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dc.contributor.author
Weber, Andrea P.
dc.contributor.author
Hahnloser, Richard H.R.
dc.date.accessioned
2019-01-21T12:26:35Z
dc.date.available
2017-06-08T18:05:26Z
dc.date.available
2019-01-21T12:26:35Z
dc.date.issued
2007-12-21
dc.identifier.issn
1553-734X
dc.identifier.issn
1553-7358
dc.identifier.other
10.1371/journal.pcbi.0030249
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/8265
dc.identifier.doi
10.3929/ethz-b-000008265
dc.description.abstract
The relationships between neural activity at the single-cell and the population levels are of central importance for understanding neural codes. In many sensory systems, collective behaviors in large cell groups can be described by pairwise spike correlations. Here, we test whether in a highly specialized premotor system of songbirds, pairwise spike correlations themselves can be seen as a simple corollary of an underlying random process. We test hypotheses on connectivity and network dynamics in the motor pathway of zebra finches using a high-level population model that is independent of detailed single-neuron properties. We assume that neural population activity evolves along a finite set of states during singing, and that during sleep population activity randomly switches back and forth between song states and a single resting state. Individual spike trains are generated by associating with each of the population states a particular firing mode, such as bursting or tonic firing. With an overall modification of one or two simple control parameters, the Markov model is able to reproduce observed firing statistics and spike correlations in different neuron types and behavioral states. Our results suggest that song- and sleep-related firing patterns are identical on short time scales and result from random sampling of a unique underlying theme. The efficiency of our population model may apply also to other neural systems in which population hypotheses can be tested on recordings from small neuron groups.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Public Library of Science
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/3.0/
dc.title
Spike Correlations in a Songbird Agree with a Simple Markov Population Model
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 3.0 Unported
ethz.journal.title
PLoS Computational Biology
ethz.journal.volume
3
en_US
ethz.journal.issue
12
en_US
ethz.journal.abbreviated
PLOS comput. biol.
ethz.pages.start
e249
en_US
ethz.size
12 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.nebis
005410277
ethz.publication.place
San Francisco, CA, USA
en_US
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.::02533 - Institut für Neuroinformatik / Institute of Neuroinformatics::03774 - Hahnloser, Richard H.R. / Hahnloser, Richard H.R.
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.::02533 - Institut für Neuroinformatik / Institute of Neuroinformatics::03774 - Hahnloser, Richard H.R. / Hahnloser, Richard H.R.
ethz.date.deposited
2017-06-08T18:05:35Z
ethz.source
ECIT
ethz.identifier.importid
imp59364bc3f3dc614462
ethz.ecitpid
pub:18981
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-19T10:26:36Z
ethz.rosetta.lastUpdated
2020-02-15T16:44:40Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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