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  Modelling odor decoding in the antennal lobe by combining sequential firing rate models with bayesian inference

Cuevas Rivera, D., Bitzer, S., & Kiebel, S. J. (2015). Modelling odor decoding in the antennal lobe by combining sequential firing rate models with bayesian inference. PLoS Computational Biology, 11(10): e1004528. doi:10.1371/journal.pcbi.1004528.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-7067-E Version Permalink: http://hdl.handle.net/21.11116/0000-0003-780B-B
Genre: Journal Article

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Cuevas Rivera, Dario1, 2, Author
Bitzer, Sebastian1, 3, 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: The olfactory information that is received by the insect brain is encoded in the form of spatiotemporal patterns in the projection neurons of the antennal lobe. These dense and overlapping patterns are transformed into a sparse code in Kenyon cells in the mushroom body. Although it is clear that this sparse code is the basis for rapid categorization of odors, it is yet unclear how the sparse code in Kenyon cells is computed and what information it represents. Here we show that this computation can be modeled by sequential firing rate patterns using Lotka-Volterra equations and Bayesian online inference. This new model can be understood as an ‘intelligent coincidence detector’, which robustly and dynamically encodes the presence of specific odor features. We found that the model is able to qualitatively reproduce experimentally observed activity in both the projection neurons and the Kenyon cells. In particular, the model explains mechanistically how sparse activity in the Kenyon cells arises from the dense code in the projection neurons. The odor classification performance of the model proved to be robust against noise and time jitter in the observed input sequences. As in recent experimental results, we found that recognition of an odor happened very early during stimulus presentation in the model. Critically, by using the model, we found surprising but simple computational explanations for several experimental phenomena.

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Language(s): eng - English
 Dates: 2015-04-012015-08-282015-10-09
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1004528
PMID: 26451888
PMC: PMC4599861
Other: eCollection 2015
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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: 11 (10) Sequence Number: e1004528 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1