front. Psychol. 2, 130 (2011). · DOI · Paper page
  9. Ghitza, O. & Greenberg, S. On the possible role of brain rhythms in speech perception: intelligibility of time-compressed speech with periodic and aperiodic insertions of silence. Phonetica 66, 113–126 (2009). · DOI · Paper page
  10. Liégeois-Chauvel, C. et al. Temporal envelope processing in the human left and right auditory cortices. Cereb. Cortex 14, 731–740 (2004). · DOI
  11. Ding, N. & Simon, J.Z. Neural representations of complex temporal modulations in the human auditory cortex. J. Neurophysiol. 102, 2731–2743 (2009). · DOI
  12. Luo, H. & Poeppel, D. Phase patterns of neuronal responses reliably discriminate speech in human auditory cortex. Neuron 54, 1001–1010 (2007). · DOI · Paper page
  13. Ahissar, E. et al. Speech comprehension is correlated with temporal response patterns recorded from auditory cortex. Proc. Natl. Acad. Sci. USA 98, 13367–13372 (2001). · DOI · Paper page
  14. Abrams, D.A. et al. Right-hemisphere auditory cortex is dominant for coding syllable patterns in speech. J. Neurosci. 28, 3958–3965 (2008). · DOI · Paper page
  15. Nourski, K.V. et al. Temporal envelope of time-compressed speech represented in the human auditory cortex. J. Neurosci. 29, 15564–15574 (2009). · DOI · Paper page
  16. Schroeder, C.E. & Lakatos, P. Low-frequency neuronal oscillations as instruments of sensory selection. Trends Neurosci. 32, 9–18 (2009). · DOI · Paper page
  17. Atencio, C.A., Sharpee, T.O. & Schreiner, C.E. Cooperative nonlinearities in auditory cortical neurons. Neuron 58, 956–966 (2008). · DOI
  18. Sakata, S. & Harris, K.D. Laminar structure of spontaneous and sensory-evoked population activity in auditory cortex. Neuron 64, 404–418 (2009). · DOI · Paper page
  19. Börgers, C., Epstein, S. & Kopell, N.J. Background gamma rhythmicity and attention in cortical local circuits: a computational study. Proc. Natl. Acad. Sci. USA 102, 7002–7007 (2005). · DOI · Paper page
  20. Fries, P., Nikolic, D. & Singer, W. The gamma cycle. Trends Neurosci. 30, 309–316 (2007). · DOI · Paper page
  21. Kayser, C., Logothetis, N.K. & Panzeri, S. Millisecond encoding precision of auditory cortex neurons. Proc. Natl. Acad. Sci. USA 107, 16976–16981 (2010). · DOI
  22. Chang, E.F. et al. Categorical speech representation in human superior temporal gyrus. Nat. Neurosci. 13, 1428–1432 (2010). · DOI · Paper page
  23. Rauschecker, J.P. & Scott, S.K. Maps and streams in the auditory cortex: nonhuman primates illuminate human speech processing. Nat. Neurosci. 12, 718–724 (2009). · DOI · Paper page
  24. Kopell, N. et al. Gamma and theta rhythms in biophysical models of hippocampal circuits. in Hippocampal Microcircuits: A Computational Modeller's Resource Book (eds. Cutsuridis, V., Graham, B.P., Cobb, S. & Vida, I.) Ch 15 (Springer, 2011). · DOI
  25. Shamir, M. et al. Representation of time-varying stimuli by a network exhibiting oscillations on a faster time scale. PLoS Comput. Biol. 5, e1000370 (2009). · DOI
  26. Atencio, C.A. & Schreiner, C.E. Columnar connectivity and laminar processing in cat primary auditory cortex. PLoS ONE 5, e9521 (2010). · DOI
  27. Zatorre, R.J., Belin, P. & Penhune, V.B. Structure and function of auditory cortex: music and speech. Trends Cogn. Sci. 6, 37–46 (2002). · DOI · Paper page
  28. Boemio, A. et al. Hierarchical and asymmetric temporal sensitivity in human auditory cortices. Nat. Neurosci. 8, 389–395 (2005). · DOI · Paper page
  29. Jamison, H.L. et al. Hemispheric specialization for processing auditory nonspeech stimuli. Cereb. Cortex 16, 1266–1275 (2006). · DOI · Paper page
  30. Obleser, J., Eisner, F. & Kotz, S.A. Bilateral speech comprehension reflects differential sensitivity to spectral and temporal features. J. Neurosci. 28, 8116–8123 (2008). · DOI · Paper page
  31. Morillon, B. et al. Neurophysiological origin of human brain asymmetry for speech and language. Proc. Natl. Acad. Sci. USA 107, 18688–18693 (2010). · DOI
  32. Telkemeyer, S. et al. Sensitivity of newborn auditory cortex to the temporal structure of sounds. J. Neurosci. 29, 14726–14733 (2009). · DOI · Paper page
  33. Hutsler, J. & Galuske, R.A. Hemispheric asymmetries in cerebral cortical networks. Trends Neurosci. 26, 429–435 (2003). · DOI
  34. Gireesh, E.D. & Plenz, D. Neuronal avalanches organize as nested theta- and beta/gamma-oscillations during development of cortical layer 2/3. Proc. Natl. Acad. Sci. USA 105, 7576–7581 (2008). · DOI · Paper page
  35. Pagnamenta, A.T. et al. Characterization of a family with rare deletions in CNTNAP5 and DOCK4 suggests novel risk loci for autism and dyslexia. Biol. Psychiatry 68, 320–328 (2010). · DOI · Paper page
  36. Peschansky, V.J. et al. The effect of variation in expression of the candidate dyslexia susceptibility gene homolog Kiaa0319 on neuronal migration and dendritic morphology in the rat. Cereb. Cortex 20, 884–897 (2010). · DOI
  37. Wang, Y. et al. Dcdc2 knockout mice display exacerbated developmental disruptions following knockdown of doublecortin. Neuroscience 190, 398–408 (2011). · DOI
  38. Goswami, U. A temporal sampling framework for developmental dyslexia. Trends Cogn. Sci. 15, 3–10 (2011). · DOI · Paper page
  39. Ramus, F. & Szenkovits, G. What phonological deficit? Q. J. Exp. Psychol. (Hove) 61, 129–141 (2008). · DOI · Paper page
  40. Ziegler, J.C. et al. Speech-perception-in-noise deficits in dyslexia. Dev. Sci. 12, 732–745 (2009). · DOI · Paper page
  41. Lehongre, K. et al. Altered low-gamma sampling in auditory cortex accounts for the three main facets of dyslexia. Neuron 72, 1080–1090 (2011). · DOI · Paper page
  42. Holcombe, A.O. Seeing slow and seeing fast: two limits on perception. Trends Cogn. Sci. 13, 216–221 (2009). · DOI
  43. Chandrasekaran, C. et al. Monkeys and humans share a common computation for face/voice integration. PLoS Comput. Biol. 7, e1002165 (2011). · DOI
  44. Schroeder, C.E. et al. Dynamics of active sensing and perceptual selection. Curr. Opin. Neurobiol. 20, 172–176 (2010). · DOI · Paper page
  45. Oberlaender, M. et al. Cell type-specific three-dimensional structure of thalamocortical circuits in a column of rat vibrissal cortex. Cereb. Cortex doi:10.1093/cercor/bhr317 (16 November 2011). · DOI · Paper page
  46. Chi, T., Ru, P. & Shamma, S.A. Multiresolution spectrotemporal analysis of complex sounds. J. Acoust. Soc. Am. 118, 887–906 (2005). · DOI · Paper page

Cited by

See all 1,917

Metadata

Authors
Giraud, A.-L., & Poeppel, D.
Venue
Nature Neuroscience
Published
2012-03-18
Volume
15
Issue
4
Pages
511-517
ISSN
  • 1097-6256 (ISSN-L)
  • 1546-1726
OpenAlex
W2056486423
Cited by
1,917
References
4
FWCI
50.05
Type
journal article
Language
en
Open access
bronze
Licence
www.springer.com/tdm (tdm)

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Cortical oscillations and speech processing: emerging computational principles and operations. https://app.cochat.ai/papers/10.1038/nn.3063
The paper
Giraud, A.-L., & Poeppel, D. (2012). Cortical oscillations and speech processing: emerging computational principles and operations. Nature Neuroscience. https://doi.org/10.1038/nn.3063

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.