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PubMed1996

Variability of extracellular spike waveforms of cortical neurons.

1. Here we study the variability in extracellular records of action potentials. Our work is motivated, in part, by the need to construct effective algorithms to classify single-unit waveforms from multiunit recordings. 2. We used microwire electrode pairs (stereotrodes) to record from primary somatosensory cortex of awake, behaving rat. Our data consist of continuous records of extracellular activity and segmented records of extracellular spikes. Spectral and principal component techniques are used to analyze mean single-unit wave-forms, the variability between different instances of a single-unit waveform, and the underlying background activity. 3. The spectrum of the variability between different instances of a single-unit waveforms is not white, and falls off above 1 kHz with a frequency dependence of roughly f-2. This spectrum is different from that of the mean spike waveforms, which falls off roughly as f-4, but is essentially identical with the spectrum of background activity. The spatial coherence of the variability on the 10-micron scale also falls off at high frequencies. 4. The variability between different instances of a single-unit waveform is dominated by a relatively small number of principal components. As a consequence, there is a large anisotropy in the cluster of the spike waveforms. 5. The background noise cannot be represented as a stationary Gaussian random process. In particular, we observed that the spectrum changes significantly between successive 20-ms intervals. Furthermore, the total power in the background activity exhibits larger fluctuations than is consistent with a stationary Gaussian random process. 6. Roughly half of the single-unit spike waveforms exhibit systematic changes as a function of the interspike interval. Although this results in a non-Gaussian distribution in the space of waveforms, the distribution can be modeled by a scalar function of the interspike interval. 7. We use a set of 44 mean single-unit waveforms to define the space of differences between spike waveforms. This characterization, together with that of the background activity, is used to construct a filter that optimizes the detection of differences between single-unit waveforms. Further, an information theoretic measure is defined that characterizes the detectability.

Action Potentials↗
PubMed1989

Applications of the expectation-maximization algorithm to quantal analysis of postsynaptic potentials.

The expectation-maximization (EM) algorithm is a robust method for maximum likelihood estimation of the parameters of an incompletely sampled distribution. It has been used to resolve the trial-to-trial amplitude fluctuations of postsynaptic potentials, when these are recorded in the presence of noise. Its use has however been limited by the need for different recursion equations for each set of conditions defined by the signal and noise processes. These equations are derived for the following conditions which arise in studies of synaptic transmission: non-gaussian noise process; quantal fluctuation; quantal variability. In addition, a constraint can be incorporated to accommodate simple and compound binomial models of transmitter release. Some advantages of these methods are illustrated by Monte Carlo simulations.

Algorithms↗
PubMed1993

Characterization of the dynamic stress response of manual and powered wheelchair frames.

Two wheelchairs, one manual, one electrically powered, were instrumented with strain gages and operated over various laboratory terrains. Both wheelchairs were folding models with cross tubes pinned together at the center. The wheelchairs were operated on a constant speed treadmill with no bump, and with 0.953 cm (0.375 in) and 1.6 cm (0.625 in) dowels simulating bumps. The wheelchairs were also rolled off a 10.8 cm (4.25 in) platform to simulate a curb drop. The von Mises stresses were computed from the recorded strains, and statistical hypothesis tests were performed to determine whether the stresses were consistent with a stationary, narrow-band Gaussian random process. Such a stress history has been used in random fatigue analyses. Summary data for two strain gage locations on each wheelchair, for the four different test terrains, suggest that the von Mises stress can be considered stationary, but neither narrow-banded, nor Gaussian distributed.

Humans↗
PubMed1997

Non-Gaussian statistical property of the ultrasonic Doppler signal downstream of a severe stenosis.

The Doppler signal is generally considered as a Gaussian random process. However, to date very little experimental validation of this model has been available. Recently, the K model was proposed to describe the statistical properties of ultrasonic radio frequency signals and their envelopes. The coefficient of kurtosis, which has been shown to be related to the parameters of the K model, was used in the literature as an index to assess the deviations from the Gaussian statistical property of ultrasonic signals obtained from simulations, phantoms and tissues. In the present study, an in vitro flow loop model was used to evaluate the effect of a severe stenosis on the coefficient of kurtosis. Our results showed non-Gaussian statistical properties of the Doppler signal in the jet of the stenosis.

Constriction, Pathologic↗
PubMedDate not supplied

The effect of digital unsharp-mask filtering on the signal-to-noise ratio in computed radiography.

The effect of image processing in computed radiography (CR) has been analyzed in many ROC studies. The results have not shown great diagnostic improvements, except in some special occasions. The theoretical effect of image enhancement on the signal-to-noise ratio in CR images has so far not been assessed. Concerning the previous results, the changes induced in the signal-to-noise ratio by digital image processing are certainly of interest. We calculated the signal-to-noise ratio in various conditions according to the principles of the Rose model, using the computerized image data of storage phosphor radiography. Seventy-seven computed radiographs processed by Gaussian unsharp-mask filtering using different kernel widths were analyzed. The signal-to-noise ratio was reduced in all images by more than 40% when the smallest kernels were used, and increased slowly towards the original value with greater kernel sizes. In no conditions did the ratio exceed the original one. The results show that although edges and signal contrast can be enhanced by unsharp-mask filtering, this happens at the cost of increased noise. This might at least in part explain why image processing does not significantly improve the diagnostic information content of a computed radiograph.

Filtration↗
PubMed1998

Spin Echo Attenuation of Restricted Diffusion as a Discord of Spin Phase Structure

By using the particle probability density we analyze the spin echo attenuation of particles, diffusing in a bounded region. It provides a means to expand a nonuniform spin phase distribution into a series of waves that characterize the geometry and boundary conditions of confinement. Random motion disrupts the initial phase structure created by applied gradients and consequently discords its structure waves. By assuming the spin phase fluctuation and/or the randomness of spin phase distribution in the subensemble as a Gaussian stochastic process, we derive a new analytical expression for the echo attenuation related to the particle velocity correlation. For a diffusion in porous structure we get the expression featuring the same "diffusive diffraction" patterns as those being found and explained by P. T. Callaghan and A. Coy ("Principles of Nuclear Magnetic Resonance Microscopy," Oxford Univ. Press, Oxford (1991); J. Chem. Phys. 101, 4599-4609 (1994)) with the use of propagator theory. With the new approach we cast a new light on the phenomena and derive analitically how the diffusive diffractions appear when the sequence of finite or even modulated gradients are applied. The method takes into account the non-Markovian character of restricted diffusion, and therefore the echo dependence on the diffusion lengths and on the strength of applied gradient differs from the results of authors assuming the Markovian diffusion either by dealing with the diffusion propagators or by the computer simulation of Fick's diffusion. Copyright 1998 Academic Press.

Journal Article↗
PubMed1979

The response of a spatially distributed neuron to white noise current injection.

The depolarization of passive nerve cylinder or dendritic tree in the equivalent cylinder representation is assumed to satisfy the cable equation. We consider in detail the effects of white noise current injection at a given location for the case of sealed end boundary conditions and for an initial resting state. The depolarization at a point is a Gaussian random process but is not Markovian. Expression (infinite series) are obtained for the expectation, variance, spatial and temporal covariances of the depolarization. We examine the steady state expectation and variance and investigate how these are approached in time over the whole neuronal surface. We consider the relative contributions of various terms in the series for the expectation and variance of the depolarization at x = 0 (soma, trigger zone, recording electrode) for various positions of the input process. It is found that different numbers of terms must be taken to obtain a reasonable approximation depending on whether the stimulus is at proximal, central or distal parts of the dendritic tree. We consider briefly the interspike time problem and see in an approximate way how spatial effects are important in determining the mean time between impulses.

Action Potentials↗
PubMed1985

A maximum entropy criterion of filtering and coding for stationary autoregressive signals: its physical interpretations and suggestions for its application to neural information transmission.

The operations of encoding and decoding in communication agree with filtering operations of convolution and deconvolution for Gaussian signal processing. In an analogy with power transmission in thermodynamics, an autoregressive model of information transmission is proposed for representing a continuous communication system which requires a pair of an internal noise source and a signal source to encode or decode a message. In this model transinformation (informational entropy) equals the increase in stationary nonequilibrium organization formed through the amplification of white noise by a positive feedback system. The channel capacity is finite due to the existence of inherent noise in the system. The maximum entropy criterion in information dynamics corresponds to the 2nd law of thermodynamics. If the process is stationary, the communication system is invertible, and has the maximum efficiency of transformation. The total variation in informational entropy is zero in the cycle of the invertible system, while in the noninvertible system the entropy of decoding is less than that of encoding. A noisy autoregressive coding which maximizes transinformation is optimum, but is also ideal.

Animals↗
PubMed1993

Cardiac Doppler blood-flow signal analysis. Part 1. Evaluation of the normality and stationarity of the temporal signal.

The normality (Gaussian property) and stationarity of the cardiac Doppler blood-flow signal were evaluated on short-time segments distributed over the cardiac cycle. The basic approaches used to perform statistical tests on the nonstationary and quasiperiodic cardiac Doppler signal are presented. The results obtained from the data of ten patients having a normal aortic valve and ten patients having a stenotic valve indicate that a complex Gaussian random process is an acceptable approximation for the clinical cardiac Doppler signal. For segments of 10 ms or less, 82 per cent of them were accepted to be stationary with a significance level of 0.05, whereas for durations greater than 40 ms, the percentage of stationary segments was less than 75 per cent. It was concluded that the 10 ms window generally used in practice is a good choice for Doppler spectrogram estimation, but a shorter time interval would be preferable.

Aortic Valve↗
PubMedDate not supplied

Controlling behavioral experiments with a new programming language (SORCA) for microcomputer systems.

A new programming language SORCA has been defined and a compiler has been written for Z80-based microcomputer systems with CP/M operating system. The language was developed to control behavioral experiments by external stimuli and by time schedule in real-time. Eight binary hardware input lines are sampled cyclically by the computer and can be used to sense switches, level detectors and other binary information, while 8 binary hardware output lines, that are cyclically updated, can be used to control relays, lamps, generate tones or for other purposes. The typical reaction time (cycle time) of a SORCA-program is 500 microseconds to 1 ms. All functions can be programmed as often as necessary. Included are the basic logic functions, counters, timers, majority gates and other complex functions. Parameters can be given as constants or as a result of a step function or of a random process (with Gaussian or equal distribution). Several tasks can be performed simultaneously. In addition, results of an experiment (e.g., number of reactions or latencies) can be measured and printed out on request or automatically. The language is easy to learn and can also be used for many other control purposes.

Animals↗
PubMed1991

Assessing the variability of stochastic epidemics.

In predicting the course of individual realizations of an epidemic it is important to know the magnitude of the variability of such realizations about their mean. In this paper and in the context of the general stochastic epidemic, some methods of obtaining approximate estimates of this variability are investigated; one is a multivariate normal approximation based on an asymptotic Gaussian diffusion process, and another uses an approximating linear stochastic process. The extension of these methods to the more detailed models used to describe the transmission dynamics of HIV infection and AIDS is discussed.

Disease Outbreaks↗
PubMed1991

Synaptic noise and the cross-correlation between motoneuron discharges and stimuli.

Theoretical analysis of motoneuron models predicts that the cross-correlogram between motoneuron discharges and stimuli should reflect the derivative of the post-synaptic potential (PSP) in the absence of synaptic noise. The cross-correlogram peaks become wider if the amplitude of the noise is significant. To deal with this case, the linear combination of the PSP and its first derivative has been proposed as an approximation. The synaptic noise was regarded as a Gaussian stochastic process (its diffusion approximation), and the estimation of the cross-correlation function for this case is presented. The results were found to be in agreement with reported experimental data. A comparison was made with the linear combination approximation and with the reported computer simulation.

Animals↗
PubMed2025

Inference and visualization of complex genotype-phenotype maps with gpmap-tools.

Understanding how biological sequences give rise to observable traits, that is, how genotype maps to phenotype, is a central goal in biology. Yet our knowledge of genotype-phenotype maps in natural systems is limited due to the high dimensionality of sequence space and the context-dependent effects of mutations. The emergence of Multiplex assays of variant effect (MAVEs), along with large collections of natural sequences, offer new opportunities to empirically characterize these maps at an unprecedented scale. However, tools for statistical and exploratory analysis of these high-dimensional data are still needed. To address this gap, we developed gpmap-tools (https://github.com/cmarti/gpmap-tools), a python library that integrates a series of models for inference, phenotypic imputation, and error estimation from MAVE data or collections of natural sequences in the presence of genetic interactions of every possible order. gpmap-tools also provides methods for summarizing patterns of epistasis and visualization of genotype-phenotype maps containing up to millions of genotypes. To demonstrate its utility, we used gpmap-tools to infer genotype-phenotype maps containing 262,144 variants of the Shine-Dalgarno sequence from both genomic 5'UTR sequences and experimental MAVE data. Visualization of the inferred landscapes consistently revealed high-fitness ridges that link core motifs at different distances from the start codon. In summary, gpmap-tools provides a flexible, interpretable framework for studying complex genotype-phenotype maps, opening new avenues for understanding the architecture of genetic interactions and their evolutionary consequences.

Gaussian process↗
PubMed1992

A unified approach to modeling the backscattered Doppler ultrasound from blood.

A unified approach to modeling the backscattered Doppler ultrasound signal from blood is presented. The approach consists of summing the contributions from elemental acoustic voxels each containing many red blood cells (RBC's). For an insonified region that is large compared to a wavelength, it is shown that the Doppler signal is a Gaussian random process that arises from fluctuation scattering, which implies that the backscattered power is proportional to the variance of local RBC concentrations. As a result, some common misconceptions about the relationship between the backscattering coefficient and hematocrit can be readily resolved. The unified approach was also used to derive a Doppler signal simulation model which shows that, regardless of flow condition, the power in the Doppler frequency spectrum is governed by the exponential distribution. For finite beamwidth and paraxial flow, it is further shown that the digitized Doppler signal can be modeled by a moving average random process whose order is determined by the signal sampling rate as well as the flow velocity profile.

Biophysical Phenomena↗
PubMed1991

Nonlinear receiver compression effects on the amplitude distribution of backscattered ultrasonic signals.

Nonlinear receiver compression effects on the amplitude distribution of backscattered ultrasonic signals are investigated by using digitized RF signals that have been compressed in a commercially made ultrasonic B-scan imaging instrument. Amplitude distributions of compressed RF and video signals were obtained from regions of B-scan images that correspond to approximately the same physical region in a random medium model with known backscatter amplitude characteristics. The amplitude distribution of the signal before compression was obtained by using a table constructed from measurements of the imaging instrument compression characteristics as a function of time gain compensation. While the results indicate the general form of the decompressed data agrees with single parameter model curves that are predicted by a widely employed Gaussian random process model, the signal-to-noise ratios of the decompressed envelope vary up to 20% from the 1.91 value predicted that model. This implies that effects such as nonlinearities, envelope smoothing, and noise which all may be present in varying degrees in practical ultrasonic imaging instrumentation can cause appreciable departures from theoretical data even under highly controlled conditions.

Data Collection↗