Dataset Information

Take time: odor coding capacity across sensory neurons increases over time in Drosophila.

ABSTRACT: Due to the highly efficient olfactory code, olfactory sensory systems are able to reliably encode enormous numbers of olfactory stimuli. The olfactory code consists of combinatorial activation patterns across sensory neurons, thus its capacity exceeds the number of involved classes of sensory neurons by a manifold. Activation patterns are not static but vary over time, caused by the temporally complex response dynamics of the individual sensory neuron responses. We systematically analyzed the temporal dynamics of olfactory sensory neuron responses to a diverse set of odorants. We find that response dynamics depend on the combination of sensory neuron and odorant and that information about odorant identity can be extracted from the time course of the response. We also show that new response dynamics can arise when mixing two odorants. Our data show that temporal dynamics of odorant responses are able to significantly enhance the coding capacity of olfactory sensory systems.

SUBMITTER: Munch D

PROVIDER: S-EPMC5696509 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Take time: odor coding capacity across sensory neurons increases over time in Drosophila.

Münch Daniel D Galizia C Giovanni CG

Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology 20170829 12

Due to the highly efficient olfactory code, olfactory sensory systems are able to reliably encode enormous numbers of olfactory stimuli. The olfactory code consists of combinatorial activation patterns across sensory neurons, thus its capacity exceeds the number of involved classes of sensory neurons by a manifold. Activation patterns are not static but vary over time, caused by the temporally complex response dynamics of the individual sensory neuron responses. We systematically analyzed the te ...[more]

PMID: 28852844

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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

Take time: odor coding capacity across sensory neurons increases over time in Drosophila.

Publications

Take time: odor coding capacity across sensory neurons increases over time in Drosophila.

Similar Datasets

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