A dynamical pattern recognition model of γ activity in auditory cortex.
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.
Department of Electronics, Computer Science and Systems (DEIS), Via Venezia 52, 47023 Cesena, Italy.
M
M
Zavaglia
R T
RT
Canolty
T M
TM
Schofield
A P
AP
Leff
M
M
Ursino
R T
RT
Knight
W D
WD
Penny
D000161:Acoustic Stimulation / Q000379:methods*; D000328:Adult; D001303:Auditory Cortex / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D058256:Brain Waves / Q000502:physiology*; D005260:Female; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology; D046709:Pattern Recognition, Physiological / Q000502:physiology*