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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.

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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%