2. Behavioral Biology TALKS - Deutsche Zoologische Gesellschaft

845 µm and 545 µm (50 Hz) and 255 µm (100 Hz). Stimulus duration was 1 s with on-
and off-ramps of 100 ms.
In 45 out of 51 units, discharge rates increased during stimulus presentation
(maximum evoked rates to 50 Hz 31.1 ± 17.1 spikes/s, average ongoing rates 8.5 ± 9.3
spikes/s). About half of the units (n=27, 53%) exhibited adapting responses to at least
one of the two stimulus frequencies, i.e. discharge rate increased during the first 150-
200 ms of the stimulus to a maximum (onset response) and thereafter decreased by
at least 30% to a steady state response (range of response decrement 32-90%). The
strength of adaptation and the number of adapting units increased with increasing
stimulus frequency. Across 18 units stimulated with 50 Hz and 100 Hz sphere
vibrations of comparable velocities (0.17 and 0.16 m/s, respectively), average
response decrement was 29% to the 50 Hz and 47% to the 100 Hz stimulus. Seven
units (37%) exhibited adaptation to the 50 Hz and twelve (67%) to the 100 Hz
stimulus.
Our data are in fair agreement with those reported for primary afferent nerve fibers,
suggesting that adaptation at the level of the MON may reflect mechanisms already
implemented at the level of the lateral line periphery.
����161 Daniel Münch
Mixture interactions in the periphery of the olfactory system - What does banana
smell like?
Authors: Daniel Münch 1 , Ana F. Silbering 1 , Benjamin Schmeichel 1 , C. Giovanni Galizia 1
Affiliation: 1 Neurobiologie, Universität Konstanz
The olfactory world consists of thousands of odors, most of which convey important
information for a given organism. Almost all odors appear as multi component
mixtures, which makes it an even more demanding task for the olfactory system to
differentiate between these stimuli.
Olfactory sensory neurons (OSN) have response profiles generated by the receptors
they express. Different OSNs have overlapping profiles. Thus, a given odorant
molecule will always activate a set of OSNs. As a consequence, not only will the
different components of a mixture activate a pattern across OSNs, but even a single
OSN is likely activated by several of these components. However, the components
differ in their activation properties for a receptor and occur in varying
concentrations. How will a cell respond to such a mixture? Will the responses of
different ligands sum up or, if mixture interactions occur between the components,
what kind of interactions are they? Will the highest concentrated compound
dominate the response and what is the contribution of minor components?
To address these questions we chose the very well characterized Drosophila
melanogaster receptor Or22a and an odor blend with high relevance for the fly:
banana. Drosophila melanogaster is an ideal model for studying questions like these,
as many genetic tools are available to manipulate cell properties. With its only ~ 50
olfactory receptor types expressed in the adult animal, the olfactory system is rather
simple, though still able to encode thousands of odors.
We created an artificial banana blend from 15 of its major components and studied
how they contribute to the mixture response by applying them individually, in binary
mixtures or all together in their natural banana concentration. We started by
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