Apidologie

, Volume 43, Issue 3, pp 269–291 | Cite as

Dynamics of sensory processing in the dual olfactory pathway of the honeybee

Review article

Abstract

Insects identify and evaluate behaviorally relevant odorants in complex natural scenes where odor concentrations and mixture composition can change rapidly. This requires fast and reliable information processing in the olfactory system. Here, we review recent experimental findings and theoretical hypotheses on olfactory processing in the honeybee with a focus on its temporal dynamics. Specifically we address odor response characteristics of antennal lobe interneurons and projection neurons, local processing of elemental odors and odor blends, the functional role of the dual olfactory pathway in the honeybee, population coding in uniglomerular projection neurons, and a novel model for sparse and reliable coding in projection neurons and mushroom body Kenyon cells. It is concluded that the olfactory system of the honeybee implements a fast and reliable coding scheme optimized for processing dynamic input within the behaviorally relevant temporal range.

Keywords

antennal lobe sparse code latency code odor trace olfaction 

Notes

Acknowledgments

I thank Sabine Krofczik and Randolf Menzel for providing me with the electrophysiological data that was reproduced in Figures 2, 3, 4, and 5 and Farzad Farkhooi for carrying out the simulations shown in Figure 7. I am grateful to Jürgen Rybak for the 3D visualization of neurons in the Honeybee Standard Brain atlas (Figure 1) and for his helpful comments on an earlier version of this manuscript. I thank Randolf Menzel, Anneke Meyer, Paul Szyszka and Michael Schmuker for valuable discussions and Chris Häusler for the language check. We acknowledge generous funding from the German Federal Ministry of Education and Research to the Bernstein Focus Learning and Memory—Insect inspired robots (01GQ0941) and to the Bernstein Center for Computational Neuroscience Berlin (01GQ1001D).

Supplementary material

13592_2012_131_MOESM1_ESM.pdf (1.4 mb)
ESM 1(PDF 1383 kb)

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Copyright information

© INRA, DIB and Springer-Verlag, France 2012

Authors and Affiliations

  1. 1.Neuroinformatik/Theoretische Neurobiologie, Institut für BiologieFreie Universität BerlinBerlinGermany
  2. 2.Bernstein Center for Computational NeuroscienceBerlinGermany

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