According to hierarchical predictive coding models, the cortex constantly generates predictions of incoming stimuli at multiple levels of processing. Responses to auditory mismatches and omissions are interpreted as reflecting the prediction error when these predictions are violated. An alternative interpretation, however, is that neurons passively adapt to repeated stimuli. We separated these alternative interpretations by designing a hierarchical auditory novelty paradigm and recording human EEG and magnetoencephalographic (MEG) responses to mismatching or omitted stimuli. In the crucial condition, participants listened to frequent series of four identical tones followed by a fifth different tone, which generates a mismatch response. Because this response itself is frequent and expected, the hierarchical predictive coding hypothesis suggests that it should be cancelled out by a higher-order prediction. Three consequences ensue. First, the mismatch response should be larger when it is unexpected than when it is expected. Second, a perfectly monotonic sequence of five identical tones should now elicit a higher-order novelty response. Third, omitting the fifth tone should reveal the brain's hierarchical predictions. The rationale here is that, when a deviant tone is expected, its omission represents a violation of two expectations: a local prediction of a tone plus a hierarchically higher expectation of its deviancy. Thus, such an omission should induce a greater prediction error than when a standard tone is expected. Simultaneous EEE-magnetoencephalographic recordings verify those predictions and thus strongly support the predictive coding hypothesis. Higher-order predictions appear to be generated in multiple areas of frontal and associative cortices. mismatch negativity | P300 component

The mismatch negativity (MMN) is thought to index the activation of specialized neural networks for active prediction and deviance detection. However, a detailed neuronal model of the neurobiological mechanisms underlying the MMN is still lacking, and its computational foundations remain debated. We propose here a detailed neuronal model of auditory cortex, based on predictive coding, that accounts for the critical features of MMN. The model is entirely composed of spiking excitatory and inhibitory neurons interconnected in a layered cortical architecture with distinct input, predictive, and prediction error units. A spike-timing dependent learning rule, relying upon NMDA receptor synaptic transmission, allows the network to adjust its internal predictions and use a memory of the recent past inputs to anticipate on future stimuli based on transition statistics. We demonstrate that this simple architecture can account for the major empirical properties of the MMN. These include a frequency-dependent response to rare deviants, a response to unexpected repeats in alternating sequences (ABABAA. . . ), a lack of consideration of the global sequence context, a response to sound omission, and a sensitivity of the MMN to NMDA receptor antagonists. Novel predictions are presented, and a new magnetoencephalography experiment in healthy human subjects is presented that validates our key hypothesis: the MMN results from active cortical prediction rather than passive synaptic habituation.

A sequence of images, sounds, or words can be stored at several levels of detail, from specific items and their timing to abstract structure. We propose a taxonomy of five distinct cerebral mechanisms for sequence coding: transitions and timing knowledge, chunking, ordinal knowledge, algebraic patterns, and nested tree structures. In each case, we review the available experimental paradigms and list the behavioral and neural signatures of the systems involved. Tree structures require a specific recursive neural code, as yet unidentified by electrophysiology, possibly unique to humans, and which may explain the singularity of human language and cognition.