Multisite recording of extracellular potentials produced by microchannel-confined neurons in-vitro

Towards establishing electrical interfaces with patterned in vitro neurons, we have previously described the fabrication of hybrid elastomer-glass devices polymer-on-multielectrode array technology and obtained single-electrode recordings of extracellular potentials from confined neurons (Claverol-Tinturé et al., 2005). Here, we demonstrate the feasibility of spatially localized multisite recordings from individual microchannel-guided neurites extending from microwell-confined somas with good signal-to-noise ratios (20 dB) and spike magnitudes of up to 300 microV. Single-cell current source density (scCSD) analysis of the spatio-temporal patterns of membrane currents along individual processes is illustrated.     

Tracking spike-amplitude changes to improve the quality of multineuronal data analysis

During extracellular electrophysiological recording experiments, the waveform of neuronal spikes recorded from a single neuron often changes. These spike-waveform changes make single-neuron identification difficult, particularly when the activities of multiple neurons are simultaneously recorded with a multichannel microelectrode, such as a tetrode or a heptode. We have developed a tracking method of individual neurons despite their changing spike amplitudes. The method is based on a bottom-up hierarchical clustering algorithm that tracks each neuron's spike cluster during temporally overlapping clustering periods. We evaluated this method by comparing spike sorting with and without cluster tracking of an identical series of multineuronal spikes recorded from monkey area-TE neurons responding to a set of visual stimuli. According to Shannon's information theory, errors in spike-amplitude tracking reduce the expected value of the amount of information about a stimulus set that is transferred by the spike train of a cluster. In this study, cluster tracking significantly increased the expected value of the amount of information transferred by a spike train (p < 0.01). Additionally, the stability of the stimulus preference and that of the cross-correlation between clusters improved significantly (p < 0.000001). We conclude that cluster tracking improves the quality of multineuronal data analysis.     

Multineuronal spike classification based on multisite electrode recording, whole-waveform analysis, and hierarchical clustering

We proposed here a method of multineuronal spike classification based on multisite electrode recording, whole-waveform analysis, and hierarchical clustering for studying correlated activities of adjacent neurons in nervous systems. Multineuronal spikes were recorded with a multisite electrode placed in the hippocampal pyramidal cell layer of anesthetized rats. If the impedance of each electrode site is relatively low and the distance between electrode sites is sufficiently small, a spike generated by a neuron is simultaneously recorded at multielectrode sites with different amplitudes. The covariance between the spike waveform at each electrode site and a template was calculated as a damping factor due to the volume conduction of the spike from the neuron to the electrode site. Calculated damping factors were vectorized and analyzed by hierarchical clustering using a multidimensional statistical test. Since a cluster of damping vectors was shown to correspond to an antidromically identified neuron, spikes of different neurons are classified by referring to the distributions of damping vectors. Errors in damping vector calculation due to partially overlapping spikes were minimized by successively subtracting preceding spikes from raw data. Clustering errors due to complex spike bursts (i.e., spikes with variable amplitudes) were avoided by detecting such bursts and then using only the first spike of a burst for clustering. These special procedures produced better cluster separation than conventional methods, and enabled multiple neuronal spikes to be classified automatically. Waveforms of classified spikes were well superimposed. We concluded that this method is particularly useful for separating the activities of adjacent neurons that fire partially overlapping spikes and/or complex spike bursts.     

An Improved Time-Amplitude Window Discriminator

Usually extracellular microelectrodes record the activity from several neurons. In order to quantify the activity of an individual neuron in the record some form of spike discrimination is required. This paper describes a window discriminator for the separation of multi-unit neuronal spike trains which will display simultaneously the recorded spike information and the time-amplitude window levels. The window levels are displayed and can be delayed to include any portion of the spike waveform either above or below the initial threshold level. Several instruments can be cascaded for multiple time-amplitude interrogations of the same spike class from a single electrode recording. The instrument utilizes inexpensive commercially available integrated circuits.     

Method for unsupervised classification of multiunit neural signal recording under low signal-to-noise ratio

Neural spike sorting is an indispensable step in the analysis of multiunit extracellular neural signal recording. The applicability of spike sorting systems has been limited, mainly to the recording of sufficiently high signal-to-noise ratios, or to the cases where supervised classification can be utilized. We present a novel unsupervised method that shows satisfactory performance even under high background noise. The system consists of an efficient spike detector, a feature extractor that utilizes projection pursuit based on negentropy maximization (Huber, 1985 and Hyvarinen et al, 1999), and an unsupervised classifier based on probability density modeling using mixture of Gaussians (Jain et al., 2000). Our classifier is based on the mixture model with a roughly approximated number of Gaussians and subsequent mode-seeking. It does not require accurate estimation of the number of units present in the recording and, thus, is better suited for use in fully automated systems. The feature extraction stage leads to better performance than those utilizing principal component analysis and two nonlinear mappings for the recordings from the somatosensory cortex of rat and the abdominal ganglion of Aplysia. The classification method yielded correct classification ratio as high as 95%, for data where it was only 66% when a kappa-means-type algorithm was used for the classification stage.     

Detection, classification, and superposition resolution of actionpotentials in multiunit single-channel recordings by an on-linereal-time neural network

Determination of single-unit spike trains from multiunit recordings obtained during extracellular recording has been the focus of many studies over the last two decades. In multiunit recordings, superpositions can occur with high frequency if the firing rates of the neurons are high or correlated, making superposition resolution imperative for accurate spike train determination. In this work, a connectionist neural network (NN) was applied to the spike sorting challenge. A novel training scheme was developed which enabled the NN to resolve some superpositions using single-channel recordings. Simulated multiunit spike trains were constructed from templates and noise segments that were extracted from real extracellular recordings. The simulations were used to determine the performances of the NN and a simple matched template filter (MTF), which was used as a basis for comparison. The network performed as well as the MTF in identifying nonoverlapping spikes, and was significantly better in resolving superpositions and rejecting noise. An on-line, real-time implementation of the NN discriminator, using a high-speed digital signal processor mounted inside an IBM-PC, is now in use in six laboratories     

Comments on `New monolithic switched-capacitor differentiators withgood noise rejection'

For the original article see ibid., vol.24, no.1, p.177-80 (1989). The commenter notes that the basic inverting SC differentiator described by C.-Y. Wu et al. was proposed by B.J. Hosticka and G. Moschytz (1979), and the behavior of the earlier circuit is discussed. The commenter also points out that the operational amplifiers in the SC filter of Wu et al. must slew in each φ₁ phase from the signal value to ground, thus limiting the maximum usable clock frequency     

448-Detector optical recording system: development and applicationto Aplysia gill-withdrawal reflex

The spatial resolution of a fast, multichannel, optical recording apparatus has been improved in an attempt to increase the completeness of optical recordings of neuron activity responsible for the Aplysia gill-withdrawal reflex. We developed a new optical apparatus, increasing the number of pixels to 448. Action potential activity from 168 to 192 neurons could be detected. Hence, the number of optically detected neurons increased about 2.5 times in comparison with a previously used 100- or 124-detector optical apparatus. Because optically detected action potential signals were shown to be due to the cell body of a neuron, it was possible to know its position as well as its approximate size. The distribution of the detected active neurons and those neurons that showed an apparent change in spike frequency to the siphon stimulation were determined.     

Adaptive array detection of uncertain rank one waveforms

Adaptive array detection of known (within a complex scaling) rank one space time waveforms in unknown spatial noise has received considerable attention. The two published solutions are the adaptive matched filter, and the GLRT. We expand on this work to consider the case of rank one waveforms that are uncertain, i.e., only partially known. More precisely, we model the space time steering vector as the Kronecker product of two vectors, each of which is unknown but is known to lie in a known subspace. Applications for such a model include detection in the presence of multipath and spectral or polarization diversity in both radar processing and wireless communication. Using the principle of invariance, we construct detectors based on the maximal invariant. We show that the SNR required to achieve a given detection probability (for a given false alarm rate) is only weakly impacted by waveform uncertainty. Thus, our detector approaches the performance of earlier detectors, which entail known waveforms     

Glottal source estimation using a sum-of-exponentials model

An algorithm for estimating the glottal source waveform in voiced speech is described. The glottal source waveform is described using the LF model proposed by Fant et al. (1985). The vocal tract filter is modeled as a pole-zero system. The analysis of vowel sounds from several talkers shows that the analysis procedure leads to an accurate estimate of the glottal source     

Modeling lidar returns from forest canopies

Remote sensing techniques that utilize light detection and ranging (lidar) provide unique data on canopy geometry and subcanopy topography. This type of information will lead to improved understanding of important structures and processes of Earth's vegetation cover. To understand the relation between canopy structure and the lidar return waveform, a three-dimensional (3D) model was developed and implemented. Detailed field measurements and forest growth model simulations of forest stands were used to parameterize this vegetation lidar waveform model. In the model, the crown shape of trees determines the vertical distribution of plant material and the corresponding lidar waveforms. Preliminary comparisons of averaged waveforms from an airborne lidar and model simulations shows that the shape of the measured waveform was more similar to simulations using an ellipsoid or hemi-ellipsoid shape. The observed slower decay of the airborne lidar waveforms than the simulated waveforms may indicate the existence of the understories and may also suggest that higher order scattering from the upper canopy may contribute to the lidar signals. The lidar waveforms from stands simulated from a forest growth model show the dependence of the waveform on stand structure.     

Setting adaptive spike detection threshold for smoothed TEO based on robust statistics theory

We propose a novel approach aimed at adaptively setting the threshold of the smoothed Teager energy operator (STEO) detector to be used in extracellular recording of neural signals. In this proposed approach, to set the adaptive threshold of the STEO detector, we derive the relationship between the low-order statistics of its input signal and the ones of its output signal. This relationship is determined with only the background noise component assumed to be present at the input. Robust statistics theory techniques were used to achieve an unbiased estimation of these low-order statistics of the background noise component directly from the neural input signal. In this paper, the emphasis is made on extracellular neural recordings. However, the proposed method can be used in the analysis of different biomedical signals where spikes are important for diagnostic (e.g., ECG, EEG, etc.). We validated the efficacy of the proposed method using synthetic neural signals constructed from real neural recordings signals. Four different sets of extracellular recordings from four distinct neural sources have been exploited to that purpose. The first dataset is recorded from an adult male monkey using the Utath 10×10 microelectrode array implemented in the prefrontal cortex, the second one was obtained from the visual cortex of a rat using a stainless-steel-tipped microelectrode, the third dataset came from recording in a human medial lobe using intracranial electrode, and finally, the fourth one was extracted from recordings in a macaque parietal cortex using a single tetrode. Simulation results show that our approach is effective and robust, and outperforms state-of-the-art adaptive detection methods in its category (i.e., efficient and simple, and do not require a priori knowledge about neural spike waveforms shapes).     

主动声纳的混沌波形设计和解调方法

该文为解决主动声纳隐蔽性差的问题,提出了一种新的波形设计方法:将特定编码调制到混沌信号中,作为主动声纳的发射信号。文中全面考虑了水声信道滤波和加性噪声对回波的影响,采用自适应滤波器解调回波,通过识别编码检测回波信号。提出了改进的相平面Lyapunov自适应滤波器作为解调算法。仿真结果表明:该解调算法在加性白噪声和滤波情况均有较好的解调效果,并能满足实时性的要求。与现有的主动声纳发射信号相比,提高了主动声纳的隐蔽性。     

光电编码器两种精码波形细分方法原理误差对比

光电编码器通常利用细分两路正交的码盘精码信号达到高分辨力的目的,为使细分技术更加完善,对基于三角波和基于正余弦波的两种细分方法进行了专题研究。分别对理想信号中存在直流误差、幅值误差、基波相位误差、高次谐波误差几种典型误差情况进行了分析,比较两种基于不同波形细分方法的抗干扰能力。实验对精码信号介于正余弦波和三角波之间的编码器进行测试,对于同一台编码器,采用正余弦波细分时精度为36,采用三角波细分时精度为42。结果表明:基于正余弦波的细分方法抗干扰能力优于基于三角波的细分方法。对于高精度光电编码器研制和生产时,可利用正余弦波对精码信号进行细分或将实际信号校正至标准正余弦波再细分。     

基于FPGA的数字频率合成器设计与实现

为了产生稳定激励信号的目的,采用Verilog硬件语言在FPGA上实现了数字频率合成器的设计,该设计包括累加器、波形存储器、AD转换、低通滤波器等;对累加器、波形存储器都进行了仿真,并下载到FPGA中,经A/D转换,滤波,获得了稳定的正弦激励信号。本设计只实现了正弦信号设计,通过对波形存储器数据改变,可以实现任意波形的输出。     

An Analog Delay Circuit for On-Line Visual Confirmation of Discriminated Neuroelectric Signals

An analog delay circuit which utilizes an inexpensive commercially available analog shift register is described. The delay circuit when used in conjunction with a window discriminator will display "on-line" visual confirmation of single neural events from extracellular recordings containing more than one spike class. The delay can be changed to include the entire spike waveform by adjusting a potentiometer in the delay circuit.     

Single site electromyograph amplitude estimation

Previous investigators have experimentally demonstrated and/or analytically predicted that temporal whitening of the surface electromyograph (EMG) waveform prior to demodulation improves the EMG amplitude estimate. However, no systematic study of the influence of various whitening filters upon amplitude estimate performance has been reported. The authors describe a phenomenological mathematical model of a single site of the surface EMG waveform and reports on experimental studies which examined the performance of several temporal whitening filters. Surface EMG waveforms were sampled during nonfatiguing, constant-force, isometric contractions of the biceps or triceps muscles, over the range of 10-75% maximum voluntary contraction. A signal-to-noise ratio (SNR) was computed from each amplitude estimate (deviations about the mean value of the estimate were considered as noise). A moving average root mean square estimator (245 ms window) provided an average±standard deviation (A±SD) SNR of 10.7±3.3 for the individual recordings. Temporal whitening with one fourth-order whitening filter designed per site improved the A±SD SNR to 17.6±6.0     

A Learning Filter for Removing Noise Interference

We have developed a microcomputer-based filter that removes line induced electrical interference from biopotential signals by learning one period of the noise waveform and subtracting it from the signal. Since it uses a noise template, the filter can remove noise waveforms containing several harmonics of the 60 Hz line frequency.     

Chip waveform design for DS/SSMA systems with aperiodic randomspreading sequences

The dependence of the error performance and spectral efficiency of direct-sequence spread-spectrum multiple-access (DS/SSMA) systems with matched filter receivers on the chip waveform is examined. The actual shape of the chip waveform, as well as its energy, is found to influence the statistical properties of the multiple-access interference (MAI). An approach to design waveforms that may result in interchip interference (ICI) is proposed and a criterion for design based on the conditional Gaussian approximation of the MAI for systems with aperiodic random spreading sequences is derived. For a simplified system, a closed-form solution for optimal band-limited waveforms is obtained for excess bandwidth less than or equal to one by using a performance metric that includes the effect of ICI. Numerical results, based on an analytical method, as well as Monte Carlo simulations, are provided to evaluate the performance of the proposed waveforms in general systems with conventional matched filter receivers     

高分七号激光测高中全波形回波数据的EMD降噪

针对具有多个高度层的复杂场景,全波形激光测高系统记录的回波信号中往往带有较高的噪声,采用合适的降噪方法将有助于提高计算激光测距的精确性、反演地物垂直结构和构建目标特征参数的准确性。根据高分七号激光测高在轨探测的低信噪比全波形数据的特性,采用经验模态分解（Empirical mode decomposition,EMD）方法来构建典型的本征模函数（Intrinsic mode function, IMF）,对于分解出多个不同尺度IMF的筛选,比较了使用去除高频分量,阈值选取、Wavelet选取和去趋势波动分析（Detrended fluctuation analysis, DFA）等方法与策略,通过降噪效果及定量评价,测试结果表明EMD-DFA1与EMD-1IMF对高分七号激光测高的全波形数据具有较好的降噪效果,其次为EMD-Wavelet和EMD-Threshold。另外通过EMD-DFA1对单个波峰、混叠波峰、多个波峰等不同情况的全波形数据测试,结果表明该方法具有较好的自适应性。