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Translation of sensory input into behavioral output via an olfactory system.

ABSTRACT: We investigate the logic by which sensory input is translated into behavioral output. First we provide a functional analysis of the entire odor receptor repertoire of an olfactory system. We construct tuning curves for the 21 functional odor receptors of the Drosophila larva and show that they sharpen at lower odor doses. We construct a 21-dimensional odor space from the responses of the receptors and find that the distance between two odors correlates with the extent to which one odor masks the other. Mutational analysis shows that different receptors mediate the responses to different concentrations of an odorant. The summed response of the entire receptor repertoire correlates with the strength of the behavioral response. The activity of a small number of receptors is a surprisingly powerful predictor of behavior. Odors that inhibit more receptors are more likely to be repellents. Odor space is largely conserved between two dissimilar olfactory systems.

SUBMITTER: Kreher SA

PROVIDER: S-EPMC2496968 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature

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Translation of sensory input into behavioral output via an olfactory system.

Kreher Scott A SA Mathew Dennis D Kim Junhyong J Carlson John R JR

Neuron 20080701 1

We investigate the logic by which sensory input is translated into behavioral output. First we provide a functional analysis of the entire odor receptor repertoire of an olfactory system. We construct tuning curves for the 21 functional odor receptors of the Drosophila larva and show that they sharpen at lower odor doses. We construct a 21-dimensional odor space from the responses of the receptors and find that the distance between two odors correlates with the extent to which one odor masks the ...[more]

PMID: 18614033

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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

Translation of sensory input into behavioral output via an olfactory system.

Publications

Translation of sensory input into behavioral output via an olfactory system.

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