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Kappe neurons, a novel population of olfactory sensory neurons.

ABSTRACT: Perception of olfactory stimuli is mediated by distinct populations of olfactory sensory neurons, each with a characteristic set of morphological as well as functional parameters. Beyond two large populations of ciliated and microvillous neurons, a third population, crypt neurons, has been identified in teleost and cartilaginous fishes. We report here a novel, fourth olfactory sensory neuron population in zebrafish, which we named kappe neurons for their characteristic shape. Kappe neurons are identified by their Go-like immunoreactivity, and show a distinct spatial distribution within the olfactory epithelium, similar to, but significantly different from that of crypt neurons. Furthermore, kappe neurons project to a single identified target glomerulus within the olfactory bulb, mdg5 of the mediodorsal cluster, whereas crypt neurons are known to project exclusively to the mdg2 glomerulus. Kappe neurons are negative for established markers of ciliated, microvillous and crypt neurons, but appear to have microvilli. Kappe neurons constitute the fourth type of olfactory sensory neurons reported in teleost fishes and their existence suggests that encoding of olfactory stimuli may require a higher complexity than hitherto assumed already in the peripheral olfactory system.

SUBMITTER: Ahuja G

PROVIDER: S-EPMC3918911 | biostudies-other | 2014

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.

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Kappe neurons, a novel population of olfactory sensory neurons.

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