A dynamical pattern recognition model of gamma activity in auditory cortex

Authors:
M. Zavaglia;R. T. Canolty;T. M. Schofield;A. P. Leff;M. Ursino;R. T. Knight;W. D. Penny
Affiliations:
Department of Electronics, Computer Science and Systems (DEIS), Via Venezia 52, 47023 Cesena, Italy;Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA;Wellcome Trust Centre for Neuroimaging, University College, London WC1N 3BG, UK;Institute of Neurology and Institute of Cognitive Neuroscience, UCL, 17 Queen Square, London WC1N 3AR, UK;Department of Electronics, Computer Science and Systems (DEIS), Via Venezia 52, 47023 Cesena, Italy;Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA;Wellcome Trust Centre for Neuroimaging, University College, London WC1N 3BG, UK
Venue:
Neural Networks
Year:
2012

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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.