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A Neurocomputational Model of the Mismatch Negativity

Author

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  • Falk Lieder
  • Klaas E Stephan
  • Jean Daunizeau
  • Marta I Garrido
  • Karl J Friston

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.Author Summary: Computational neuroimaging enables quantitative inferences from non-invasive measures of brain activity on the underlying mechanisms. Ultimately, we would like to understand these mechanisms not only in terms of physiology but also in terms of computation. So far, this has not been addressed by mathematical models of neuroimaging data (e.g., dynamic causal models), which have rather focused on ever more detailed inferences about physiology. Here we present the first instance of a dynamic causal model that explains electrophysiological data in terms of computation rather than physiology. Concretely, we predict the mismatch negativity – an event-related potential elicited by regularity violation – from the dynamics of perceptual inference as prescribed by the free energy principle. The resulting model explains the waveform of the mismatch negativity and some of its phenomenological properties at a level of precision that has not been attempted before. This highlights the potential of neurocomputational dynamic causal models to enable inferences from neuroimaging data on neurocomputational mechanisms.

Suggested Citation

  • Falk Lieder & Klaas E Stephan & Jean Daunizeau & Marta I Garrido & Karl J Friston, 2013. "A Neurocomputational Model of the Mismatch Negativity," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-14, November.
  • Handle: RePEc:plo:pcbi00:1003288
    DOI: 10.1371/journal.pcbi.1003288
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    References listed on IDEAS

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    1. Emili Balaguer-Ballester & Nicholas R Clark & Martin Coath & Katrin Krumbholz & Susan L Denham, 2009. "Understanding Pitch Perception as a Hierarchical Process with Top-Down Modulation," PLOS Computational Biology, Public Library of Science, vol. 5(3), pages 1-15, March.
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    3. Daniel Bendor & Xiaoqin Wang, 2005. "The neuronal representation of pitch in primate auditory cortex," Nature, Nature, vol. 436(7054), pages 1161-1165, August.
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    6. Jean Daunizeau & Hanneke E M den Ouden & Matthias Pessiglione & Stefan J Kiebel & Klaas E Stephan & Karl J Friston, 2010. "Observing the Observer (I): Meta-Bayesian Models of Learning and Decision-Making," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-10, December.
    7. Zsolt Ugray & Leon Lasdon & John Plummer & Fred Glover & James Kelly & Rafael Martí, 2007. "Scatter Search and Local NLP Solvers: A Multistart Framework for Global Optimization," INFORMS Journal on Computing, INFORMS, vol. 19(3), pages 328-340, August.
    8. Will D Penny & Klaas E Stephan & Jean Daunizeau & Maria J Rosa & Karl J Friston & Thomas M Schofield & Alex P Leff, 2010. "Comparing Families of Dynamic Causal Models," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-14, March.
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    Cited by:

    1. Boris Vladimirskiy & Robert Urbanczik & Walter Senn, 2015. "Hierarchical Novelty-Familiarity Representation in the Visual System by Modular Predictive Coding," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-19, December.
    2. Cooper A Smout & Matthew F Tang & Marta I Garrido & Jason B Mattingley, 2019. "Attention promotes the neural encoding of prediction errors," PLOS Biology, Public Library of Science, vol. 17(2), pages 1-22, February.

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