The use of system identification techniques in the analysis of oculomotor burst neuron spike train dynamics

The objective of system identification methods is to construct a mathematical model of a dynamical system in order to describe adequately the input-output relationship observed in that system. Over the past several decades, mathematical models have been employed frequently in the oculomotor field, and their use has contributed greatly to our understanding of how information flows through the implicated brain regions. However, the existing analyses of oculomotor neural discharges have not taken advantage of the power of optimization algorithms that have been developed for system identification purposes. In this article, we employ these techniques to specifically investigate the "burst generator" in the brainstem that drives saccadic eye movements. The discharge characteristics of a specific class of neurons, inhibitory burst neurons (IBNs) that project monosynaptically to ocular motoneurons, are examined. The discharges of IBNs are analyzed using different linear and nonlinear equations that express a neuron's firing frequency and history (i.e., the derivative of frequency), in terms of quantities that describe a saccade trajectory, such as eye position, velocity, and acceleration. The variance accounted for by each equation can be compared to choose the optimal model. The methods we present allow optimization across multiple saccade trajectories simultaneously. We are able to investigate objectively how well a specific equation predicts a neuron's discharge pattern as well as whether increasing the complexity of a model is justifiable. In addition, we demonstrate that these techniques can be used both to provide an objective estimate of a neuron's dynamic latency and to test whether a neuron's initial firing rate (expressed as an initial condition) is a function of a quantity describing a saccade trajectory (such as initial eye position).     

Single neuron local rational arithmetic revealed in phase space of input conductances

We present a phase space analysis to explore the potential of single neuron local arithmetic operations on its input conductances. This analysis was conducted first by deriving a rational function model of local spatial summation by using the equivalent circuits for steady-state membrane potentials. It is shown that developed functional phases exist in the space of input conductances, where a single neuron's local operation on input conductances can be described in terms of a set of well-defined arithmetic functions. It is further suggested that this single neuron local rational arithmetic is programmable, in the sense that the selection of these functional phases can be effectively instructed by presynaptic activities. This programmability adds the degree of freedom in a single neuron's ability to process the input information.     

Optimizing sound features for cortical neurons

The brain's cerebral cortex decomposes visual images into information about oriented edges, direction and velocity information, and color. How does the cortex decompose perceived sounds? A reverse correlation technique demonstrates that neurons in the primary auditory cortex of the awake primate have complex patterns of sound-feature selectivity that indicate sensitivity to stimulus edges in frequency or in time, stimulus transitions in frequency or intensity, and feature conjunctions. This allows the creation of classes of stimuli matched to the processing characteristics of auditory cortical neurons. Stimuli designed for a particular neuron's preferred feature pattern can drive that neuron with higher sustained firing rates than have typically been recorded with simple stimuli. These data suggest that the cortex decomposes an auditory scene into component parts using a feature-processing system reminiscent of that used for the cortical decomposition of visual images.     

Patient controlled oral analgesia with morphine

Neurotrophic factors are proteins that promote the survival and growth of neurons in the vertebrate nervous system. Although it is well known that many neurons obtain these factors from the regions to which their axons project, studies of the sites of neurotrophic factor synthesis have raised the possibility that at least some neurons may obtain these factors from other sources. Alternative sources of neurotrophic factors include cells along a neuron's axon shaft and cells or other axons terminals within the vicinity of a neuron's cell body and dendritic arbour. In addition, recent experimental studies have shown that at certain stages of development neurotrophic factor autocrine loops operate in some neurons. The evidence for and the potential physiological significance of these different modes of action of neurotrophic factors will be discussed.     

Investigation of the temperature field developed by a spinning beam in laser processing

Various laser treatments have been developed for metallic surface modification. In these processes, rapid heating of a specific area on the surface by a laser is the critical feature employed to produce a different phase or layer on the surface. The laser beam mode, such as a Gaussian, rectangular, or annular beam in a stationary or spinning state, has been found to have a very important effect in the laser processing. Many significant models have been established to estimate the temperature field developed by a laser and therefore to predict the optimum conditions in the process, but these models are mainly applicable to a stationary beam. Previous work has shown the advantages in some applications of using a spinning beam. Therefore, modeling work for a spinning beam is necessary. The present article reworked our previous model on a spinning beam mode for a continuous CO₂ laser, to calculate a two-dimensional temperature profile by using a line source and superposition of a number of Gaussian sources. An excellent agreement with experimental work for a nitrided Ti-6Al-4V alloy (IMI 318) for a situation of a small (50-μm) melt pool was achieved. A relationship was derived between the normalized laser power and specimen speed to produce a uniformly thick surface layer.     

A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells

The problem of predicting the position of a freely foraging rat based on the ensemble firing patterns of place cells recorded from the CA1 region of its hippocampus is used to develop a two-stage statistical paradigm for neural spike train decoding. In the first, or encoding stage, place cell spiking activity is modeled as an inhomogeneous Poisson process whose instantaneous rate is a function of the animal's position in space and phase of its theta rhythm. The animal's path is modeled as a Gaussian random walk. In the second, or decoding stage, a Bayesian statistical paradigm is used to derive a nonlinear recursive causal filter algorithm for predicting the position of the animal from the place cell ensemble firing patterns. The algebra of the decoding algorithm defines an explicit map of the discrete spike trains into the position prediction. The confidence regions for the position predictions quantify spike train information in terms of the most probable locations of the animal given the ensemble firing pattern. Under our inhomogeneous Poisson model position was a three to five times stronger modulator of the place cell spiking activity than theta phase in an open circular environment. For animal 1 (2) the median decoding error based on 34 (33) place cells recorded during 10 min of foraging was 8.0 (7.7) cm. Our statistical paradigm provides a reliable approach for quantifying the spatial information in the ensemble place cell firing patterns and defines a generally applicable framework for studying information encoding in neural systems.     

The prediction of case depth in laser transformation hardening

An approximate heat flow model is developed to predict the case depth in laser transformation hardening of steel surfaces. The model exploits the dimensional relationships between the process variables to give master diagrams for the hardened depth using Gaussian and uniform, rectangular sources. Critical values of dimensionless parameters are identified which predict the conditions for first hardening and the onset of surface melting. Good agreement is demonstrated with a wide range of experimental data, and comparisons are made with previous modeling methods and process diagrams.     

Reducing maternal smoking and relapse: long-term evaluation of a pediatric intervention

Simultaneous recordings of action potentials (APs) of multiple single motor units (MUs) were obtained in brachialis and biceps (caput breve) muscles during sinusoidally modulated isometric contractions of elbow flexor muscles and during sinusoidal flexion/extension movements in the elbow against a preload in the extension direction. The results show that MUs typically fire in one short burst for each sinusoidal cycle. The mean phase lead of the bursts of APs relative to a sinusoidally modulated isometric torque in the elbow joint or relative to sinusoidal movements in the elbow increases gradually with frequency. The increase of the mean phase lead during isometric contractions was very similar for all MUs and could be explained well by modeling the force production of MUs with a second-order linear low-pass system. For sinusoidal flexion/extension movements each MU reveals a specific, reproducible phase lead as a function of frequency. However, there is a large variability in phase behavior between MUs. Also, the modulation of the firing rate for sinusoidal isometric contractions versus sinusoidal movements appeared to be different for various MUs. In simultaneous recordings some MUs clearly revealed a larger firing rate in each burst for movements relative to isometric contractions, whereas other MUs revealed a smaller firing rate. This suggests that some MUs are preferentially activated during movements whereas others are preferably activated during isometric contractions. The results demonstrate task-dependent changes in the relative activation of MUs within a single muscle for sinusoidal isometric contractions and movements.     

Regulation of action-potential firing in spiny neurons of the rat neostriatum in vivo

Both silent and spontaneously firing spiny projection neurons have been described in the neostriatum, but the reason for their differences in firing activity are unknown. We compared properties of spontaneously firing and silent spiny neurons in urethan-anesthetized rats. Neurons were identified as spiny projection neurons after labeling by intracellular injection of biocytin. The threshold for action-potential firing was measured under three different conditions: 1) electrical stimulation of the contralateral cerebral cortex, 2) brief directly applied current pulses, and 3) spontaneous action-potentials occurring during spontaneous episodes of depolarization ( state). The average membrane potential and the amplitude of noiselike fluctuations of membrane potential in the state were determined by fitting a Gaussian curve to the membrane-potential distribution. All neurons in the sample exhibited spontaneous membrane potential shifts between a hyperpolarized state and a depolarized state, but not all fired action potentials while in the state. The difference between the spontaneously firing and the silent spiny neurons was in the average membrane potential in the state, which was significantly more depolarized in the spontaneously firing than in the silent spiny neurons. There were no significant differences in the threshold, the amplitude of the noiselike fluctuations of membrane potential in the state, or in the proportion of time that the membrane potential was in the state. In both spontaneously firing and silent neurons, the threshold for action potentials evoked by current pulses was significantly higher than for those evoked by cortical stimulation. Application of more intense current pulses that reproduced the excitatory postsynaptic potential rate of rise produced firing at correspondingly lower thresholds. Because the membrane potential in the state is mainly determined by the balance between the synaptic drive and the outward potassium conductances activated in the subthreshold range of membrane potentials, either or both of these factors may determine whether firing occurs in response to spontaneous afferent activity.     

Practical Bayesian inference using mixtures of mixtures

Discrete mixtures of normal distributions are widely used in modeling amplitude fluctuations of electrical potentials at synapses of human and other animal nervous systems. The usual framework has independent data values yj arising as yj = mu j + xn0 + j, where the means mu j come from some discrete prior G(mu) and the unknown xno + j's and observed xj, j = 1,...,n0, are Gaussian noise terms. A practically important development of the associated statistical methods is the issue of nonnormality of the noise terms, often the norm rather than the exception in the neurological context. We have recently developed models, based on convolutions of Dirichlet process mixtures, for such problems. Explicitly, we model the noise data values xj as arising from a Dirichlet process mixture of normals, in addition to modeling the location prior G(mu) as a Dirichlet process itself. This induces a Dirichlet mixture of mixtures of normals, whose analysis may be developed using Gibbs sampling techniques. We discuss these models and their analysis, and illustrate them in the context of neurological response analysis.     

内配碳赤铁矿球团反应动力学及其模型

研究了碳对内配固体燃料赤铁矿球团焙烧过程的影响，研究表明，球团矿内各种反应是同时进行的，各自的反应速度地是相互独立的，提出了该反应体的过程模型，试验结果及模型计算表明，在内配固体燃料赤铁矿团焙烧过程中存在赤铁矿先被还原成磁铁矿或浮士体，然后再氧化成赤铁矿的现象，为改进赤铁矿球团的焙烧工艺提供了理论基础。     

Capillary electrophoresis of insulin-like growth factors: enhanced ultraviolet detection using dynamically coated capillaries and on-line solid-phase extraction

We consider a model of an integrate-and-fire neuron with synaptic current dynamics, in which the synaptic time constant tau' is much smaller than the membrane time constant tau. We calculate analytically the firing frequency of such a neuron for inputs described by a random Gaussian process. We find that the first order correction to the frequency due to tau' is proportional to the square root of the ratio between these time constants radicaltau'/tau. This implies that the correction is important even when the synaptic time constant is small compared with that of the potential. The frequency of a neuron with tau'>0 can be reduced to that of the basic IF neuron (corresponding to tau'=1) using an "effective" threshold which has a linear dependence on radical tau'/tau. Numerical simulations show a very good agreement with the analytical result, and permit an extrapolation of the "effective" threshold to higher orders in radical tau'/tau. The obtained frequency agrees with simulation data for a wide range of parameters.     

Experimental, Numerical, and Analytical Models for a Dispersion Study

Wind tunnel tests, computational fluid dynamics simulations, and Gaussian models are employed for a dispersion study in nonuniform atmospheric boundary layer conditions with releases affected by neighboring building wake entrainment and topography. A vertical stack release and a horizontal crosswind release are subjected to both qualitative (plume trajectory and geometry) and quantitative (concentration) analysis. The results of the three modeling approaches (experiments, computational fluid dynamics simulations, and Gaussian models) are compared, their relative strengths are discussed, and conclusions are presented. It is suggested that a combined numerical (computational fluid dynamics) and physical (wind tunnel) modeling approach might benefit atmospheric dispersion studies in complex, nonuniform atmospheric environments.     

Simulation of Binary Random Fields with Applications to Two-Phase Random Media

This paper introduces a methodology for simulation of binary random fields according to their prescribed autocorrelation function. It starts with a brief outline of the essential features of binary random fields and their implications in modeling two-phase random media. The exposition of the proposed methodology is done in two steps. In the first step, an algorithm is introduced to obtain samples of a binary field from generated realizations of a Gaussian field, using the theory of zero crossings of Gaussian fields. This mapping constitutes essentially a nonlinear transformation with memory of the Gaussian sample functions. In the second step, an iterative algorithm is introduced that allows the determination of the probabilistic characteristics of the underlying Gaussian field, so that the resulting binary field obtained through the proposed nonlinear transformation has a prescribed autocorrelation function. Several numerical examples are provided to demonstrate the capabilities of the methodology, especially in modeling two-phase random media. The methodology is shown to have a wide range of applicability and its computational cost is small, especially when a large number of realizations is needed.     

A Mec1- and Rad53-dependent checkpoint controls late-firing origins of DNA replication

DNA replication in eukaryotic cells initiates from many replication origins which fire throughout the S phase of the cell cycle in a predictable pattern: some origins fire early, others late. Little is known about how the initiation of DNA replication and the elongation of newly synthesized DNA strands are coordinated during S phase. Here we show that, in budding yeast, hydroxyurea, which blocks the progression of replication forks from early-firing origins, also inhibits the firing of late origins. These late origins are maintained in the initiation-competent prereplicative state for extended periods. The block to late origin firing is an active process and is defective in yeast with mutations in the rad53 and mec1 checkpoint genes, indicating that regulation of late origin firing may also be an important component of the 'intra-S-phase' checkpoint and may aid cell survival under adverse conditions.     

Effects of localized pontine lesions on auditory brain-stem evoked potentials and binaural processing in humans

Potassium channels are involved in the control of neuronal excitability by fixing the membrane potential, shaping the action potential, and setting firing rates. Recently, attention has been focused on identifying the factors influencing excitability in second-order auditory and vestibular neurons. Located in the brainstem, second-order auditory and vestibular neurons are sites for convergence of inputs from first-order auditory or vestibular ganglionic cells with other sensory systems and also motor areas. Typically, second-order auditory neurons exhibit two distinct firing patterns in response to depolarization: tonic, with a repetitive firing of action potentials, and phasic, characterized by only one or a few action potentials. In contrast, all mature vestibular second-order neurons fire tonically on depolarization. Already, certain fundamental roles have emerged for potassium currents in these neurons. In mature auditory and vestibular neurons, I(K), the delayed rectifier, is required for the fast repolarization of action potentials. In tonically firing auditory neurons, I(A), the transient outward rectifier, defines the discharge pattern. I(DS), a delayed rectifier-like current distinguished by its low threshold of activation, is found in phasically firing auditory and some developing vestibular neurons where it limits firing to one or a few spikes, and also may contribute to forming short-duration excitatory postsynaptic potential (EPSPs). Also, I(DS) sets the threshold for action potential generation rather high, which may prevent spontaneous discharge in phasically firing cells. During development, there is a gradual acquisition and loss of some potassium conductances, suggesting developmental regulation. As there are similarities in membrane properties of second-order auditory and vestibular neurons, investigations on firing pattern and its underlying mechanisms in one system should help to uncover fundamental properties of the other.     

Martingale Approach to Monte Carlo Simulation and Linear Random Vibration

A method is developed for generating samples of the state X of a linear filter driven by Gaussian white noise. The method can also be applied to developed formulas giving the second moment properties of X. The proposed solution is based on two facts. First, the integrals defining the forcing component of X are continuous martingales because their integrands are continuous deterministic functions and their integrators are Brownian motions. Second, a continuous martingale satisfying some additional properties can be time changed to a standard Brownian motion. The proposed Monte Carlo algorithm for generating samples of X calculates samples of this process from samples of a standard Brownian motion defined in a new clock by mapping these samples to the original clock. The algorithm does not use recurrence formulas for generating samples of X and does not approximate the probability law of this process. Examples are presented to illustrate the application of the proposed method. The examples include the Ornstein-Uhlenbeck process and the response of a simple oscillator and multi-degree-of-freedom system to Gaussian white noise.     

Polynomial Chaos Decomposition for the Simulation of Non-Gaussian Nonstationary Stochastic Processes

A method is developed for representing and synthesizing random processes that have been specified by their two-point correlation function and their nonstationary marginal probability density functions. The target process is represented as a polynomial transformation of an appropriate Gaussian process. The target correlation structure is decomposed according to the Karhunen–Loève expansion of the underlying Gaussian process. A sequence of polynomial transformations in this process is then used to match the one-point marginal probability density functions. The method results in a representation of a stochastic process that is particularly well suited for implementation with the spectral stochastic finite element method as well as for general purpose simulation of realizations of these processes.     

基于贝叶斯决策模型的热轧卷筒电机故障诊断

卷筒电机是卷筒的动力来源,对钢卷卷形质量和卷取稳定性有着重要影响.针对热连轧机组卷取工序中卷筒电机的鼠笼条断裂问题,通过从现场采集卷筒电机电流信号,并经过信号截取、滤波、拟合等操作提取出信号特征,利用多元高斯模型进行建模,并基于最小风险的贝叶斯分类算法对其进行故障诊断.试验结果表明,采用最小风险贝叶斯决策模型可以较好地...     

钢铁厂高能效利用副产煤气的可行途径

钢铁厂在生产过程中,副产了大量可燃煤气。副产煤气热值范围在3～20MJ/Nm3,所含的热量可被利用,如:电厂、焦炉、热风炉、热轧厂等所需燃料,可用副产煤气部分替代天然气,减少煤气放散,降低公司的能耗成本和环境污染。使用副产煤气虽能得益却也面临难题。副产煤气的产量和成分取决于生产和工艺状况,煤气用户必须随之调整其操作。用气设备的启停操作改变了煤气用量,造成煤气管网压力波动,副产煤气成分变化则会引起热值波动,一旦管网压力波动或成分改变,将对管网上用气单元的燃烧过程产生不利影响。针对副产煤气利用的复杂情况,AMB、AMEH和BFI公司开发了高能效利用副产煤气的途径,应用于部分钢铁厂,如:改善煤气管网压力和流量分布的技术,具备供需分布显示和预报功能的煤气管理系统。介绍了煤气管网压力波动控制以及副产煤气利用的结果和经验。