A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity

Abstract

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.

Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns

“…This finding is a first demonstration of the involvement of these networks in implicit learning of rapidly evolving statistical structure but is generally consistent with previous reports implicating these structures in auditory sequence learning. Activations in IFG and AC are commonly reported in the context of the MMN oddball paradigm (27,28) and interpreted (29) as suggesting that the MMN arises from an interaction between bottom-up and reentrant effects (9,11,30). Similarly, violations of artificial grammars consisting of complex, nonadjacent, or hierarchical relationships between elements in sound sequences have been shown to activate IFG during both explicit decision making and implicit tasks (31)(32)(33).…”

“…In the predictive coding framework, evoked responses have usually been interpreted as reflecting prediction error (7,11,50,51). In this context, increasing stimulus predictability and the concomitant suppression of prediction error are associated with a decrease in the sensory evoked response: an effect which is indeed commonly observed for the relatively simple stimulus sequences previously used to investigate predictive coding (2,3,7,9,51,52).…”

“…Most previous work on sensitivity to acoustic patterning has been conducted in the context of the MMN paradigm where the occurrence of a mismatch response to a signal that violates a previously established regularity is taken as a (indirect) measure of the extent to which this regularity has been acquired (10,11,16,30). …”

“…A ccumulating work suggests that the brain is sensitive to statistical regularities in sensory input, at multiple time scales (1)(2)(3)(4)(5)(6)(7)(8)(9). The auditory system has been a useful testbed to investigate these processes (2,3,(9)(10)(11)(12)(13), largely due to the vantage point provided by the mismatch negativity (MMN) paradigm (12,13). The MMN is an auditory-evoked response generated by sounds violating some regular aspect of the prior sequence and is hypothesized to reflect a discrepancy between the memory trace, or expectations, generated by the standard stimulus, and the deviant information (12,13).…”

Visual mismatch negativity (vMMN): A review and meta-analysis of studies in psychiatric and neurological disorders

“…However, other recent studies using neurobiologically informed computational models (Garagnani & Pulvermü ller, 2011;Wacongne, Changeux, & Dehaene, 2012) found that the MMN is likely to be generated by active cortical predictive mechanisms rather than passive adaptation. The dynamics of the network that is thought to generate the aMMN has been extensively investigated with large-scale models which incorporate hypotheses of both adaptation and change detection (Garrido et al, 2008;Garrido, Kilner, Kiebel, Stephan, & Friston, 2007;Garrido, Kilner, Stephan, & Friston, 2009;.…”