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  Inferring neuronal dynamics from calcium imaging data using biophysical models and bayesian inference

Rahmati, V., Kirmse, K., Markovic, D., Holthoff, K., & Kiebel, S. J. (2016). Inferring neuronal dynamics from calcium imaging data using biophysical models and bayesian inference. PLoS Computational Biology, 12(2): e1004736. doi:10.1371/journal.pcbi.1004736.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-2077-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-1DF1-D
Genre: Journal Article

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 Creators:
Rahmati, Vahid1, 2, Author
Kirmse, Knut2, Author
Markovic, Dimitrije1, 2, 3, Author              
Holthoff, Knut2, Author
Kiebel, Stefan J.1, 2, 3, Author              
Affiliations:
1Department of Psychology, TU Dresden, Germany, ou_persistent22              
2Department of Neurology, Biomagnetic Center, Jena University Hospital, Germany, ou_persistent22              
3Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              

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 Abstract: Calcium imaging has been used as a promising technique to monitor the dynamic activity of neuronal populations. However, the calcium trace is temporally smeared which restricts the extraction of quantities of interest such as spike trains of individual neurons. To address this issue, spike reconstruction algorithms have been introduced. One limitation of such reconstructions is that the underlying models are not informed about the biophysics of spike and burst generations. Such existing prior knowledge might be useful for constraining the possible solutions of spikes. Here we describe, in a novel Bayesian approach, how principled knowledge about neuronal dynamics can be employed to infer biophysical variables and parameters from fluorescence traces. By using both synthetic and in vitro recorded fluorescence traces, we demonstrate that the new approach is able to reconstruct different repetitive spiking and/or bursting patterns with accurate single spike resolution. Furthermore, we show that the high inference precision of the new approach is preserved even if the fluorescence trace is rather noisy or if the fluorescence transients show slow rise kinetics lasting several hundred milliseconds, and inhomogeneous rise and decay times. In addition, we discuss the use of the new approach for inferring parameter changes, e.g. due to a pharmacological intervention, as well as for inferring complex characteristics of immature neuronal circuits.

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Language(s): eng - English
 Dates: 2015-06-182016-01-052016-02-19
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1004736
PMID: 26894748
PMC: PMC4760968
Other: eCollection 2016
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 12 (2) Sequence Number: e1004736 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1