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Design principles of the sparse coding network and the role of “sister cells” in the olfactory system of Drosophila

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2013
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Title
Design principles of the sparse coding network and the role of “sister cells” in the olfactory system of Drosophila
Published in
Frontiers in Computational Neuroscience, January 2013
DOI 10.3389/fncom.2013.00141
Pubmed ID
Authors

Danke Zhang, Yuanqing Li, Si Wu, Malte J. Rasch

Abstract

Sensory systems face the challenge to represent sensory inputs in a way to allow easy readout of sensory information by higher brain areas. In the olfactory system of the fly drosopohila melanogaster, projection neurons (PNs) of the antennal lobe (AL) convert a dense activation of glomeruli into a sparse, high-dimensional firing pattern of Kenyon cells (KCs) in the mushroom body (MB). Here we investigate the design principles of the olfactory system of drosophila in regard to the capabilities to discriminate odor quality from the MB representation and its robustness to different types of noise. We focus on understanding the role of highly correlated homotypic projection neurons ("sister cells") found in the glomeruli of flies. These cells are coupled by gap-junctions and receive almost identical sensory inputs, but target randomly different KCs in MB. We show that sister cells might play a crucial role in increasing the robustness of the MB odor representation to noise. Computationally, sister cells thus might help the system to improve the generalization capabilities in face of noise without impairing the discriminability of odor quality at the same time.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Unknown 46 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 30%
Researcher 11 23%
Student > Master 8 17%
Student > Postgraduate 4 9%
Student > Bachelor 1 2%
Other 4 9%
Unknown 5 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 45%
Neuroscience 11 23%
Physics and Astronomy 3 6%
Computer Science 2 4%
Mathematics 1 2%
Other 4 9%
Unknown 5 11%