A dynamical pattern recognition model of γ activity in auditory cortex.
other · Level V
Where this comes from
- Record sourced from PubMed, PMID 22327049.
- Also identified by DOI 10.1016/j.neunet.2011.12.007 and PMC identifier 3314972.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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.
Medical subject headings
- Acoustic Stimulation
- Auditory Cortex
- Brain Waves
- Models, Neurological
- Nerve Net
- Pattern Recognition, Physiological