大鼠嗅球气味响应在体分析与生物电子鼻研究
发布时间：2019-03-13 13:02
【摘要】：生物嗅觉系统对气味具有较高的辨识度,在气味识别速度、气味识别精度上远远超过目前基于化学传感器阵列构建的电子鼻系统。但如何将生物嗅觉应用于实际的气味检测中,仍然存在着很大的挑战。气味分子在动物鼻腔中与嗅觉感受细胞作用后,化学信息转变成生物电信号传导至嗅觉信息处理的中转站—嗅球。通过嗅球对气味信息的处理整合,嗅觉信息进一步传递给大脑皮层中嗅觉相关的皮层,实现气味认知。其中,嗅球被认为是嗅觉初级中枢,对嗅觉形成起到了关键的作用。理解嗅球的气味认知机制,在嗅觉信息处理和实际气味检测中都具有重要意义。 目前,对于气味刺激产生的嗅球细胞响应记录主要采用膜片钳技术和光学成像技术。膜片钳技术不能实现多点同步测量,缺乏大量神经细胞协同作用的信息,而光学成像技术背景噪声大,成像精度仍较难突破。因此,采用植入式微电极阵列传感器对嗅球电生理信号进行长时程、多点同步的监测与分析,有利于理解嗅球细胞群的信息处理,实现生物电子鼻的设计。本文对嗅觉系统多个层次的细胞进行了基于电生理数据的细胞建模与仿真,设计了基于植入式传感器的在体嗅球僧帽细胞气味刺激响应记录与分析平台,分析了嗅球僧帽细胞群气味响应模式。结合生物学和工程学手段,构建新型的生物电子鼻系统实现气味识别。 本论文的主要内容和贡献如下： 1.对嗅觉系统多个层次的细胞进行建模仿真。利用电生理信号建立了基于电压门控通道的嗅觉感受细胞电生理模型,并仿真了嗅觉感受细胞受到刺激时引起细胞动作电位的发放。仿真了僧帽细胞电生理模型。探索了嗅觉系统模型对嗅觉生理现象的仿真,并结合生理实验测得的数据对模型参数进行了优化。 2.设计了麻醉大鼠嗅觉气味响应研究系统。该系统包括气味刺激装置、呼吸信号记录装置、电生理信号记录装置、记录电极的制备平台和数据分析平台。详细描述了大鼠的手术流程与电极植入过程,并对嗅球切片进行染色,验证电极植入位置。 3.提出了短时程气味刺激下大鼠的快速气味感知,实现了时间依赖性的气味分类。采用细胞群脉冲发放矩阵模式分析法,在特征空间中绘制了僧帽细胞群对气味刺激的空间响应曲线,表明麻醉大鼠在短时程气味刺激后的第一个呼吸周期内就完成了对气味的感知。选取细胞发放相似时间段内同步记录的僧帽细胞群脉冲发放特征,用主成分分析方法实现了对五种气味的分类。 4.提出基于僧帽细胞层场电位信号的生物电子鼻设计。低频场电位信号稳定且容易获取,在不同气味刺激下功率谱能量分布存在差异。使用K最邻近分类算法对多窗法谱估计得到的四种气味刺激时频图进行分类,在信号伽马频段(40-120Hz)得到77.4%的分类准确率。
[Abstract]:The bio-olfactory system has a higher recognition degree for odour. The recognition speed and precision of the smell recognition system are much higher than those of the electronic nose system based on the chemical sensor array. However, there is still a great challenge how to apply bio-olfactory to actual odour detection. After the odor molecules interact with olfactory receptive cells in the nasal cavity of animals, the chemical information is transformed into the olfactory bulb, which is transmitted to the olfactory information processing station by bioelectrical signals. Through the integration of olfactory bulb to odor information, olfactory information is further transmitted to the olfactory-related cortex in the cerebral cortex to realize odour cognition. Among them, olfactory bulb is considered as the primary center of olfaction and plays a key role in olfactory formation. Understanding the odor cognition mechanism of olfactory bulb is of great significance in olfactory information processing and actual odour detection. At present, patch clamp technique and optical imaging technique are used to record the response of olfactory bulb cells induced by odour stimulation. Patch clamp technique can not achieve multi-point simultaneous measurement and lacks a lot of information about the synergetic action of nerve cells. However, the background noise of optical imaging technology is large, and the imaging accuracy is still difficult to break through. Therefore, using embedded microelectrode array sensor to monitor and analyze the electrophysiological signals of olfactory bulb for a long time is helpful to understand the information processing of olfactory bulb cell group and realize the design of bio-electronic nose. In this paper, several layers of cells in olfactory system were modeled and simulated based on electrophysiological data, and a recording and analysis platform of odour stimulation response of in vivo olfactory bulb monks was designed based on implanted sensor. The odour response pattern of olfactory bulb monk-cap cell group was analyzed. Combined with biological and engineering methods, a new type of bio-electronic nose system was constructed to realize odor recognition. The main contents and contributions of this paper are as follows: 1. The multiple layers of cells in olfactory system are modeled and simulated. The electrophysiological model of olfactory sensory cells based on voltage-gated channels was established by using electrophysiological signals, and the action potentials of olfactory sensory cells stimulated by stimulation were simulated. The electrophysiological model of monk-cap cells was simulated. This paper explores the simulation of olfactory physiological phenomena by olfactory system model, and optimizes the parameters of the model by combining the measured data of physiological experiments. 2. A study system of olfactory odor response in anesthetized rats was designed. The system comprises an odour stimulating device, a respiratory signal recording device, an electrophysiological signal recording device, a recording electrode preparation platform and a data analysis platform. The procedure of operation and electrode implantation were described in detail. The olfactory bulb sections were stained to verify the location of electrode implantation. 3. Fast odour sensing of rats under short-term odour stimulation was proposed, and time-dependent odour classification was realized. The spatial response curve of monkat cell group to odour stimulation was plotted in the characteristic space by means of the matrix pattern analysis of cell population pulse emission. These results suggest that anaesthetized rats complete the perception of odour during the first respiratory cycle after short-term odour stimulation. According to the pulse emission characteristics of monkat cell group recorded synchronously in the similar period of cell release, the classification of five odors was carried out by principal component analysis (PCA). 4. A design of bio-electronic nose based on the field potential signal of monk-cap cell layer is proposed. The low frequency field potential signal is stable and easy to obtain, and the energy distribution of power spectrum is different under different odour stimuli. The K-nearest neighbor classification algorithm is used to classify the four kinds of odor stimulation time-frequency patterns estimated by multi-window spectral estimation. The classification accuracy is 77.4% in the gamma band (40-120Hz) of the signal.
【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2012
【分类号】：R339.12;TP212.3
本文编号：2439410
[Abstract]:The bio-olfactory system has a higher recognition degree for odour. The recognition speed and precision of the smell recognition system are much higher than those of the electronic nose system based on the chemical sensor array. However, there is still a great challenge how to apply bio-olfactory to actual odour detection. After the odor molecules interact with olfactory receptive cells in the nasal cavity of animals, the chemical information is transformed into the olfactory bulb, which is transmitted to the olfactory information processing station by bioelectrical signals. Through the integration of olfactory bulb to odor information, olfactory information is further transmitted to the olfactory-related cortex in the cerebral cortex to realize odour cognition. Among them, olfactory bulb is considered as the primary center of olfaction and plays a key role in olfactory formation. Understanding the odor cognition mechanism of olfactory bulb is of great significance in olfactory information processing and actual odour detection. At present, patch clamp technique and optical imaging technique are used to record the response of olfactory bulb cells induced by odour stimulation. Patch clamp technique can not achieve multi-point simultaneous measurement and lacks a lot of information about the synergetic action of nerve cells. However, the background noise of optical imaging technology is large, and the imaging accuracy is still difficult to break through. Therefore, using embedded microelectrode array sensor to monitor and analyze the electrophysiological signals of olfactory bulb for a long time is helpful to understand the information processing of olfactory bulb cell group and realize the design of bio-electronic nose. In this paper, several layers of cells in olfactory system were modeled and simulated based on electrophysiological data, and a recording and analysis platform of odour stimulation response of in vivo olfactory bulb monks was designed based on implanted sensor. The odour response pattern of olfactory bulb monk-cap cell group was analyzed. Combined with biological and engineering methods, a new type of bio-electronic nose system was constructed to realize odor recognition. The main contents and contributions of this paper are as follows: 1. The multiple layers of cells in olfactory system are modeled and simulated. The electrophysiological model of olfactory sensory cells based on voltage-gated channels was established by using electrophysiological signals, and the action potentials of olfactory sensory cells stimulated by stimulation were simulated. The electrophysiological model of monk-cap cells was simulated. This paper explores the simulation of olfactory physiological phenomena by olfactory system model, and optimizes the parameters of the model by combining the measured data of physiological experiments. 2. A study system of olfactory odor response in anesthetized rats was designed. The system comprises an odour stimulating device, a respiratory signal recording device, an electrophysiological signal recording device, a recording electrode preparation platform and a data analysis platform. The procedure of operation and electrode implantation were described in detail. The olfactory bulb sections were stained to verify the location of electrode implantation. 3. Fast odour sensing of rats under short-term odour stimulation was proposed, and time-dependent odour classification was realized. The spatial response curve of monkat cell group to odour stimulation was plotted in the characteristic space by means of the matrix pattern analysis of cell population pulse emission. These results suggest that anaesthetized rats complete the perception of odour during the first respiratory cycle after short-term odour stimulation. According to the pulse emission characteristics of monkat cell group recorded synchronously in the similar period of cell release, the classification of five odors was carried out by principal component analysis (PCA). 4. A design of bio-electronic nose based on the field potential signal of monk-cap cell layer is proposed. The low frequency field potential signal is stable and easy to obtain, and the energy distribution of power spectrum is different under different odour stimuli. The K-nearest neighbor classification algorithm is used to classify the four kinds of odor stimulation time-frequency patterns estimated by multi-window spectral estimation. The classification accuracy is 77.4% in the gamma band (40-120Hz) of the signal.
【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2012
【分类号】：R339.12;TP212.3
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