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dc.contributor.author
Lieder, Falk
dc.contributor.author
Stephan, Klaas
dc.contributor.author
Daunizeau, Jean
dc.contributor.author
Garrido, Marta I.
dc.contributor.author
Friston, Karl J.
dc.date.accessioned
2019-01-17T12:36:51Z
dc.date.available
2017-06-11T02:52:24Z
dc.date.available
2019-01-17T12:36:51Z
dc.date.issued
2013-11-07
dc.identifier.issn
1553-734X
dc.identifier.issn
1553-7358
dc.identifier.other
10.1371/journal.pcbi.1003288
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/77888
dc.identifier.doi
10.3929/ethz-b-000077888
dc.description.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.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
PLOS
dc.rights.uri
http://creativecommons.org/licenses/by/3.0/
dc.title
A Neurocomputational Model of the Mismatch Negativity
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 3.0 Unported
ethz.journal.title
PLoS Computational Biology
ethz.journal.volume
9
en_US
ethz.journal.issue
11
en_US
ethz.journal.abbreviated
PLOS comput. biol.
ethz.pages.start
e1003288
en_US
ethz.size
14 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.publication.place
San Francisco, CA
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02631 - Institut für Biomedizinische Technik / Institute for Biomedical Engineering::03955 - Stephan, Klaas E. / Stephan, Klaas E.
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02631 - Institut für Biomedizinische Technik / Institute for Biomedical Engineering::03955 - Stephan, Klaas E. / Stephan, Klaas E.
ethz.relation.isReferencedBy
10.1371/annotation/ca4c3cdf-9573-4a93-9542-3a62cdbb8396
ethz.date.deposited
2017-06-11T02:54:58Z
ethz.source
ECIT
ethz.identifier.importid
imp59365171efc5187914
ethz.ecitpid
pub:122652
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-18T07:56:05Z
ethz.rosetta.lastUpdated
2024-02-02T06:58:32Z
ethz.rosetta.versionExported
true
ethz.COinS
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