@article{KropfRoessler2018,
  author    = {Kropf, Jan and R{\"o}ssler, Wolfgang},
  title     = {In-situ recording of ionic currents in projection neurons and Kenyon cells in the olfactory pathway of the honeybee},
  series = {PLoS ONE},
  volume    = {13},
  journal   = {PLoS ONE},
  number    = {1},
  doi       = {10.1371/journal.pone.0191425},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-175869},
  pages     = {e0191425},
  year      = {2018},
  abstract  = {The honeybee olfactory pathway comprises an intriguing pattern of convergence and divergence: ~60.000 olfactory sensory neurons (OSN) convey olfactory information on ~900 projection neurons (PN) in the antennal lobe (AL). To transmit this information reliably, PNs employ relatively high spiking frequencies with complex patterns. PNs project via a dual olfactory pathway to the mushroom bodies (MB). This pathway comprises the medial (m-ALT) and the lateral antennal lobe tract (l-ALT). PNs from both tracts transmit information from a wide range of similar odors, but with distinct differences in coding properties. In the MBs, PNs form synapses with many Kenyon cells (KC) that encode odors in a spatially and temporally sparse way. The transformation from complex information coding to sparse coding is a well-known phenomenon in insect olfactory coding. Intrinsic neuronal properties as well as GABAergic inhibition are thought to contribute to this change in odor representation. In the present study, we identified intrinsic neuronal properties promoting coding differences between PNs and KCs using in-situ patch-clamp recordings in the intact brain. We found very prominent K+ currents in KCs clearly differing from the PN currents. This suggests that odor coding differences between PNs and KCs may be caused by differences in their specific ion channel properties. Comparison of ionic currents of m- and l-ALT PNs did not reveal any differences at a qualitative level.},
  language  = {en}
}
@article{StrubeBlossBrownSpaetheetal.2015,
  author    = {Strube-Bloss, Martin F. and Brown, Austin and Spaethe, Johannes and Schmitt, Thomas and R{\"o}ssler, Wolfgang},
  title     = {Extracting the Behaviorally Relevant Stimulus: Unique Neural Representation of Farnesol, a Component of the Recruitment Pheromone of Bombus terrestris},
  series = {PLoS One},
  volume    = {10},
  journal   = {PLoS One},
  number    = {9},
  doi       = {10.1371/journal.pone.0137413},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-125875},
  pages     = {e0137413},
  year      = {2015},
  abstract  = {To trigger innate behavior, sensory neural networks are pre-tuned to extract biologically relevant stimuli. Many male-female or insect-plant interactions depend on this phenomenon. Especially communication among individuals within social groups depends on innate behaviors. One example is the efficient recruitment of nest mates by successful bumblebee foragers. Returning foragers release a recruitment pheromone in the nest while they perform a 'dance' behavior to activate unemployed nest mates. A major component of this pheromone is the sesquiterpenoid farnesol. How farnesol is processed and perceived by the olfactory system, has not yet been identified. It is much likely that processing farnesol involves an innate mechanism for the extraction of relevant information to trigger a fast and reliable behavioral response. To test this hypothesis, we used population response analyses of 100 antennal lobe (AL) neurons recorded in alive bumblebee workers under repeated stimulation with four behaviorally different, but chemically related odorants (geraniol, citronellol, citronellal and farnesol). The analysis identified a unique neural representation of the recruitment pheromone component compared to the other odorants that are predominantly emitted by flowers. The farnesol induced population activity in the AL allowed a reliable separation of farnesol from all other chemically related odor stimuli we tested. We conclude that the farnesol induced population activity may reflect a predetermined representation within the AL-neural network allowing efficient and fast extraction of a behaviorally relevant stimulus. Furthermore, the results show that population response analyses of multiple single AL-units may provide a powerful tool to identify distinct representations of behaviorally relevant odors.},
  language  = {en}
}