System identification of Drosophila olfactory sensory neurons.
System identification of Drosophila olfactory sensory neurons.
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果蝇嗅觉的感觉神经元的系统鉴定。
DOI：
10.1007/s10827-010-0265-0
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发表时间：
2011-02
影响因子：
1.2
通讯作者：
Slutskiy YB
中科院分区：
文献类型：
--
作者：
Kim AJ;Lazar AA;Slutskiy YB
由于缺乏对嗅觉感觉神经元（OSNs）如何编码气味的深入了解，阻碍了对高级脑中心嗅觉信号处理的理解。在这里，我们采用系统识别的方法来研究时变气味刺激的编码及其在果蝇OSNs中进一步处理的表征。为了应用系统识别技术，我们建立了一种新颖的低湍流气味传递系统，使我们能够以精确和可重复的方式传递空气中的刺激。该系统在刺激再现性方面提供1%的容忍度，并在毫秒时间尺度上精确控制气味浓度和浓度梯度。利用这种新颖的设置，我们记录并分析了osn对各种时变气味波形的体内反应。我们首次报道，在试验中，OR59b osn的反应是非常精确和可重复的。此外，我们的经验表明，OSN的响应不仅取决于浓度，而且取决于气味浓度的变化率。此外，我们证明了浓度-浓度梯度空间中的二维（2D）编码流形提供了神经元响应的定量描述。然后，我们使用白噪声系统识别方法构建了固定平均气味浓度和固定对比度的感觉神经元的一维（1D）和二维（2D）线性-非线性-泊松（LNP）级联模型。我们发现，在预测尖峰序列的强度率方面，二维LNP模型与一维LNP模型表现相当，均方根误差（RMSE）增加了约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，两个模型的非线性块都随着平均输入浓度的变化而变化。对于气味波形的固定平均值，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Or59b osn的白噪声系统识别仅依赖于气味浓度的第一时刻。最后，通过比较二维编码流形和二维LNP模型，我们证明了OSN识别结果取决于所采用的测试气味波形的特定类型。这表明Or59b神经网络的自适应神经编码模型可以改变其响应气味浓度波形的非线性。
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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影响因子：
4.3
作者：
Gu Y;Lucas P;Rospars JP
通讯作者：
Rospars JP
影响因子：
7.8
作者：
Kass, RE;Ventura, V;Cai, C
通讯作者：
Cai, C
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作者：
MARMARELIS, PZ;NAKA, KI
通讯作者：
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作者：
Paninski, L;Lau, B;Reyes, A
通讯作者：
Reyes, A
影响因子：
2.5
作者：
Fairhall, Adrienne L.;Burlingame, C. Andrew;Berry, Michael J., II
通讯作者：
Berry, Michael J., II