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 theMMNis still lacking, and its computational foundations remaindebated.Wepropose here a detailed neuronalmodelof auditory cortex, basedonpredictive 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 uponNMDAreceptor 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.Wedemonstrate 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 theMMNtoNMDAreceptor antagonists. Novel predictions are presented, and a new magnetoencephalography experiment in healthy human subjects is presented that validates our key hypothesis: theMMNresults from active cortical prediction rather than passive synaptic habituation. ©2012 the authors.