ArticleNature communications2020
Combining predictive coding and neural oscillations enables online syllable recognition in natural speech.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Pharmacological modulation of directed network communication and neural hubs in action-effect integration.The international journal of neuropsychopharmacology · 2025Trial
- Evaluating the compatibility of predictive coding as a model of linguistic predictability effects in reading.Frontiers in cognition · 2026Review
- Maturational Changes in Action-Effect Integration Processes Are Reflected by Changes in the Directed Cortical Network Communication.Human brain mapping · 2025Article
- Discrimination and Integration of Phonological Features in Children with Autism Spectrum Disorder: An Exploratory Multi-Feature Oddball Protocol.Brain sciences · 2025Article
- "What" and "When" Predictions Jointly Modulate Speech Processing.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Can a single brain cell be surprised?Nature communications · 2025Article
- A functional systems view on neural tracking of natural speech.Frontiers in systems neuroscience · 2025Article
- Convergent neural signatures of speech prediction error are a biological marker for spoken word recognition.Nature communications · 2024Article
- Dog-human vocal interactions match dogs' sensory-motor tuning.PLoS biology · 2024Article
- Cortical-subcortical interactions underlie processing of auditory predictions measured with 7T fMRI.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Effects of Stimulus Rate and Periodicity on Auditory Cortical Entrainment to Continuous Sounds.eNeuro · 2024Article
- Fast frequency modulation is encoded according to the listener expectations in the human subcortical auditory pathway.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks.Frontiers in neuroscience · 2024Article
- Distinct roles of delta- and theta-band neural tracking for sharpening and predictive coding of multi-level speech features during spoken language processing.Human brain mapping · 2023Article
- Can language models be used for real-world urban-delivery route optimization?Innovation (Cambridge (Mass.)) · 2023Article
- Rhythmic modulation of prediction errors: A top-down gating role for the beta-range in speech processing.PLoS computational biology · 2023Article
- Speech Reception in Young Children with Autism Is Selectively Indexed by a Neural Oscillation Coupling Anomaly.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Latent neural dynamics encode temporal context in speech.Hearing research · 2023Review
- Intraoperative cortical localization of music and language reveals signatures of structural complexity in posterior temporal cortex.iScience · 2023Article
- Phase Alignment of Low-Frequency Neural Activity to the Amplitude Envelope of Speech Reflects Evoked Responses to Acoustic Edges, Not Oscillatory Entrainment.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
Corrections and comments
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
On-line comprehension of natural speech requires segmenting the acoustic stream into discrete linguistic elements. This process is argued to rely on theta-gamma oscillation coupling, which can parse syllables and encode them in decipherable neural activity. Speech comprehension also strongly depends on contextual cues that help predicting speech structure and content. To explore the effects of theta-gamma coupling on bottom-up/top-down dynamics during on-line syllable identification, we designed a computational model (Precoss-predictive coding and oscillations for speech) that can recognise syllable sequences in continuous speech. The model uses predictions from internal spectro-temporal representations of syllables and theta oscillations to signal syllable onsets and duration. Syllable recognition is best when theta-gamma coupling is used to temporally align spectro-temporal predictions with the acoustic input. This neurocomputational modelling work demonstrates that the notions of predictive coding and neural oscillations can be brought together to account for on-line dynamic sensory processing.
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