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dc.contributor.author
Surace, Simone Carlo
dc.contributor.author
Pfister, Jean-Pascal
dc.date.accessioned
2018-07-19T10:02:25Z
dc.date.available
2017-06-11T22:14:33Z
dc.date.available
2018-07-19T10:02:25Z
dc.date.issued
2015-11-16
dc.identifier.issn
1932-6203
dc.identifier.other
10.1371/journal.pone.0142435
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/108988
dc.identifier.doi
10.3929/ethz-b-000108988
dc.description.abstract
Single neuron models have a long tradition in computational neuroscience. Detailed biophysical models such as the Hodgkin-Huxley model as well as simplified neuron models such as the class of integrate-and-fire models relate the input current to the membrane potential of the neuron. Those types of models have been extensively fitted to in vitro data where the input current is controlled. Those models are however of little use when it comes to characterize intracellular in vivo recordings since the input to the neuron is not known. Here we propose a novel single neuron model that characterizes the statistical properties of in vivo recordings. More specifically, we propose a stochastic process where the subthreshold membrane potential follows a Gaussian process and the spike emission intensity depends nonlinearly on the membrane potential as well as the spiking history. We first show that the model has a rich dynamical repertoire since it can capture arbitrary subthreshold autocovariance functions, firing-rate adaptations as well as arbitrary shapes of the action potential. We then show that this model can be efficiently fitted to data without overfitting. We finally show that this model can be used to characterize and therefore precisely compare various intracellular in vivo recordings from different animals and experimental conditions.
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/4.0/
dc.title
A Statistical Model for In Vivo Neuronal Dynamics
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
PLoS ONE
ethz.journal.volume
10
en_US
ethz.journal.issue
11
en_US
ethz.journal.abbreviated
PLoS ONE
ethz.pages.start
e0142435
en_US
ethz.size
21 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.identifier.nebis
006206116
ethz.publication.place
San Francisco CA, USA
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2017-06-11T22:14:49Z
ethz.source
ECIT
ethz.identifier.importid
imp593653d98a06f83586
ethz.ecitpid
pub:170023
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-12T15:20:31Z
ethz.rosetta.lastUpdated
2021-02-15T00:44:26Z
ethz.rosetta.versionExported
true
ethz.COinS
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