Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published May 6, 2011 | Accepted Version
Journal Article Open

Normalization for Sparse Encoding of Odors by a Wide-Field Interneuron

Abstract

Sparse coding presents practical advantages for sensory representations and memory storage. In the insect olfactory system, the representation of general odors is dense in the antennal lobes but sparse in the mushroom bodies, only one synapse downstream. In locusts, this transformation relies on the oscillatory structure of antennal lobe output, feed-forward inhibitory circuits, intrinsic properties of mushroom body neurons, and connectivity between antennal lobe and mushroom bodies. Here we show the existence of a normalizing negative-feedback loop within the mushroom body to maintain sparse output over a wide range of input conditions. This loop consists of an identifiable "giant" nonspiking inhibitory interneuron with ubiquitous connectivity and graded release properties.

Additional Information

© 2011 American Association for the Advancement of Science. Received for publication 17 December 2010. Accepted for publication 28 March 2011. We thank G. Turner for help with early locust experiments; V. Jayaraman, G. Turner, and G. Jefferis for help with the Drosophila recordings (fig. S9); and the Caltech Imaging Center for use of a confocal microscope. This work was funded by the National Institute for Deafness and Communications Disorders (G.L.), the Lawrence Hanson Fund (G.L.), the Max Planck Society (G.L.), the Office of Naval Research (grants N00014-07- 1-0741 and N00014-10-1-0735 to G.L. and S.C.), a grant from Evolved Machines, Inc. (G.L. and M.P.), a Research Council of UK Academic Fellowship, and a grant from the Biotechnology and Biological Sciences Research Council (UK grant number BB/F005113/1) (T.N.).

Attached Files

Accepted Version - ukmss-37970.pdf

Files

ukmss-37970.pdf
Files (1.7 MB)
Name Size Download all
md5:a59f7ae059d894721e8d77639ae86f85
1.7 MB Preview Download

Additional details

Created:
August 19, 2023
Modified:
October 23, 2023