English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

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

MPS-Authors
/persons/resource/persons19554

Bitzer,  Sebastian
Department of Psychology, TU Dresden, Germany;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

/persons/resource/persons19770

Kiebel,  Stefan J.
Department of Psychology, TU Dresden, Germany;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department of Neurology, Biomagnetic Center, Jena University Hospital, Germany;

Locator
There are no locators available
Fulltext (public)

Rivera_2015.PDF
(Publisher version), 4MB

Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0029-7067-E
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.