Dataset Information

Representation and transformation of sensory information in the mouse accessory olfactory system.

ABSTRACT: In mice, nonvolatile social cues are detected and analyzed by the accessory olfactory system (AOS). Here we provide a first view of information processing in the AOS with respect to individual chemical cues. 12 sulfated steroids, recently discovered mouse AOS ligands, caused widespread activity among vomeronasal sensory neurons (VSNs), yet VSN responses clustered into a small number of repeated functional patterns or processing streams. Downstream neurons in the accessory olfactory bulb (AOB) responded to these ligands with enhanced signal/noise compared to VSNs. Although the dendritic connectivity of AOB mitral cells suggests the capacity for broad integration, most sulfated steroid responses were well-modeled by linear excitatory drive from just one VSN processing stream. However, a substantial minority demonstrated multi-stream integration. Most VSN excitation patterns were also observed in the AOB, but excitation by estradiol sulfate processing streams was rare, suggesting AOB circuit organization is specific to the biological relevance of sensed cues.

SUBMITTER: Meeks JP

PROVIDER: S-EPMC2930753 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

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Representation and transformation of sensory information in the mouse accessory olfactory system.

Meeks Julian P JP Arnson Hannah A HA Holy Timothy E TE

Nature neuroscience 20100509 6

In mice, nonvolatile social cues are detected and analyzed by the accessory olfactory system (AOS). Here we provide a first view of information processing in the AOS with respect to individual chemical cues. 12 sulfated steroids, recently discovered mouse AOS ligands, caused widespread activity among vomeronasal sensory neurons (VSNs), yet VSN responses clustered into a small number of repeated functional patterns or processing streams. Downstream neurons in the accessory olfactory bulb (AOB) re ...[more]

PMID: 20453853

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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

Representation and transformation of sensory information in the mouse accessory olfactory system.

Publications

Representation and transformation of sensory information in the mouse accessory olfactory system.

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