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Sparse Distributed Representation of Odors in a Large-scale Olfactory Bulb Circuit

Overview of attention for article published in PLoS Computational Biology, March 2013
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Title
Sparse Distributed Representation of Odors in a Large-scale Olfactory Bulb Circuit
Published in
PLoS Computational Biology, March 2013
DOI 10.1371/journal.pcbi.1003014
Pubmed ID
Authors

Yuguo Yu, Thomas S. McTavish, Michael L. Hines, Gordon M. Shepherd, Cesare Valenti, Michele Migliore

Abstract

In the olfactory bulb, lateral inhibition mediated by granule cells has been suggested to modulate the timing of mitral cell firing, thereby shaping the representation of input odorants. Current experimental techniques, however, do not enable a clear study of how the mitral-granule cell network sculpts odor inputs to represent odor information spatially and temporally. To address this critical step in the neural basis of odor recognition, we built a biophysical network model of mitral and granule cells, corresponding to 1/100th of the real system in the rat, and used direct experimental imaging data of glomeruli activated by various odors. The model allows the systematic investigation and generation of testable hypotheses of the functional mechanisms underlying odor representation in the olfactory bulb circuit. Specifically, we demonstrate that lateral inhibition emerges within the olfactory bulb network through recurrent dendrodendritic synapses when constrained by a range of balanced excitatory and inhibitory conductances. We find that the spatio-temporal dynamics of lateral inhibition plays a critical role in building the glomerular-related cell clusters observed in experiments, through the modulation of synaptic weights during odor training. Lateral inhibition also mediates the development of sparse and synchronized spiking patterns of mitral cells related to odor inputs within the network, with the frequency of these synchronized spiking patterns also modulated by the sniff cycle.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 4 5%
Germany 1 1%
India 1 1%
France 1 1%
Greece 1 1%
United States 1 1%
Unknown 79 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 24%
Researcher 21 24%
Student > Master 10 11%
Student > Postgraduate 8 9%
Student > Bachelor 6 7%
Other 15 17%
Unknown 7 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 27%
Neuroscience 22 25%
Engineering 11 13%
Computer Science 6 7%
Physics and Astronomy 6 7%
Other 8 9%
Unknown 11 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 May 2013.
All research outputs
#16,737,737
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#7,220
of 8,964 outputs
Outputs of similar age
#130,319
of 210,318 outputs
Outputs of similar age from PLoS Computational Biology
#105
of 152 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 210,318 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 152 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.