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At least 1,045 records · Page 58Linked to original sources
PubMed2000

Spatio-temporal analyses of stimulus-evoked and spontaneous stochastic neural activity observed by optical imaging in guinea pig auditory cortex.

Stimulus-evoked response in the cortex involves random neural activity besides the deterministic responses reproducible to the stimulus. Recently, we have developed a new bright optical system that enables us to investigate the spatio-temporal patterns of such stochastic activity in the guinea pig auditory cortex without averaging. We show that (1) the stochastic neural activity is evoked by a tone-stimulus in addition to the deterministic response, and spontaneous stochastic activity is also observed in a similar manner; (2) our statistical estimation of optical responses such as variance showed that the evoked stochastic activity was increased by the sound stimulus compared to the spontaneous activity; (3) both types of stochastic activity mainly display oscillatory behavior, in the frequency range of 5-11 Hz; (4) there are no significant differences between the stimulus-induced and spontaneous stochastic neural activity in our statistical analyses using the PSD (power-spectrum density) and the spatial correlation function; (5) the spatial area of the evoked stochastic activity is not strongly correlated with the tonotopical area of the deterministic response that is mainly localized in the caudal area of field A of the guinea pig auditory cortex. Thus, the stochastic neural activity existing in the stimulus response and the spontaneous activity in the auditory cortex are possibly generated by a common neural mechanism. These results were confirmed statistically using 27 animals.

Acoustic Stimulation↗
PubMed2004

Curve-fitting overlapped bands: quantification and improvement of curve-fitting robustness in the presence of errors in the model and in the data.

Estimation of the band parameters of overlapped bands often relies on curve-fitting. It has been demonstrated that curve-fitting provides the maximum likelihood estimation of band parameters under a series of assumptions. One of these assumptions is that the curve-fitting model is correct and any error in the data is random. Under real conditions, we have to acknowledge the unavoidable presence of errors in the model and systematic errors in the data. Here, we derive an expression for the estimation of how these errors affect the quality of the parameters obtained from curve-fitting. In addition, we derive theoretical expressions to quantify the extent to which different methods can improve the curve-fitting robustness to these errors. The methods considered are: (i) deterministic and (ii) probabilistic constraints in the band parameters, (iii) curve-fitting band-narrowed data, and (iv) building a more accurate model. The theoretical expressions obtained are tested in the curve-fitting of a synthetic noisy spectrum with either baseline or band shape errors, and in the curve-fitting of the experimental infrared amide I band of the membrane protein bacteriorhodopsin.

Animals↗
PubMed2004

A novel method for determining the nature of time series.

The delay vector variance (DVV) method, which analyzes the nature of a time series with respect to the prevalence of deterministic or stochastic components, is introduced. Due to the standardization within the DVV method, it is possible both to statistically test for the presence of nonlinearities in a time series, and to visually inspect the results in a DVV scatter diagram. This approach is convenient for interpretation as it conveys information about the linear or nonlinear nature, as well as about the prevalence of deterministic or stochastic components in the time series, thus unifying the existing approaches which deal either with only deterministic versus stochastic, or the linear versus nonlinear aspect. The results on biomedical time series, namely heart rate variability (HRV) and functional Magnetic Resonance Imaging (fMRI) time series, illustrate the applicability of the proposed DVV-method.

Algorithms↗
PubMed2003

Probabilistic assessment of contamination using the two-phase flow model.

A physically motivated model is indispensable for a successful analysis of the impact of leaching from nuclear waste storage sites on the environment and public health. While most analyses use the single-phase flow model for modelling unsaturated flow and solute transport, the two-phase flow model considering the resistance of gas to water flow is a more realistic one. The effect of the two-phase flow model on the water content is theoretically investigated first in this study. Then, by combining a geostatistical generator using the turning bands method and a multi-phase transport code TOUGH2, an automatic process is used for Monte Carlo simulation of the solute transport. This stochastic approach is applied to a potentially polluted site by low-level nuclear waste in Taiwan. In the simulation, the saturated hydraulic conductivity is treated as the random variable. The stochastic approach provides a probabilistic assessment of contamination. The results show that even though water content from the two-phase flow model is only 1.5% less than the one from the single-phase flow model, the two-phase flow causes a slower movement but a wider lateral spreading of the plume in the unsaturated zone. The stochastic approach provides useful probability information which is not available from the deterministic approach. The probability assessment of groundwater contamination provides the basis for more informed waste management, better environmental assessment and improved evaluation of impact on public health.

Forecasting↗
PubMed2004

Inclusive fitness for traits affecting metapopulation demography.

Defining computable analytical measures of the effects of selection in populations with demographic and environmental stochasticity is a long-standing problem. We derive an analytical measure which takes in account all consequences of the discrete nature of deme size. Expressions of this measure are detailed for infinite island models of population structure. As an illustration we consider the evolution of dispersal in populations made of small demes with environmental and demographic stochasticity. We confirm some results obtained from the analysis of models based on deterministic approximations. In particular, when there is an Allee effect, we show that evolution of the dispersal rate may lead the metapopulation to extinction. Thus, selection on the dispersal rate could restrict the distribution of species subject to Allee effects. This selection-driven extinction is prevented by kin selection when the environmental extinction rate is small.

Alleles↗
PubMed2002

Characterisation of congenital nystagmus waveforms in terms of periodic orbits.

Because the oscillatory eye movements of congenital nystagmus vary from cycle to cycle, there is no clear relationship between the waveform produced and the underlying abnormality of the ocular motor system. We consider the durations of successive cycles of nystagmus which could be (1) completely determined by the lengths of the previous cycles, (2) completely independent of the lengths of the previous cycles or (3) a mixture of the two. The behaviour of a deterministic system can be characterised in terms of a collection of (unstable) oscillations, referred to as periodic orbits, which make up the system. By using a recently developed technique for identifying periodic orbits in noisy data, we find evidence for periodic orbits in nystagmus waveforms, eliminating the possibility that each cycle is independent of the previous cycles. The technique also enables us to identify the waveforms which correspond to the deterministic behaviour of the ocular motor system. These waveforms pose a challenge to our understanding of the ocular motor system because none of the current extensions to models of the normal behaviour of the ocular motor system can explain the range of identified waveforms.

Adolescent↗
PubMed1994

Dynamical assessment of physiological systems and states using recurrence plot strategies.

Physiological systems are best characterized as complex dynamical processes that are continuously subjected to and updated by nonlinear feedforward and feedback inputs. System outputs usually exhibit wide varieties of behaviors due to dynamical interactions between system components, external noise perturbations, and physiological state changes. Complicated interactions occur at a variety of hierarchial levels and involve a number of interacting variables, many of which are unavailable for experimental measurement. In this paper we illustrate how recurrence plots can take single physiological measurements, project them into multidimensional space by embedding procedures, and identify time correlations (recurrences) that are not apparent in the one-dimensional time series. We extend the original description of recurrence plots by computing an array of specific recurrence variables that quantify the deterministic structure and complexity of the plot. We then demonstrate how physiological states can be assessed by making repeated recurrence plot calculations within a window sliding down any physiological dynamic. Unlike other predominant time series techniques, recurrence plot analyses are not limited by data stationarity and size constraints. Pertinent physiological examples from respiratory and skeletal motor systems illustrate the utility of recurrence plots in the diagnosis of nonlinear systems. The methodology is fully applicable to any rhythmical system, whether it be mechanical, electrical, neural, hormonal, chemical, or even spacial.

Animals↗
PubMed1994

Spatial and space-time correlations in systems of subpopulations with stochastic migration.

The great majority of models of the population genetics of subdivided populations have made the simplifying assumption that the gene frequencies in migrant groups are deterministic. The present paper examines models which more closely mimic natural conditions, in which the gene frequencies in migrant groups are subject to stochastic effects. It is shown that some types of stochastic migration can cause dramatic changes in spatial correlations and variance. These changes depend on how the stochastic migration effects in the gene frequency recursion equations are shared among nearby subpopulations during the same generation. Only for cases where the effects are completely unshared are the equilibrium spatial and space-time correlations among adult subpopulations unaffected, but the variance is always inflated. The analyses here use novel methods, by recasting population genetic migration-drift models as space-time autoregressive moving average (STARMA) processes. Recent theorems for STARMA processes are employed for finding the spatial correlations, and for the first time in population genetics theory the complete set of space-time correlations, for systems with general patterns of migration rates and numbers of spatial dimensions. The space-time correlations provide a uniquely detailed description of a system, and thus form a link between observed spatial autocorrelation statistics and the underlying space-time population genetic process. STARMA theoretical processes have direct statistical analogues that can be applied for process identification, parameter estimation, model fitting, and forecasting in real systems.

Animals↗
PubMed1987

Influence of noise on the function of a "physiological" neural network.

A model neural network with stochastic elements in its millisecond dynamics is investigated. The network consists of neuronal units which are modelled in close analogy to physiological neurons. Dynamical variables of the network are the cellular potentials, axonic currents and synaptic efficacies. The dynamics of the synapses obeys a modified Hebbian rule and, as proposed by v. d. Malsburg (1981, 1985), develop on a time scale of a tenth of a second. In a previous publication (Buhmann and Schulten 1986) we have confirmed that the resulting noiseless auto-associative network is capable of the well-known computational tasks of formal associative networks (Cooper 1973; Kohonen et al. 1984, 1981; Hopfield 1982). In the present paper we demonstrate that random fluctuations of the membrane potential improve the performance of the network. In comparison to a deterministic network a noisy neural network can learn at lower input frequencies and with lower average neural firing rates. The electrical activity of a noisy network is very reminiscent of that observed by physiological recordings. We demonstrate furthermore that associative storage reduces the effective dimension of the phase space in which the electrical activity of the network develops.

Animals↗
PubMed2006

A semi-stochastic model of the transmission of Escherichia coli O157 in a typical UK dairy herd: dynamics, sensitivity analysis and intervention/prevention strategies.

When modelling the transmission of infection within small populations, it is necessary to consider the possibility of stochastic fade-out of infection. We present a semi-stochastic model for the transmission of a microparasite, in this case Escherichia coli O157, within a multigroup system, namely a typical UK dairy herd. The model includes birth, death, maturation, the dry/lactating cycle and various types of transmission (i.e. direct, pseudovertical (representing direct faecal-oral transmission between dam and calf within the first 48 h) and indirect (via free-living infectious units in the environment)). We present the results of our simulation study alongside data from empirical studies and also compare simulation results with those for the corresponding deterministic model. We then examine the effects of reducing shedding in the food-producing groups on outbreak size and prevalence of infection. A sensitivity analysis of herd prevalence reveals that, for both the deterministic and the semi-stochastic model, the prevalence within the herd is most sensitive to two parameters relating to the weaned group. This supports our previously reported conclusions for the deterministic model, which were based on an analysis of the next-generation matrix. The sensitivity analysis also indicates that herd prevalence is greatly affected by two other parameters relating to the lactating group. We conclude by discussing the possible efficacy of suggested intervention strategies.

Animals↗
PubMed1997

Models for the simple epidemic.

Stochastic and deterministic models for the simple epidemic are formulated and compared. Both discrete and continuous time models are considered. The conjecture of equivalence between stochastic and deterministic models for large initial susceptible populations is shown to be of limited practical importance by a simulation study, whereas the number of initial infectives is shown to dramatically affect the convergence of the two types of models. In addition, the discrete stochastic model, which is easy to formulate and simulate, is shown to converge with its continuous counterpart as the time step becomes small.

Disease Outbreaks↗
PubMed1994

Omega-dimension of chaotic time series.

New characteristics, Omega-dimension (D(Omega)) and spectral density of dimension (D'(Omega)), of deterministically generated irregular signals are proposed. They are the functions that are calculated employing spectral transformation such as filtering with limit transmission frequency Omega. The Omega-dimension of the time series generated by a dynamical system with a homogeneous strange attractor does not depend on Omega and coincides with the dimension measured using a standard technique. If the time series does not possess the similarity property as the time scale changes (i.e., it is multiscaled in time), the calculation of D(Omega) gives additional information on the properties of the signal. In particular, it allows for the estimation of additional degrees of freedom in the time series on signal transmission through the communication channel and preliminary processing.

Journal Article↗
PubMed2004

Stochastic heart-rate model can reveal pathologic cardiac dynamics.

A simple one-dimensional Langevin-type stochastic difference equation can simulate the heart-rate fluctuations in a time scale from minutes to hours. The model consists of a deterministic nonlinear part and a stochastic part typical of Gaussian noise, and both parts can be directly determined from measured heart-rate data. Data from healthy subjects typically exhibit the deterministic part with two or more stable fixed points. Studies of 15 congestive heart-failure subjects reveal that the deterministic part of pathologic heart dynamics has no clear stable fixed points. Direct simulations of the stochastic model for normal and pathologic cases can produce statistical parameters similar to those of real subjects. Results directly indicate that pathologic situations simplify the heart-rate control system.

Computer Simulation↗
PubMed2005

Length of stay-based patient flow models: recent developments and future directions.

Modelling patient flow in health care systems is vital in understanding the system activity and may therefore prove to be useful in improving their functionality. An extensively used measure is the average length of stay which, although easy to calculate and quantify, is not considered appropriate when the distribution is very long-tailed. In fact, simple deterministic models are generally considered inadequate because of the necessity for models to reflect the complex, variable, dynamic and multidimensional nature of the systems. This paper focuses on modelling length of stay and flow of patients. An overview of such modelling techniques is provided, with particular attention to their impact and suitability in managing a hospital service.

Bed Occupancy↗
PubMed2004

Independent component analysis: fetal signal reconstruction from magnetocardiographic recordings.

Independent component analysis (ICA) was used for the processing of cardiological signals obtained by means of fetal magnetocardiography (fMCG), a technique allowing the non-invasive recording of the weak magnetic field variations associated to the electrical activity of the fetal heart. Purpose of the present work was to verify whether a computational-light ICA algorithm (FastICA), tailored to the characteristics of fMCG, could reconstruct reliable signals of the fetal cardiac activity during the last gestational trimester, when good electrophysiological traces are difficult to obtain although being extremely important for clinical diagnosis of severe fetal dysrhythmias. Several combinations of input recordings and output components were examined in order to assess the best configuration to successfully use FastICA. The reconstructed traces were compared with those obtained with deterministic techniques already used for this purpose, and they showed to be stable and reliable, unaffected by overlapped maternal and fetal beats and suitable for clinical applications.

Algorithms↗
PubMed2000

Dosimetry and microdosimetry of targeted radiotherapy.

Dosimetry in targeted radiotherapy (TR) uses different calculation methods, whose degree of refinement is closely conditioned by the particular objective sought. It is more generally performed to establish a correlation between the quantity of radiation delivered to a target and the biological damage observed or that can be reliably predicted. It can thus be used to optimise treatments and allow comparison of different therapeutic approaches, as well as to study the basic methods of irradiation of biological matter. Two broad types of investigations can be found in the literature: microdosimetric ones (stochastic approaches used to study energy deposits) and macrodosimetric ones (non-stochastic or deterministic approaches). The mathematical formalism is consistent between these two types, and the calculation methods currently used are often similar. This review presents different approaches to the dosimetry of radionuclides used in TR. The introduction defines the general problem, the role of dosimetry in TR and the specific problems raised by targeting (non-uniformity of source distributions). The first part considers the types of calculation methods found in TR in relation to the basic quantities used to represent stochastic energy deposit on a cellular scale. In particular, it compares the formalism and the methods used in microdosimetric or conventional macrodosimetric approches. Although microdosimetry, or even track structure calculations, can provide the basic elements for modelling the absorbed dose process, a simplified dosimetric approach may be adequate to describe the phenomena observed. The scheme proposed by the MIRD committee relates to such an approach and is presented together with other methods allowing the calculation of the mean dose delivered (analytic methods, dose point kernels, Monte-Carlo, etc.). The second part shows the application range for the various methods, providing selected examples of dosimetric approaches in TR on different scales, from the organ (or tissues) to the cell or even DNA, and a brief presentation of bone marrow dosimetry.

Animals↗
PubMedDate not supplied

Analysis of recurrent events: application to a clinical trial of colony stimulating factor with the endpoint of febrile neutropenia.

This paper discusses the analysis of randomized clinical trials where the primary endpoint is a recurrent event observed during a time period which can vary among patients. In particular it describes a method of estimating robust confidence limits for clinically relevant parameters. As an illustrative example, a clinical trial of a macrophage colony stimulating factor (M-CSF) for patients with acute myeloblastic leukaemia (AML) is introduced where the endpoint is the incidence and duration of febrile neutropenia. The causal effects in the framework of Rubin's deterministic model are defined as estimands. A non-parametric method based on the permutation test, using an accelerated search procedure proposed by Garthwaite, is compared with other standard methods by simulation and by application to the AML trial data.

Computer Simulation↗
PubMed2003

A technique for estimating maximum harvesting effort in a stochastic fishery model.

Exploitation of biological resources and the harvest of population species are commonly practiced in fisheries, forestry and wild life management. Estimation of maximum harvesting effort has a great impact on the economics of fisheries and other bio-resources. The present paper deals with the problem of a bioeconomic fishery model under environmental variability. A technique for finding the maximum harvesting effort in fluctuating environment has been developed in a two-species competitive system, which shows that under realistic environmental variability the maximum harvesting effort is less than what is estimated in the deterministic model. This method also enables us to find out the safe regions in the parametric space for which the chance of extinction of the species is minimized. A real life fishery problem has been considered to obtain the inaccessible parameters of the system in a systematic way. Such studies may help resource managers to get an idea for controlling the system.

Animals↗