Journal of Computational Neuroscience

, Volume 30, Issue 1, pp 143–161 | Cite as

System identification of Drosophila olfactory sensory neurons

  • Anmo J. Kim
  • Aurel A. LazarEmail author
  • Yevgeniy B. Slutskiy


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.


System identification Olfactory sensory neurons White noise analysis I/O modeling 

Alphabetical list of abbreviations






Bayesian Adaptive Regression Splines


Dipropylene glycol






Maximally informative dimensions


Olfactory sensory neuron


Photoionization detector


Peristimulus time histogram


Reverse correlation


Root-mean-square error


Spike-triggered average


Spike-triggered covariance



The work presented here was supported by NIH under grant number R01DC008701-01 and was conducted in the Axel laboratory at Columbia University. The authors would like to thank Dr. Richard Axel for insightful discussions and for his outstanding support. The authors would like to also thank the reviewers for their suggestions for improving the presentation of the paper.

Supplementary material

10827_2010_265_MOESM1_ESM.pdf (4.3 mb)
Supplementary Material (PDF 4.33 MB)


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Anmo J. Kim
    • 1
  • Aurel A. Lazar
    • 1
    Email author
  • Yevgeniy B. Slutskiy
    • 1
  1. 1.Department of Electrical EngineeringColumbia UniversityNew YorkUSA

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