多通道神经元锋电位检测和分类的新方法

大脑神经元胞外单细胞动作电位(即锋电位)的检测和分类是提取神经元脉冲序列、研究神经系统信息处理机制的关键．为了提高锋电位的检出率和分类的正确性,设计了一种处理多通道锋电位记录信号的算法,用于分析微电极阵列记录的大鼠海马神经元锋电位信号,电极阵列上的测量点排列紧密,4个通道可以同时记录到来自相同神经元的信号．该算法首先利用一种多通道阈值检测法检出四通道记录信号中的锋电位,然后利用一种基于复合锋电位的主成分特征参数分类法将锋电位分类．仿真数据和实验记录信号的检验结果表明：与相应的单通道算法相比,该算法的锋电位检出率和分类的正确性显著提高,并且可以增加单次实验测得的神经元数目．因此,该算法为实现神经元锋电位的自动检测提供了一种简单有效的新 方法．     

基于联合锋电位-局部场电位的整合野调制特性研究

整合野调制在生物视觉系统的图形背景分离和轮廓检测中具有重要作用。为了研究整合野空间频率调制特性,首先测定单个神经元的感受野范围和整合类型,挑选出要研究的抑制性神经元;然后,用匹配追踪算法和希尔伯特变换提取Spike触发的伽马频带LFP瞬时能量;最后,将Spike触发的伽马频带LFP(local field potential,LFP)瞬时能量用于分析神经元的整合调制特性.对在大鼠初级视觉皮层(primary visual cortex,V1区)采集的96个神经元进行统计分析,实验结果表明:1)Spike触发的伽马频带LFP瞬时能量可以作为分析V1区整合野调制特性的有效指标;2)基于Spike触发的伽马频带LFP瞬时能量所获得的抑制指数要显著高于通过Spike发放率以及伽马频带LFP功率的结果(P0.001),并且整合野的调制规律性更加明显,个体适用性更好。     

一种基于支持向量机的自适应肿瘤分类检测算法

根据肿瘤分类检测模型的特点,提出了一种新的算法,该算法结合使用了基因选择和数据抽取的有效方法,并在此基础上使用支持向量机对基因表达数据进行分类或者检测。其中乳腺癌的分类交叉验证结果由88．46％提高到100．0％,急性白血病的也由71．05％提高至100．0％。实验结果说明了这一方法的有效性,为在大量的基因表达数据中提高检测癌症的准确性提出了一种比较通用的方法。     

自适应蚁群算法在DNA中的应用

主要讨论了自适应蚁群算法在DNA序列比对中的应用,主要的过程是：首先,我们设一个计分函数和一个得分策略,在任意给出一对DNA序列,建立一个序列比对矩阵．现由4只蚂蚁从左上角向右下角移动,并且最终到达右下角,那么这4只蚂蚁随意走出4条路径,根据4条路径得出4对等长的比对,再依照计分函数分别计算出4条路径的比对得分,再由1．3式进一步验证4条路径的平均得分值,取其中得分最高（即最优路径）路径;进行第二次信息素增量的调整,方法是根据蚂蚁所走过的方向和该方向上得分比例计算出来的,信息素的变化量利用矩阵来存储,那么下一次蚂蚁所选的路径就要根据以前在各条路径上的信息素浓度总和的大小选择移动方向,最终经过有限次迭代,蚂蚁就会找到一条最优路径,也就是一条与原来DNA最相似的DNA链．     

高频电刺激改变神经元锋电位的波形

为了正确检测和研究高频电刺激(high frequencystimulation,HFS)期间神经元的动作电位发放活动,进而深入揭示深部脑刺激治疗神经系统疾病的机制,本课题研究HFS期间锋电位波形的变化.在麻醉大鼠海马CA1区的输入神经通路Schaffer侧支上,施加1~2 min时长的100或者200 Hz顺向高频刺激(orthodromic-HFS,O-HFS),利用微电极阵列采集刺激下游神经元的多通道锋电位信号,并获得由O-HFS经过单突触传导激活的中间神经元的单元锋电位波形及其特征参数.结果表明,O-HFS使得锋电位的幅值明显减小而半高宽明显增加,以基线记录为基准计算百分比值,O-HFS期间锋电位的降支幅值和升支幅值分别可减小20%和40%左右,半高宽则增加10%以上.并且,在大量神经元同时产生动作电位期间,或者在比200 Hz具有更大兴奋作用的100 Hz刺激期间,锋电位波形的改变更多,幅值的减小可达50%,宽度的增加可达20%.可以推测,高频电刺激对于神经元的兴奋作用可能升高细胞膜电位,从而改变细胞膜离子通道的活动特性,导致动作电位波形的改变.这些结果支持深部脑刺激具有兴奋性调节作用的假说,对于正确分析高频电刺激期间神经元锋电位活动具有指导意义,也为进一步研究深部脑刺激(DBS)治疗脑神经系统疾病的机制提供了重要线索.     

新疆南部戊肝疫区间隔20年后局部再爆发的调查

为了探讨戊肝流行20年后再爆发的特点,对2001年喀什地区莎车县某乡戊型肝炎的流行和临床特点进行了调查,并对病后半年的部分病例用ELISA方法检测戊肝抗体水平。结果发生在1981～1983年大流行时散发病的疫区,间隔20年后再次局部爆发戊肝,乡总患病率0．64％(167／25979),分布在相邻六个自然村,每村平均患病人数为28人。饮用水为手压井(地下水),全年以8～12月为一高峰,均为维族农民,男98人,女69人,最大年龄54岁,最小年龄12岁,中位年龄28．9岁。临床表现符合急性黄疸型肝炎,病情轻,无重症和死亡病例。抽查病例中病后半年抗-HEVIgG阳性率68％,IgM阳性率16％,双阳性12％。首发病例传染源尚不清楚,可排除水源和食物污染,流行和临床特征符合戊肝,应探讨HEV的变异和人群抗体及保护力的持续时间。     

心室纤颤信号的自适应检测

在分析了心室纤颤信号和心动过速信号自相关性的基础上,运用自适应ＬＭＳ算法。以误差信号能量作为特征值,成功地检测了心室纤颤信号,比仅依赖心率来判别心跳是否正常可靠性要高一些。通过对１５６个数据学习和三次判断,其心室纤颤信号识别率达到９７．４％,心动过速信号识别率达到１００％．     

基因转录爆发的信号转导机制

随着单分子检测技术水平的提高,学术界发现了与传统认知大相径庭的转录现象——转录爆发。基因转录不是连续平稳且波动较小的过程,而是表现出发生时间极不规律、信使RNA产量波动极大的特征。转录爆发本质上是编码和传递调控信号的方式。本文综述了转录爆发现象、转录爆发的两态模型、转录爆发信号转导机制以及其重要的生物学意义。最后,指出了该领域亟待解决的问题。     

基于差分进化算法的酿酒酵母分批补料培养在线自适应控制

酿酒酵母分批补料培养中,葡萄糖添加过量会导致乙醇大量积累,破坏细胞结构及功能,降低葡萄糖利用效率;葡萄糖添加不足会限制细胞生长。为解决这一矛盾,提出了一种基于差分进化算法的在线自适应控制策略,并利用计算机仿真方法对该策略、传统的间歇流加、分段恒速流加及PID控制策略的控制性能进行了研究和比较。结果表明,在该控制策略下,发酵液中的乙醇浓度能够被稳定地控制在1g/L的低水平,而细胞浓度却达到34.45g/L的高水平,比采用间歇流加、分段恒速流加及PID控制策略的批次分别提高了243%、18%和29%。由此可知,该自适应控制策略能够将葡萄糖流加速率控制在适宜水平,避免乙醇过量积累的同时保证细胞的快速增殖。     

刺激隐神经中C类纤维诱发的小脑浦肯野细胞简单锋电位反应

用玻璃微电极记录了猫小脑浦肯野细胞的简单锋电位(PC-SS)。在标准化互协方差函数图中,PC-SS自发放电无明显波峰;弱刺激隐神经只引起A类纤维传入时,PC-SS出现A类诱发放电反应(A-CED),它包括潜伏期为16.7±0.9ms的早反应和270.8±12.8ms的晚反应。用极化电流选择性阻滞A类纤维传导后,强刺激只引起C类纤维单独传入时,出现潜伏期为142.4±4.3ms的C类诱发反应(C-CED)。强刺激同时引起A类和C类纤维传入时,只出现A-CED而不出现C-CED。按标准化功率谱密度函数分析,PC-SS自发放电可分为两种类型。一类为高峰型,最大能量峰值平均为15.7±4.7×10~(-3),峰频为4.07±1.67Hz;刺激A类纤维使峰值增大,而刺激C类纤维却使峰值减小。另一类为低峰型,峰值为8.4±1.4×10~(-3),峰频为3.67±2.90Hz。刺激A类和C类纤维均使峰值增大,前者增大更多,但峰频均无显著性变化。上述结果表明,C类纤维传入可以到达小脑浦肯野细胞,引起特异的PC-SS放电反应。     

STOCHASTIC RESONANCE MEASURED BY THEMUTUAL INFORMATION IN THE NEURONSTHAT TRANSMIT CHAOTIC SPIKE TRAINS

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急性大鼠癫痫模型海马神经元原发性单位后放电脉冲间隔特征

受到刺激后即刻出现的海马（hippocampus,HPC）原发性单位后放电是癫痫相关性细胞电活动的重要形式之一,其放电脉冲间隔（interspike interval,ISI）和串内平均频率（Hz）特征及其在网络癫痫形成中的作用值得探讨。实验用急性强直电刺激（60Hz,2S,0．4-0．6mA）大鼠右侧后背HPC（acute tetanization of the fight posterior dorsal hippocampus,以后简称ATPDH）或右侧尾壳核（acute tetanization of the fight caudate putamen nucleus,以后简称ATRC）诱导HPC或皮层网络癫痫,重点观察HPC神经元原发性单位后放电模式和上述的瞬时时间编码特征。结果表明：（1）HPC原发性单位后放电表现为两种不同的放电模式,即先易化后抑制或先抑制后易化,其ISI序列分别表现为先小后大的“头尾”式分布或先大后小的“尾头”式分布。（2）ATFDH主要引起“尾头”式（35／57串）、而ATRC主要引起“头尾”式（12／22串）ISI点分布的原发性单位后放电,串内“头”、“尾”平均持续时间均具有明显差异（P〈0．05）。（3）ATRC可以诱导双侧HPC单位后放电出现交互的“头尾”、‘呢头”式ISI点分布特征。（4）多串电刺激可以诱导HPC原发性单位后放电特征性ISI点分布重复显现。（5）特征性HPC原发性单位后放电伴随出现网络癫痫发作样高频电振荡。这提示：强直电刺激诱导的HPC神经元原发性单位后放电“头尾”或呢头”式ISI序列分布规律,可以较准确地反映所记录神经元的诱发性易化或抑制活动的程度,用于网络癫痫形成中单个成员细胞癫痫相关性电活动机制的分析。     

神经起步点产生的一种新型簇放电节律———阵发周期1节律

实验中发现了神经起步点产生的一种新型的簇放电节律－－阵发周期1节律。其特征如下：连续周期1放电与休止期（quiescence）轮流出现;非周期性,连续放电持续期、连续放电次数以及休止期有较大变异性;位于周期1节律和静息状态之间。具有较长周期的伪单色噪声激励的FHN（FizHugh-Nagumo）模型可以产生类似的阵发周期1节律。模型和实验中的阵发周期1节律的统计特征、变化规律和所处的参数区间相类似。这表明：阵发周期1节律是由与伪单色噪声类似的长时程振荡激励引起的。     

Firing statistics of inhibitory neuron with delayed feedback. I. Output ISI probability density

Activity of inhibitory neuron with delayed feedback is considered in the framework of point stochastic processes. The neuron receives excitatory input impulses from a Poisson stream, and inhibitory impulses from the feedback line with a delay. We investigate here, how does the presence of inhibitory feedback affect the output firing statistics. Using binding neuron (BN) as a model, we derive analytically the exact expressions for the output interspike intervals (ISI) probability density, mean output ISI and coefficient of variation as functions of model's parameters for the case of threshold 2. Using the leaky integrate-and-fire (LIF) model, as well as the BN model with higher thresholds, these statistical quantities are found numerically. In contrast to the previously studied situation of no feedback, the ISI probability densities found here both for BN and LIF neuron become bimodal and have discontinuity of jump type. Nevertheless, the presence of inhibitory delayed feedback was not found to affect substantially the output ISI coefficient of variation. The ISI coefficient of variation found ranges between 0.5 and 1. It is concluded that introduction of delayed inhibitory feedback can radically change neuronal output firing statistics. This statistics is as well distinct from what was found previously (Vidybida and Kravchuk, 2009) by a similar method for excitatory neuron with delayed feedback.     

Firing statistics of inhibitory neuron with delayed feedback. II: Non-Markovian behavior

The instantaneous state of a neural network consists of both the degree of excitation of each neuron the network is composed of and positions of impulses in communication lines between the neurons. In neurophysiological experiments, the neuronal firing moments are registered, but not the state of communication lines. But future spiking moments depend essentially on the past positions of impulses in the lines. This suggests, that the sequence of intervals between firing moments (inter-spike intervals, ISIs) in the network could be non-Markovian.     

Parametric inference of neuronal response latency in presence of a background signal

Neurons are commonly characterized by spontaneous generation of action potentials (spikes), which appear without any apparent or controlled stimulation. When a stimulus is applied, the spontaneous firing may prevail and hamper identification of the effect of the stimulus. Therefore, for any rigorous analysis of evoked neuronal activity, the presence of spontaneous firing has to be taken into account. If the background signal is ignored, however small it is compared to the response activity, and however large is the delay, estimation of the response latency will be wrong, and the error will persist even when sample size is increasing. The first question is: what is the response latency to the stimulus? Answering this question becomes even more difficult if the latency is of a complex nature, for example composed of a physically implied deterministic part and a stochastic part. This scenario is considered here, where the response time is a sum of two components; the delay and the relative latency. Parametric estimators for the time delay and the response latency are derived. These estimators are evaluated on simulated data and their properties are discussed. Finally, we show that the mean of the response latency is always satisfactorily estimated, even assuming a wrong distribution for the response latency.     

实验性神经起步点的不规则节律中的非稳定周期轨道

在大鼠坐骨神经结扎模型中,记录到放电节律以周期加方式相互转化,在确认了周期二与周期三之间的不规则节律具有混性质后,借助峰峰间期序列的回归映象方法,使用了一种测度增强算法,确认了此混沌放电中的非稳定周期轨道结构,提示出非稳定周期轨道在神经不规则放电节律的动力学之中起着重要作用。     

Nonstationarity and duration of the cardiac interval time series in assessing the functional state of operator personnel

The influence of nonstationarity in the time series of cardiac intervals on the assessment of the functional state (FS) of operator personnel was analyzed with a three-factor model of heart rhythm variability (HRV). ECG recordings were made in supine position at rest and in the sedentary position before and after important operator testing. In all three cases, the FS assessments were not influenced by nonstationarity of the input data. The effect of nonstationarity was also negligible for some particular HRV indices. Reliable assessments could be obtained from relatively short samples (256 down to 32 RR intervals) with prior norming of the factor indices for the corresponding segment length. The influence of the time series duration on the HRV indices was examined in various FSs; stable indices and proper recording conditions were determined.     

The origin of spontaneous synchronized burst in cultured neuronal networks based on multi-electrode arrays

Many neural networks in mammalian central nervous system (CNS) fire single spike and complex spike burst. In fact, the conditions for triggering burst are not well understood. In the paper multi-electrode arrays (MEA) are used to record the spontaneous electrophysiological activities of cultured rat hippocampal neuronal network for a long time. After about 3 weeks culture, a transition from single spike to burst is observed in several networks. All of these spikes fire quickly before burst begins. The firing rate during the burst is lower than that just before the burst, but differences of inter-spike intervals (ISIs) between two firing patterns are not clear. Moreover, the electrical activities on neighboring electrodes show strong synchrony during the burst activities. In a word, the generation of the burst requires that network should have a sufficient level of excitation as well as a balance of synaptic inhibition.