大鼠初级视觉皮层V1区视觉刺激的响应信号分析
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摘要
神经系统是人和动物体进行信息处理、加工和存储的主要系统。神经元是神经系统的基本功能单位,人和动物体对各种信息的操纵基本上都是依靠神经元实现的。目前人们对神经系统信息处理机制的理解基本上都是通过对神经元信号传递过程的研究获得的。神经信息科学的任务就是探索神经系统对感知、行为、学习和记忆等方面的控制方式,以便揭示神经信息的产生、传输、加工、编码、存储以及提取等处理机制。视觉神经系统是人和动物体进行信息处理的主要系统,研究已经证明大脑80%信息量是通过视觉系统接收的。因此,研究视觉神经系统的信息处理机制非常必要。
本文以大鼠初级视觉皮层V1区神经元网络为研究对象,以不同朝向的移动光栅为视觉刺激模式,采用微电极阵列技术记录不同视觉刺激条件下V1区神经元的响应信号,目的在于探索响应信号的特征与视觉刺激信息的关系以揭示大鼠初级视觉皮层V1区神经元对视觉信息的响应模式。本文主要对大鼠V1区神经元的两种响应信号(动作电位和局部场电位)进行研究,其主要分为以下两大部分：
第一,对于大鼠初级视觉皮层V1神经元动作电位信号,在研究多种分类方法的基础上,本文采用特征提取法获得动作电位特征确定神经元数目,并在特征集的基础上利用改进K均值算法和层次聚类算法把每个动作电位指定到各个神经元细胞上以便对神经元的放电时间序列进行分析。通过对神经元放电时间序列的分析,发现移动光栅刺激的朝向会在神经元的放电频率、相邻放电时间间隔模式以及复杂度上有不同程度的反映。此外,利用相关函数分析法发现多个神经元放电行为存在同步性,V1区神经元网络能够实现视觉信息处理是多个神经元协同作用的结果。
第二,对于大鼠初级视觉皮层V1神经元局部场电位信号,本文采用时、频域分析方法研究时发现移动光栅刺激的朝向会在平均功率上有所体现,如在移动光栅刺激的朝向为150°时,各通道平均功率相对较大。然而,移动光栅刺激的朝向对局部场电位信号重心频率的影响很小,各通道的重心频率基本上都在1.2～1.9 Hz之间。在对局部场电位信号的相位进行分析时,本文发现不同通道信号之间存在相位同步,并且移动光栅刺激的朝向对各通道信号之间的相位同步程度存在影响。
Nervous system is a main system that human and animal can process, treat and store information by them. Neuron is the basic function unit of the nervous system. We can process all kinds of information that are relying on neurons on the whole. At present, that we understand the mechanism of nervous system information processing is largely dependent on understanding the neuronal signal transferring process. The task of the nerve information science is to explore the control mode of nervous system for perception, behavior, learning, and memory and so on, in order to reveal the information processing mechanism such as neural information generation, transmission, processing, coding, storage and extraction. The visual nerve system is the primary system to process information. The study has proved that 80% information is acquired by the visual system. Therefore, it is very necessary to research the visual nerve system.
In this paper, the neural network of rat primary visual cortex V1 is selected as a research object. With different orientation moving grating as the visual stimulus, with the microelectrode array technology recording response signal for the primary visual cortex V1 neurons when the rat is under the different visual stimulation conditions, the purpose is to explore the relationship between the features of response signals and the visual stimulation information, so as to reveal the response mode of the rat primary visual cortex V1 neurons for the visual information. This paper mainly researches on two kinds of response signal including action potentials and local field p, and can be divided into the following two parts:
Firstly, for the action potential signal of rat primary visual cortex V1 neurons, based on studying multiple methods of classification, the feature extraction method is used to obtain features of action potential and determine the number of neurons. Based on the feature set, the improved K-means algorithm and hierarchical clustering algorithm are used to specify each action potential to each neuron cells in order to analyze the neuron firing time series. Through analyzing the neuron firing time series, we found the orientation of the moving grating stimuli has different degrees of reflection on the firing rate and interspike interval for neurons. In addition, we found there is synchronization through analyzing the firing correlations between neurons by the way of correlation function, and the V1 neural network can achieve visual information processing is the result of neurons'synergy.
Secondly, for the local field potential signals of rat primary visual cortex V1 neurons, with the traditional methods of analyzing biological signals, we found that the orientation of the moving grating stimuli has influence on the average power, such as the average power of each channel is relatively higher when the orientation of the moving grating stimuli is 150°. However, the effect of moving grating stimuli orientation to the local field potential signals'gravity frequency is very small. The gravity frequency of each channel is basically between 1.2 Hz and 1.9 Hz. Through the phase analysis, we observed that this is the phase-synchronization between signal of channels, and the degree of phase-synchronization is different when the orientation of moving grating stimuli is changed.
本文以大鼠初级视觉皮层V1区神经元网络为研究对象,以不同朝向的移动光栅为视觉刺激模式,采用微电极阵列技术记录不同视觉刺激条件下V1区神经元的响应信号,目的在于探索响应信号的特征与视觉刺激信息的关系以揭示大鼠初级视觉皮层V1区神经元对视觉信息的响应模式。本文主要对大鼠V1区神经元的两种响应信号(动作电位和局部场电位)进行研究,其主要分为以下两大部分：
第一,对于大鼠初级视觉皮层V1神经元动作电位信号,在研究多种分类方法的基础上,本文采用特征提取法获得动作电位特征确定神经元数目,并在特征集的基础上利用改进K均值算法和层次聚类算法把每个动作电位指定到各个神经元细胞上以便对神经元的放电时间序列进行分析。通过对神经元放电时间序列的分析,发现移动光栅刺激的朝向会在神经元的放电频率、相邻放电时间间隔模式以及复杂度上有不同程度的反映。此外,利用相关函数分析法发现多个神经元放电行为存在同步性,V1区神经元网络能够实现视觉信息处理是多个神经元协同作用的结果。
第二,对于大鼠初级视觉皮层V1神经元局部场电位信号,本文采用时、频域分析方法研究时发现移动光栅刺激的朝向会在平均功率上有所体现,如在移动光栅刺激的朝向为150°时,各通道平均功率相对较大。然而,移动光栅刺激的朝向对局部场电位信号重心频率的影响很小,各通道的重心频率基本上都在1.2～1.9 Hz之间。在对局部场电位信号的相位进行分析时,本文发现不同通道信号之间存在相位同步,并且移动光栅刺激的朝向对各通道信号之间的相位同步程度存在影响。
Nervous system is a main system that human and animal can process, treat and store information by them. Neuron is the basic function unit of the nervous system. We can process all kinds of information that are relying on neurons on the whole. At present, that we understand the mechanism of nervous system information processing is largely dependent on understanding the neuronal signal transferring process. The task of the nerve information science is to explore the control mode of nervous system for perception, behavior, learning, and memory and so on, in order to reveal the information processing mechanism such as neural information generation, transmission, processing, coding, storage and extraction. The visual nerve system is the primary system to process information. The study has proved that 80% information is acquired by the visual system. Therefore, it is very necessary to research the visual nerve system.
In this paper, the neural network of rat primary visual cortex V1 is selected as a research object. With different orientation moving grating as the visual stimulus, with the microelectrode array technology recording response signal for the primary visual cortex V1 neurons when the rat is under the different visual stimulation conditions, the purpose is to explore the relationship between the features of response signals and the visual stimulation information, so as to reveal the response mode of the rat primary visual cortex V1 neurons for the visual information. This paper mainly researches on two kinds of response signal including action potentials and local field p, and can be divided into the following two parts:
Firstly, for the action potential signal of rat primary visual cortex V1 neurons, based on studying multiple methods of classification, the feature extraction method is used to obtain features of action potential and determine the number of neurons. Based on the feature set, the improved K-means algorithm and hierarchical clustering algorithm are used to specify each action potential to each neuron cells in order to analyze the neuron firing time series. Through analyzing the neuron firing time series, we found the orientation of the moving grating stimuli has different degrees of reflection on the firing rate and interspike interval for neurons. In addition, we found there is synchronization through analyzing the firing correlations between neurons by the way of correlation function, and the V1 neural network can achieve visual information processing is the result of neurons'synergy.
Secondly, for the local field potential signals of rat primary visual cortex V1 neurons, with the traditional methods of analyzing biological signals, we found that the orientation of the moving grating stimuli has influence on the average power, such as the average power of each channel is relatively higher when the orientation of the moving grating stimuli is 150°. However, the effect of moving grating stimuli orientation to the local field potential signals'gravity frequency is very small. The gravity frequency of each channel is basically between 1.2 Hz and 1.9 Hz. Through the phase analysis, we observed that this is the phase-synchronization between signal of channels, and the degree of phase-synchronization is different when the orientation of moving grating stimuli is changed.
引文
[1]罗四维.视觉感知系统信息处理理论[M].北京：电子共业出版社,2006
[2]Garrett B. Stanley, Fei F. Li, Yang Dan. Reconstruction of Natural Scenes from Ensemble Responses in the Lateral Geniculate Nucleus[J]. The Journal of Neuroscience,1999,19 (18):8036-8042
[3]Fred Rieke, David Warland, Rob de Ruyter van Steveninck, et al. Spikes:Exploring the Neural Code[M]. Cambridge:MIT Press,1997
[4]寿天德.视觉信息处理的脑机制.上海：上海科技教育出版社,1997
[5]Polyak, S.. The Vertebrate Visual System[M]. Chicago:Univ. Chicago Press,1957
[6]Peter Dayan, L.F. Abbott. Theoretical Neuroscience [M]. The MIT Press,2001
[7]Bruno B. Averbeck, Daeyeol Lee. Coding and transmission of information by neural ensembles[J]. Trends In Neurosciences,2004,27 (4):225-230
[8]Diego A. Guntnisky, Valentin Dragoi. Adaptive coding of visual information in neural populations[J]. Nature,2008,452:220-225
[9]Gersterin GL., Clark W.A. Simultaneous studies of firing patterns in several neurons[J]. Science,1964,143:1325-1327
[10]Richard B. Stein, Douglas J. Weber. Editing trains of action potentials from multi-electrode arrays [J]. Journal of Neuroscience Methods,2004,134 (1):91-100
[11]Emery N Brown, Robert E Kass, Partha P Mitra. Multiple neural spike train data analysis: state-of-the-art and future challenges [J]. Nature Neuroscience,2004,7(5):456-461.
[12]Michael S Lewicki. A review of method for spike sorting:the detection and classification of neural action potentials [J]. Nerwork Comput Neyral yst,1998,9(4):53-57
[13]Rodrigo Quian Quiroga. Spike sorting [J]. Scholarpedia,2007,2(12):3583
[14]Andre Snellings, David J. Anderson, J. Wayne Aldridge. Improved signal and reduced noise in neural recordings from close-spaced electrode arrays using independent component analysis as a preprocessor [J]. Journal of Neuroscience Methods,2006,150(2):254-264
[15]Pu-Ming Zhang, Jin-Yong Wu, Yi Zhou, et al. Spike soring in multi-channel extracellular recordings of retinas [C]. IEEE Int. Conf. Neural Networks & Signal Processing. Nanjing, China,2003,712-715
[16]Bruce C. Wheeler, William J. Heetderks. A comparison of techniques for classification of multiple neural signal [J]. IEEE Transactions On Biomedical Engineering,1982,29 (12): 752-759
[17]Ronen Segev, Joe Goodhouse, Jason Puchalla, et al. Recording spikes from a large fraction of the ganglion cells in a retinal patch [J]. Nature Neurscience,2004,7(10):1155-1162
[18]刘海龙.MEA信号动作电位的小波变换分类[J].计算机与数字工程,2006,34(4)：35-38
[19]Michael S. Lewicki. Bayesian Modeling and Classification of Neural Signals [J]. Neural Computation,1994,6 (5):1005-1030
[20]Shoham S, Fellows M R, Normamn R A. Robust, automatic spike sorting using mixtures of multivariate t-distributaries [J]. Journal of Neuroscience methods,2003,127 (2):111-122
[21]Gozani S N, Miller J P. Optimal dicrimination and classification of neuronal action potential waveforms from multiunit, multichannel recordings using software-based linear filters[J]. IEEE Transactions on biomedical engineering,1994,41 (4):358-372
[22]Jianhua Dai, Xiaochun Liu, Shaomin Zhang, et al. Neuronal Spike Sorting based on 2-stage RBF Networks [C].2008 Second International Conference on Future Generation Communication and Networking. USA, IEEE computer society:2008.47-50
[23]Jianhua Dai, Xiaochun Liu, Shaomin Zhang, et al. Experimental Study on Neuronal Spike Sorting Methods [C].2008 Second International Conference on Future Generation Communication and Networking. USA, IEEE computer society:2008,230-233.
[24]Adrian ED. The mechanism of nervous action:electrical studies of the neuron.[M]. London: Oxford Press,1932
[25]Mark J. Schnitzer, Markus Meister. Multineuronal firing patterns in the signal from eye to brain[J]. Neuron,2003,37:499-511
[26]Markus Meister, Leon Lagnado, Denis A. Baylor. Concerted signaling by retinal ganglion Cells[J]. Science,1995,270:1207-1210
[27]Meister, M.and Berry. The neural code of the retina[J]. Neuron,1999,22:435-450
[28]Nicholas A Lesica, Alireza S Boloori, Garrett B Stanley. Adaptive encoding in the visual pathway[J]. Network,2003,14:119-135
[29]Carter AG, Regehr WG. Quantal events shape cerebellar interneuron firing[J]. Nature Neuroscience,2002,5 (12):1309-1318
[30]Ghitza UE, Fabbricatore AT, Prokopenko V, et al. Persistent cue-evoked activity of accumbens neurons after prolonged abstinence from self-administered cocaine[J]. The Journal of Neuroscience,2003,23 (19):7239-7245
[31]Celikel T, Szostak VA, Feldman DE. Modulation of spike timing by sensory deprivation during induction of cortical map plasticity[J].Nature neuroscience,2004,7(5):534-541
[32]Mazurek ME, Shadlen MN. Limits to the temporal fidelity of cortical spike rate signals [J]. Nature neuroscience,2002,5 (5):463-471
[33]陈爱华,周艺.小鸡视网膜神经节细胞的反应特性：多电极记录研究[J].中国科学,2003,33(1)：82-88
[34]Don H. Johnson. Information Theory and Neural Information Processing[J].IEEE Transaction On Information Theroy,2010,56 (2):653-666
[35]Rodrigo Quian Quiroga, Stefano Panzeri. Extracting information from neuronal populations: information theory and decoding approaches [J]. Nature,2009,10:173-185
[36]Hanjorg Scherberger, Murray R. Jarvis, land Richard A. Andersen 1. Cortical local field potential encodes movement intentions in the posterior parietial cortex[J]. Neuron,2005.46: 347-354
[37]Roelfsema P R, Engel A K, Konig P, et al. Visumotor intergration is associated with zero time-lag synchronization anong cortical areas[J]. Nature,1997,385:157-161
[38]Destexhe A, Contreras D, Steriade M. Spatiotemporal analysis of local field potentials and unit discharges in cat cerebral cortex during natural wake and sleep states[J]. Journal of Neuroscience,1999,19:4595-4608
[39]Murller M, Baier G, Galka A, et al. Detection and characterization of changes of the correlation structure in multivariate time series[J]. Physical Review E,2005,71:046116
[40]Peter E. Latham, Mate Lengyel. Phase Coding:Spikes Get a Boost from Local Fields[J], Current Biology,2008,18:349-351
[41]Marcelo A. Montemurro, Malte J. Rasch, Yusuke Murayama, et al. Phase-of-Firing Coding of Natural Visual Stimuli in Primary Visual Cortex[J]. Current Biology,2008,18:375-380
[42]P.G. Musial, S.N. Baker, GL. Gerstein, et al. Signal-to-noise ratio improvement in multiple electrode recording[J]. Journal of Neuroscience Methods,2002,115:29-43
[43]李宏东,姚天翔模.模式分类[M].北京：机械工业出版社,2003
[44]Zhang X, Zhang RL, Zhang ZG, et al. Measurement of nueronal activity of individual neurons after stock in the rat using a microwire electrode array[J]. J Neyrosci Methods, 2007,162:91-100
[45]姚舜,刘海龙,陈传平.局部场电位和微分特性影响下神经元网络发放锋电位的检测[J].计算机与机械工程,2005,33(12)：19-23
[2]Garrett B. Stanley, Fei F. Li, Yang Dan. Reconstruction of Natural Scenes from Ensemble Responses in the Lateral Geniculate Nucleus[J]. The Journal of Neuroscience,1999,19 (18):8036-8042
[3]Fred Rieke, David Warland, Rob de Ruyter van Steveninck, et al. Spikes:Exploring the Neural Code[M]. Cambridge:MIT Press,1997
[4]寿天德.视觉信息处理的脑机制.上海：上海科技教育出版社,1997
[5]Polyak, S.. The Vertebrate Visual System[M]. Chicago:Univ. Chicago Press,1957
[6]Peter Dayan, L.F. Abbott. Theoretical Neuroscience [M]. The MIT Press,2001
[7]Bruno B. Averbeck, Daeyeol Lee. Coding and transmission of information by neural ensembles[J]. Trends In Neurosciences,2004,27 (4):225-230
[8]Diego A. Guntnisky, Valentin Dragoi. Adaptive coding of visual information in neural populations[J]. Nature,2008,452:220-225
[9]Gersterin GL., Clark W.A. Simultaneous studies of firing patterns in several neurons[J]. Science,1964,143:1325-1327
[10]Richard B. Stein, Douglas J. Weber. Editing trains of action potentials from multi-electrode arrays [J]. Journal of Neuroscience Methods,2004,134 (1):91-100
[11]Emery N Brown, Robert E Kass, Partha P Mitra. Multiple neural spike train data analysis: state-of-the-art and future challenges [J]. Nature Neuroscience,2004,7(5):456-461.
[12]Michael S Lewicki. A review of method for spike sorting:the detection and classification of neural action potentials [J]. Nerwork Comput Neyral yst,1998,9(4):53-57
[13]Rodrigo Quian Quiroga. Spike sorting [J]. Scholarpedia,2007,2(12):3583
[14]Andre Snellings, David J. Anderson, J. Wayne Aldridge. Improved signal and reduced noise in neural recordings from close-spaced electrode arrays using independent component analysis as a preprocessor [J]. Journal of Neuroscience Methods,2006,150(2):254-264
[15]Pu-Ming Zhang, Jin-Yong Wu, Yi Zhou, et al. Spike soring in multi-channel extracellular recordings of retinas [C]. IEEE Int. Conf. Neural Networks & Signal Processing. Nanjing, China,2003,712-715
[16]Bruce C. Wheeler, William J. Heetderks. A comparison of techniques for classification of multiple neural signal [J]. IEEE Transactions On Biomedical Engineering,1982,29 (12): 752-759
[17]Ronen Segev, Joe Goodhouse, Jason Puchalla, et al. Recording spikes from a large fraction of the ganglion cells in a retinal patch [J]. Nature Neurscience,2004,7(10):1155-1162
[18]刘海龙.MEA信号动作电位的小波变换分类[J].计算机与数字工程,2006,34(4)：35-38
[19]Michael S. Lewicki. Bayesian Modeling and Classification of Neural Signals [J]. Neural Computation,1994,6 (5):1005-1030
[20]Shoham S, Fellows M R, Normamn R A. Robust, automatic spike sorting using mixtures of multivariate t-distributaries [J]. Journal of Neuroscience methods,2003,127 (2):111-122
[21]Gozani S N, Miller J P. Optimal dicrimination and classification of neuronal action potential waveforms from multiunit, multichannel recordings using software-based linear filters[J]. IEEE Transactions on biomedical engineering,1994,41 (4):358-372
[22]Jianhua Dai, Xiaochun Liu, Shaomin Zhang, et al. Neuronal Spike Sorting based on 2-stage RBF Networks [C].2008 Second International Conference on Future Generation Communication and Networking. USA, IEEE computer society:2008.47-50
[23]Jianhua Dai, Xiaochun Liu, Shaomin Zhang, et al. Experimental Study on Neuronal Spike Sorting Methods [C].2008 Second International Conference on Future Generation Communication and Networking. USA, IEEE computer society:2008,230-233.
[24]Adrian ED. The mechanism of nervous action:electrical studies of the neuron.[M]. London: Oxford Press,1932
[25]Mark J. Schnitzer, Markus Meister. Multineuronal firing patterns in the signal from eye to brain[J]. Neuron,2003,37:499-511
[26]Markus Meister, Leon Lagnado, Denis A. Baylor. Concerted signaling by retinal ganglion Cells[J]. Science,1995,270:1207-1210
[27]Meister, M.and Berry. The neural code of the retina[J]. Neuron,1999,22:435-450
[28]Nicholas A Lesica, Alireza S Boloori, Garrett B Stanley. Adaptive encoding in the visual pathway[J]. Network,2003,14:119-135
[29]Carter AG, Regehr WG. Quantal events shape cerebellar interneuron firing[J]. Nature Neuroscience,2002,5 (12):1309-1318
[30]Ghitza UE, Fabbricatore AT, Prokopenko V, et al. Persistent cue-evoked activity of accumbens neurons after prolonged abstinence from self-administered cocaine[J]. The Journal of Neuroscience,2003,23 (19):7239-7245
[31]Celikel T, Szostak VA, Feldman DE. Modulation of spike timing by sensory deprivation during induction of cortical map plasticity[J].Nature neuroscience,2004,7(5):534-541
[32]Mazurek ME, Shadlen MN. Limits to the temporal fidelity of cortical spike rate signals [J]. Nature neuroscience,2002,5 (5):463-471
[33]陈爱华,周艺.小鸡视网膜神经节细胞的反应特性：多电极记录研究[J].中国科学,2003,33(1)：82-88
[34]Don H. Johnson. Information Theory and Neural Information Processing[J].IEEE Transaction On Information Theroy,2010,56 (2):653-666
[35]Rodrigo Quian Quiroga, Stefano Panzeri. Extracting information from neuronal populations: information theory and decoding approaches [J]. Nature,2009,10:173-185
[36]Hanjorg Scherberger, Murray R. Jarvis, land Richard A. Andersen 1. Cortical local field potential encodes movement intentions in the posterior parietial cortex[J]. Neuron,2005.46: 347-354
[37]Roelfsema P R, Engel A K, Konig P, et al. Visumotor intergration is associated with zero time-lag synchronization anong cortical areas[J]. Nature,1997,385:157-161
[38]Destexhe A, Contreras D, Steriade M. Spatiotemporal analysis of local field potentials and unit discharges in cat cerebral cortex during natural wake and sleep states[J]. Journal of Neuroscience,1999,19:4595-4608
[39]Murller M, Baier G, Galka A, et al. Detection and characterization of changes of the correlation structure in multivariate time series[J]. Physical Review E,2005,71:046116
[40]Peter E. Latham, Mate Lengyel. Phase Coding:Spikes Get a Boost from Local Fields[J], Current Biology,2008,18:349-351
[41]Marcelo A. Montemurro, Malte J. Rasch, Yusuke Murayama, et al. Phase-of-Firing Coding of Natural Visual Stimuli in Primary Visual Cortex[J]. Current Biology,2008,18:375-380
[42]P.G. Musial, S.N. Baker, GL. Gerstein, et al. Signal-to-noise ratio improvement in multiple electrode recording[J]. Journal of Neuroscience Methods,2002,115:29-43
[43]李宏东,姚天翔模.模式分类[M].北京：机械工业出版社,2003
[44]Zhang X, Zhang RL, Zhang ZG, et al. Measurement of nueronal activity of individual neurons after stock in the rat using a microwire electrode array[J]. J Neyrosci Methods, 2007,162:91-100
[45]姚舜,刘海龙,陈传平.局部场电位和微分特性影响下神经元网络发放锋电位的检测[J].计算机与机械工程,2005,33(12)：19-23