Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6863512 | Neural Networks | 2012 | 14 Pages |
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 (75-150Â 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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
M. Zavaglia, R.T. Canolty, T.M. Schofield, A.P. Leff, M. Ursino, R.T. Knight, W.D. Penny,