A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity

By Catherine Wacongne, Jean-Pierre Changeux, Stanislas Dehaene. 2012

542 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Signal Processing

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Citations in the corpus

Theory of Mind: A Neural Prediction Problem (2013, positive): “…coding models need make no assumption about the distribution of these two kinds of neurons within a population; in practice, aggregate population activity is often dominated by error neurons (Friston, 2009; Wacongne et al., 2012; Egner et al., 2010; Keller et al., 2012; Meyer and Sauerland, 2009).”
A Hierarchy of Responses to Auditory Regularities in the Macaque Brain (2014, unknown): “, 2001), generated by an automatic change-detection mechanism (Winkler et al., 2009; Bendixen et al., 2012; Wacongne et al., 2012).”
Theory of cortical function (2017, background): “The predictive coding models that do so (19, 20, 22), as discussed above, are inconsistent with empirical phenomena.”
Repetition Suppression and Expectation Suppression Are Dissociable in Time in Early Auditory Evoked Fields (2012, background): “…ES a viable alternative explanation for the observed RS. Recent theoretical models of sensory processing inspire the hypothesis that RS and ES may be manifestations of prediction errors on different time scales (Friston, 2005; Kiebel et al., 2008; Garrido et al., 2009; Wacongne et al., 2012).”
How Prediction Errors Shape Perception, Attention, and Motivation (2012, background): “However, subsequent evidence has shown that the MMN is different from simple repetition suppression, and is the result of a violated prediction, rather than a physical stimulus change (Summerfield and Koechlin, 2008; Todorovic et al., 2011; Bendixen et al., 2012; Wacongne et al., 2012; Figure 1A).”