Dataset Information

Olfactory Behaviors Assayed by Computer Tracking Of Drosophila in a Four-quadrant Olfactometer.

ABSTRACT: A key challenge in neurobiology is to understand how neural circuits function to guide appropriate animal behaviors. Drosophila melanogaster is an excellent model system for such investigations due to its complex behaviors, powerful genetic techniques, and compact nervous system. Laboratory behavioral assays have long been used with Drosophila to simulate properties of the natural environment and study the neural mechanisms underlying the corresponding behaviors (e.g. phototaxis, chemotaxis, sensory learning and memory)(1-3). With the recent availability of large collections of transgenic Drosophila lines that label specific neural subsets, behavioral assays have taken on a prominent role to link neurons with behaviors(4-11). Versatile and reproducible paradigms, together with the underlying computational routines for data analysis, are indispensable for rapid tests of candidate fly lines with various genotypes. Particularly useful are setups that are flexible in the number of animals tested, duration of experiments and nature of presented stimuli. The assay of choice should also generate reproducible data that is easy to acquire and analyze. Here, we present a detailed description of a system and protocol for assaying behavioral responses of Drosophila flies in a large four-field arena. The setup is used here to assay responses of flies to a single olfactory stimulus; however, the same setup may be modified to test multiple olfactory, visual or optogenetic stimuli, or a combination of these. The olfactometer setup records the activity of fly populations responding to odors, and computational analytical methods are applied to quantify fly behaviors. The collected data are analyzed to get a quick read-out of an experimental run, which is essential for efficient data collection and the optimization of experimental conditions.

SUBMITTER: Lin CC

PROVIDER: S-EPMC5091941 | biostudies-other | 2016 Aug

REPOSITORIES: biostudies-other

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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

Olfactory Behaviors Assayed by Computer Tracking Of Drosophila in a Four-quadrant Olfactometer.

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