Kim Anmo J, Lazar Aurel A, Slutskiy Yevgeniy B
Department of Electrical Engineering, Columbia University, New York, NY, USA.
J Comput Neurosci. 2011 Feb;30(1):143-61. doi: 10.1007/s10827-010-0265-0. Epub 2010 Aug 21.
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.
对嗅觉感觉神经元（OSN）如何编码气味缺乏更深入的理解，阻碍了我们在理解高等脑中枢嗅觉信号处理方面的进展。在此，我们采用系统辨识方法来研究时变气味刺激的编码及其在果蝇OSN的尖峰域中进行进一步处理的表征。为了应用系统辨识技术，我们构建了一种新型的低湍流气味输送系统，该系统使我们能够以精确且可重复的方式输送空气传播的刺激。该系统在刺激可重复性方面提供1%的容差，并能在毫秒时间尺度上精确控制气味浓度和浓度梯度。利用这个新型装置，我们记录并分析了OSN对各种时变气味波形的体内反应。我们首次报告，在多次试验中，OR59b OSN的反应非常精确且可重复。此外，我们通过实验表明，OSN的反应不仅取决于浓度，还取决于气味浓度的变化率。而且，我们证明在浓度 - 浓度梯度空间中的二维（2D）编码流形提供了对神经元反应的定量描述。然后，我们使用白噪声系统辨识方法，针对固定的平均气味浓度和固定对比度，构建感觉神经元的一维（1D）和二维（2D）线性 - 非线性 - 泊松（LNP）级联模型。我们表明，在预测尖峰序列的强度率方面，2D LNP模型与1D LNP模型的表现相当，均方根误差（RMSE）增加约5%至10%。令人惊讶的是，我们发现对于白噪声气味波形的固定对比度，两个模型各自的非线性块会随着平均输入浓度而变化。对于固定的气味波形均值，1D和2D LNP模型的非线性形状似乎与刺激对比度无关。这表明对Or59b OSN的白噪声系统辨识仅取决于气味浓度的一阶矩。最后，通过比较2D编码流形和2D LNP模型，我们证明OSN辨识结果取决于所采用的测试气味波形的特定类型。这表明了一种针对Or59b OSN的自适应神经编码模型，该模型会根据气味浓度波形改变其非线性。
