A Neurocomputational Model of the Mismatch Negativity

Lieder, Falk, Stephan, Klaas E., Daunizeau, Jean, Garrido, Marta I. and Friston, Karl J. (2013) A Neurocomputational Model of the Mismatch Negativity. PLoS Computational Biology, 9 11: e1003288.1-e1003288.14. doi:10.1371/journal.pcbi.1003288


Author Lieder, Falk
Stephan, Klaas E.
Daunizeau, Jean
Garrido, Marta I.
Friston, Karl J.
Title A Neurocomputational Model of the Mismatch Negativity
Journal name PLoS Computational Biology   Check publisher's open access policy
ISSN 1553-734X
Publication date 2013-01-01
Year available 2013
Sub-type Article (original research)
DOI 10.1371/journal.pcbi.1003288
Open Access Status DOI
Volume 9
Issue 11
Start page e1003288.1
End page e1003288.14
Total pages 14
Place of publication San Francisco, CA United States
Publisher Public Library of Science
Language eng
Subject 2804 Cellular and Molecular Neuroscience
2303 Ecology
1312 Molecular Biology
1311 Genetics
1105 Dentistry
2611 Modelling and Simulation
1703 Computational Theory and Mathematics
Abstract The mismatch negativity (MMN) is an event related potential evoked by violations of regularity. Here, we present a model of the underlying neuronal dynamics based upon the idea that auditory cortex continuously updates a generative model to predict its sensory inputs. The MMN is then modelled as the superposition of the electric fields evoked by neuronal activity reporting prediction errors. The process by which auditory cortex generates predictions and resolves prediction errors was simulated using generalised (Bayesian) filtering - a biologically plausible scheme for probabilistic inference on the hidden states of hierarchical dynamical models. The resulting scheme generates realistic MMN waveforms, explains the qualitative effects of deviant probability and magnitude on the MMN - in terms of latency and amplitude - and makes quantitative predictions about the interactions between deviant probability and magnitude. This work advances a formal understanding of the MMN and - more generally - illustrates the potential for developing computationally informed dynamic causal models of empirical electromagnetic responses.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2014 Collection
 
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Citation counts: TR Web of Science Citation Count  Cited 23 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 31 times in Scopus Article | Citations
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