COMPUTATIONAL MODEL

It is well known that cognitive functions exert task-specific modulation of the response properties of human auditory cortex. However, the underlying neuronal mechanisms are not well understood yet. In this dissertation I present a novel approach for integrating ‘bottom-up ’ (neural network modeling) and ‘top-down’ (experiment) methods to study the dynamics of cortical circuits correlated to short-term memory (STM) processing that underlie the task-specific modulation of human auditory perception during performance of the delayed-match-to-sample (DMS) task. The experimental approach measures high-density magnetoencephalography (MEG) signals from human participants to investigate the modulation of human auditory evoked responses (AER) induced by the overt processing of auditory STM during task performance. To accomplish this goal, a new signal processing method based on independent component analysis (ICA) was developed for removing artifact contamination in the MEG recordings and investigating the functional neural circuits underlying the task-specific modulation of human AER. The computational approach uses a large-scale neural network model based on the electrophysiological knowledg