A dynamical pattern recognition model of γ activity in auditory cortex.
Neural Netw
; 28: 1-14, 2012 Apr.
Article
em En
| MEDLINE
| ID: mdl-22327049
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Córtex Auditivo
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Estimulação Acústica
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Reconhecimento Fisiológico de Modelo
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Ondas Encefálicas
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Modelos Neurológicos
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Rede Nervosa
Tipo de estudo:
Prognostic_studies
Limite:
Adult
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Female
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Humans
Idioma:
En
Ano de publicação:
2012
Tipo de documento:
Article