Dynamics of sensory processing in the dual olfactory pathway of the honeybee
- Martin Paul Nawrot
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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.
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- Introduction
- The olfactory pathway of the honeybee
- Local processing in the honeybee antennal lobe network
- Population coding in uniglomerular projection neurons
- Recent model approaches to odor coding in the honeybee olfactory pathway
- Conclusions and outlook
- References
- References
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References (127)
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About this Article
- Title
- Dynamics of sensory processing in the dual olfactory pathway of the honeybee
- Journal
-
Apidologie
Volume 43, Issue 3 , pp 269-291 - Cover Date
- 2012-05-01
- DOI
- 10.1007/s13592-012-0131-3
- Print ISSN
- 0044-8435
- Online ISSN
- 1297-9678
- Publisher
- Springer-Verlag
- Additional Links
- Topics
- Keywords
-
- antennal lobe
- sparse code
- latency code
- odor trace
- olfaction
- Authors
-
-
Martin Paul Nawrot
(1)
(2)
-
Martin Paul Nawrot
- Author Affiliations
-
- 1. Neuroinformatik/Theoretische Neurobiologie, Institut für Biologie, Freie Universität Berlin, Königin-Luise-Straße 1-3, 14195, Berlin, Germany
- 2. Bernstein Center for Computational Neuroscience, Berlin, Germany