This thesis aims at testing the predictive coding account of auditory perception. This framework rests on precision-weighted prediction errors elicited by unexpected sounds that propagate along a hierarchical organization in order to maintain the brain adapted to a varying acoustic environment. Using the mismatch negativity (MMN), a brain response to unexpected stimuli (deviants) that could reflect such errors, we could address the computational and neurophysiological underpinnings of predictive coding. Precisely, we manipulated the predictability of deviants and applied computational learning models and dynamic causal models (DCM) to electrophysiological responses (EEG, MEG) measured simultaneously. Deviant predictability was found to modulate deviance responses, a result supporting their interpretation as prediction errors. Such effect might involve the (high-level) implicit learning of sound sequence regularities that would in turn influence auditory processing in lower hierarchical levels. Computational modeling revealed the perceptual learning of sounds, resting on temporal integration exhibiting differences induced by our predictability manipulation. In addition, DCM analysis indicated predictability changes in the synaptic connectivity established by deviance processing. These results conform predictive coding predictions regarding both deviance processing and its modulation by deviant predictability and strongly support perceptual learning of auditory regularities achieved within an auditory hierarchy. Our findings also highlight the power of this mechanistic framework to elaborate and test new hypothesis enabling to improve our understanding of auditory processing