A dynamical pattern recognition model of γ activity in auditory cortex

M Zavaglia, R T Canolty, T M Schofield, A P Leff, M Ursino, R T Knight, W D Penny

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9 Citations (Scopus)


This paper describes a dynamical process which serves both as a model of temporal pattern recognition in the brain and as a forward model of neuroimaging data. This process is considered at two separate levels of analysis: the algorithmic and implementation levels. At an algorithmic level, recognition is based on the use of Occurrence Time features. Using a speech digit database we show that for noisy recognition environments, these features rival standard cepstral coefficient features. At an implementation level, the model is defined using a Weakly Coupled Oscillator (WCO) framework and uses a transient synchronization mechanism to signal a recognition event. In a second set of experiments, we use the strength of the synchronization event to predict the high gamma (75-150 Hz) activity produced by the brain in response to word versus non-word stimuli. Quantitative model fits allow us to make inferences about parameters governing pattern recognition dynamics in the brain.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalNeural Networks
Early online date13 Jan 2012
Publication statusPublished - Apr 2012


  • Acoustic Stimulation
  • Adult
  • Auditory Cortex
  • Brain Mapping
  • Brain Waves
  • Female
  • Humans
  • Neurological Models
  • Nerve Net
  • Neuronal Plasticity
  • Physiological Pattern Recognition
  • Comparative Study

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