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.
Original language | English |
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Pages (from-to) | 1-14 |
Number of pages | 14 |
Journal | Neural Networks |
Volume | 28 |
Early online date | 13 Jan 2012 |
DOIs | |
Publication status | Published - Apr 2012 |
Keywords
- Acoustic Stimulation
- Adult
- Auditory Cortex
- Brain Mapping
- Brain Waves
- Female
- Humans
- Neurological Models
- Nerve Net
- Neuronal Plasticity
- Physiological Pattern Recognition
- Comparative Study