Dataset Information

Coincidence of pheromone and plant odor leads to sensory plasticity in the heliothine olfactory system.

ABSTRACT: Male moths possess a highly specialized olfactory system comprised of two segregated sub-arrangements dedicated to processing information about plant odors and pheromones, respectively. Communication between these two sub-systems has been described at the peripheral level, but relatively little is known about putative interactions at subsequent synaptic relays. The male moth faces the challenge of seeking out the conspecific female in a highly dynamic odor world. The female-produced pheromone blend, which is a limited resource serving as guidance for the male, will reach his antennae in intermittent pockets of odor filaments mixed with volatiles from various plants. In the present study we performed calcium imaging for measuring odor-evoked responses in the uni-glomerular antennal-lobe projection neurons (analog to mitral cells in the vertebrate olfactory bulb) of Helicoverpa armigera. In order to investigate putative interactions between the two sub-systems tuned to plant volatiles and pheromones, respectively, we performed repeated stimulations with a selection of biologically relevant odors. We found that paired stimulation with a plant odor and the pheromone led to suppressed responses in both sub-systems as compared to those evoked during initial stimulation including application of each odor stimulus alone. The fact that the suppression persisted also after pairing, indicates the existence of a Hebbian-like plasticity in the primary olfactory center established by temporal pairing of the two odor stimulation categories.

SUBMITTER: Ian E

PROVIDER: S-EPMC5414983 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The lack of a deeper understanding of how olfactory sensory neurons (OSNs) encode odors has hindered the progress in understanding the olfactory signal processing in higher brain centers. Here we employ methods of system identification to investigate the encoding of time-varying odor stimuli and their representation for further processing in the spike domain by Drosophila OSNs. In order to apply system identification techniques, we built a novel low-turbulence odor delivery system that allowed us to deliver airborne stimuli in a precise and reproducible fashion. The system provides a 1% tolerance in stimulus reproducibility and an exact control of odor concentration and concentration gradient on a millisecond time scale. Using this novel setup, we recorded and analyzed the in-vivo response of OSNs to a wide range of time-varying odor waveforms. We report for the first time that across trials the response of OR59b OSNs is very precise and reproducible. Further, we empirically show that the response of an OSN depends not only on the concentration, but also on the rate of change of the odor concentration. Moreover, we demonstrate that a two-dimensional (2D) Encoding Manifold in a concentration-concentration gradient space provides a quantitative description of the neuron's response. We then use the white noise system identification methodology to construct one-dimensional (1D) and two-dimensional (2D) Linear-Nonlinear-Poisson (LNP) cascade models of the sensory neuron for a fixed mean odor concentration and fixed contrast. We show that in terms of predicting the intensity rate of the spike train, the 2D LNP model performs on par with the 1D LNP model, with a root mean-square error (RMSE) increase of about 5 to 10%. Surprisingly, we find that for a fixed contrast of the white noise odor waveforms, the nonlinear block of each of the two models changes with the mean input concentration. The shape of the nonlinearities of both the 1D and the 2D LNP model appears to be, for a fixed mean of the odor waveform, independent of the stimulus contrast. This suggests that white noise system identification of Or59b OSNs only depends on the first moment of the odor concentration. Finally, by comparing the 2D Encoding Manifold and the 2D LNP model, we demonstrate that the OSN identification results depend on the particular type of the employed test odor waveforms. This suggests an adaptive neural encoding model for Or59b OSNs that changes its nonlinearity in response to the odor concentration waveforms.

Dataset Information

Coincidence of pheromone and plant odor leads to sensory plasticity in the heliothine olfactory system.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets