Effect of periodic stimulus on a neuronal diffusion model with signal-dependent noise

To relate the noise intensity with a periodically modulated input signal in a single neuron stochastic model we introduce a diffusion model with both time modulated drift and diffusion coefficient. Such a model is the continuous version of a Stein model with time oscillating frequencies for the Poisson processes describing the inputs impinging on the neuron. We focus here on some aspects of the resonance phenomenon for such a model. We compare the corresponding interspike interval distribution with the analogous distribution for a model sharing the same parameter values, but with constant noise intensity. Examples with two different levels for this noise intensity are discussed. The enhancement of the height of the peaks in the interspike interval distribution appearing at the modulation period, the improvement of the phase locking behavior and an enlargement of the noise ranges where a resonance like behavior arises are the main features observed in the considered cases.     

Quantum squeezed state analysis of spontaneous ultra weak light photon emission of practitioners of meditation and control subjects

Research on human ultra-weak photon emission (UPE) has suggested a typical human emission anatomic percentage distribution pattern. It was demonstrated that emission intensities are lower in long-term practitioners of meditation as compared to control subjects. The percent contribution of emission from different anatomic locations was not significantly different for meditation practitioners and control subjects. Recently, a procedure was developed to analyze the fluctuations in the signals by measuring probabilities of detecting different numbers of photons in a bin and correct these for background noise. The procedure was tested utilizing the signal from three different body locations of a single subject, demonstrating that probabilities have non-classical features and are well described by the signal in a coherent state from the three body sites. The values indicate that the quantum state of photon emitted by the subject could be a coherent state in the subject being investigated. The objective in the present study was to systematically quantify, in subjects with long-term meditation experience and subjects without this experience, the photon count distribution of 12 different locations. Data show a variation in quantum state parameters within each individual subject as well as variation in quantum state parameters between the groups.     

Quantum nature of photon signal emitted by Xanthoria parietina and its implications to biology

The properties of living systems are usually described in the semi-classical framework that makes phenomenological division of properties into four classes--matter, psyche, soft consciousness and hard consciousness. Quantum framework provides a scientific basis of this classification of properties. The scientific basis requires the existence of macroscopic quantum entity entangled with quantum photon field of a living system. Every living system emits a photon signal with features indicating its quantum nature. Quantum nature of the signal emitted by a sample of X. parietina is confirmed by analysing photo count distributions obtained in 20000 measurements of photon number in contiguous bins of sizes of 50, 100, 200, 300 and 500 ms. The measurements use a broadband detector sensitive in 300-800 nm range (Photo count distributions of background noise and observed signal are measured similarly. These measurements background noise corrected squeezed state parameters of the signal. The parameters are signal strength expressed in counts per bin, r = 0.06, theta = 2.76 and phi = 0.64. The parameters correctly reproduce photo count distribution of any bin size in 50 ms-6 s range. The reproduction of photo count distributions is a credible evidence of spontaneous emission of photon signal in a quantum squeezed state for macroscopic time by the sample. The evidence is extrapolated to other living systems emitting similar photon signals. It is suggested that every living system is associated with a photon field in squeezed state. The suggestion has far reaching implications to biology and provides two ways of observing and manipulating a living system--either through matter or field or a combination of the two. Some implications and possible scenarios are elaborated.     

The Path Integral Formulation of Climate Dynamics

The chaotic nature of the atmospheric dynamics has stimulated the applications of methods and ideas derived from statistical dynamics. For instance, ensemble systems are used to make weather predictions recently extensive, which are designed to sample the phase space around the initial condition. Such an approach has been shown to improve substantially the usefulness of the forecasts since it allows forecasters to issue probabilistic forecasts. These works have modified the dominant paradigm of the interpretation of the evolution of atmospheric flows (and oceanic motions to some extent) attributing more importance to the probability distribution of the variables of interest rather than to a single representation. The ensemble experiments can be considered as crude attempts to estimate the evolution of the probability distribution of the climate variables, which turn out to be the only physical quantity relevant to practice. However, little work has been done on a direct modeling of the probability evolution itself. In this paper it is shown that it is possible to write the evolution of the probability distribution as a functional integral of the same kind introduced by Feynman in quantum mechanics, using some of the methods and results developed in statistical physics. The approach allows obtaining a formal solution to the Fokker-Planck equation corresponding to the Langevin-like equation of motion with noise. The method is very general and provides a framework generalizable to red noise, as well as to delaying differential equations, and even field equations, i.e., partial differential equations with noise, for example, general circulation models with noise. These concepts will be applied to an example taken from a simple ENSO model.     

Can quantum-bumps in photoreceptors be reconstructed from noise-data?

The method of reconstructing quantum bumps in photoreceptor cells from noise data by making use of shot noise theory is critically reviewed. The application of this method produces results irrespective of whether the conditions for reconstructing bumps by the method are satisfied or not and even irrespective of whether at high stimulus intensities quantum bumps exist or not. We argue that at high intensities the concept of quantum bumps indeed becomes physically meaningless and degenerates to a purely mathematical concept. In order to investigate the meaning of the results of the reconstruction method, we submit it to a test model for which bumps and single channel opening events can be evaluated analytically. By comparing the analytical results of the test model with that of the reconstruction method applied to the test model we find: (1) even at low intensities, the reconstructed bump values deviate from the analytical results by up to an order of magnitude due to the variability of the bumps, (2) at high intensities, the reconstruction method produces single chennel opening events rather than anything like a quantum bump. We also find, however, that there is no continuous transition from a bump at low intensities to a single channel event at high intensities.     

Role of correlated noise in textural features extraction

Predictive models of tumor response based on heterogeneity metrics in medical images, such as textural features, are highly suggestive. However, the demonstrated sensitivity of these features to noise does affect the model being developed. An in-depth analysis of the noise influence on the extraction of texture features was performed based on the assumption that an improvement in information quality can also enhance the predictive model. A heuristic approach was used that recognizes from the beginning that the noise has its own texture and it was analysed how it affects the quantitative signal data. A simple procedure to obtain noise image estimation is shown; one which makes it possible to extract the noise-texture features at each observation. The distance measured between the textural features in signal and estimated noise images allows us to determine the features affected in each observation by the noise and, for example, to exclude some of them from the model. A demonstration was carried out using synthetic images applying realistic noise models found in medical images. Drawn conclusions were applied to a public cohort of clinical images obtained using FDG-PET to show how the predictive model could be improved. A gain in the area under the receiver operating characteristic curve between 10 and 20% when noise texture information is used was shown. An improvement between 20 and 30% can be appreciated in the estimated model quality.     

Models of retinal signal processing at high luminances

Summary In psychophysics the relation between the threshold luminance increment and the background luminance for visual increment detection, is known to become linear for high luminances. This so called Weber-law means that at high luminances the threshold is caused by the detection apparatus, and not by the quantum noise of the stimulus itself. A simple machine implementing the Weber-law is discussed. This machine obviates the need for an internal noise source (or dark light). It is the combined result of an apparatus that reduces the output event rate by causing the quantum to spike ratio to follow the input intensity, and a detection criterion that needs a constant number of extra output events to generate a positive response. The dynamic properties of the machine are described by the solutions of a simple though nonlinear differential equation. The supra threshold properties prove to mimic several of the responses known to occur at ganglion cell level in the cat's retina. A tentative model of an on-center receptive field is presented, and results obtained by simulating this model on a digital cumputer are compared with some recent electrophysiological findings. Finally a possible extension of the model to a full on-center/off-surround receptive field model is discussed.The authors are indebted to the Netherlands Organisation for the Advancement of Pure Research (Z.W.O.) for financial support of a part of this investigation.We thank W. A. Aarnink for invaluable help in debugging and running the programs involved in this work.     

Quantum-like model of processing of information in the brain based on classical electromagnetic field

We propose a model of quantum-like (QL) processing of mental information. This model is based on quantum information theory. However, in contrast to models of "quantum physical brain" reducing mental activity (at least at the highest level) to quantum physical phenomena in the brain, our model matches well with the basic neuronal paradigm of the cognitive science. QL information processing is based (surprisingly) on classical electromagnetic signals induced by joint activity of neurons. This novel approach to quantum information is based on representation of quantum mechanics as a version of classical signal theory which was recently elaborated by the author. The brain uses the QL representation (QLR) for working with abstract concepts; concrete images are described by classical information theory. Two processes, classical and QL, are performed parallely. Moreover, information is actively transmitted from one representation to another. A QL concept given in our model by a density operator can generate a variety of concrete images given by temporal realizations of the corresponding (Gaussian) random signal. This signal has the covariance operator coinciding with the density operator encoding the abstract concept under consideration. The presence of various temporal scales in the brain plays the crucial role in creation of QLR in the brain. Moreover, in our model electromagnetic noise produced by neurons is a source of superstrong QL correlations between processes in different spatial domains in the brain; the binding problem is solved on the QL level, but with the aid of the classical background fluctuations.     

Modeling the variability of firing rate of retinal ganglion cells.

Impulse trains simulating the maintained discharges of retinal ganglion cells were generated by digital realizations of the integrate-and-fire model. If the mean rate were set by a "bias" level added to "noise," the variability of firing would be related to the mean firing rate as an inverse square root law; the maintained discharges of retinal ganglion cells deviate systematically from such a relationship. A more realistic relationship can be obtained if the integrate-and-fire mechanism is "leaky"; with this refinement, the integrate-and-fire model captures the essential features of the data. However, the model shows that the distribution of intervals is insensitive to that of the underlying variability. The leakage time constant, threshold, and distribution of the noise are confounded, rendering the model unspecifiable. Another aspect of variability is presented by the variance of responses to repeated discrete stimuli. The variance of response rate increases with the mean response amplitude; the nature of that relationship depends on the duration of the periods in which the response is sampled. These results have defied explanation. But if it is assumed that variability depends on mean rate in the way observed for maintained discharges, the variability of responses to abrupt changes in lighting can be predicted from the observed mean responses. The parameters that provide the best fits for the variability of responses also provide a reasonable fit to the variability of maintained discharges.     

Predicting the species abundance distribution using a model food web

A large number of models of the species abundance distribution (SAD) have been proposed, many of which are generically similar to the log-normal distribution, from which they are often indistinguishable when describing a given data set. Ecological data sets are necessarily incomplete samples of an ecosystem, subject to statistical noise, and cannot readily be combined to yield a closer approximation to the underlying distribution. In this paper, we adopt the Webworld ecosystem model to study the predicted SAD in detail. The Webworld model is complex, and does not allow analytic examination of such features; rather, we use simulation data and an approach similar to that of ecologists analysing empirical data. By examining large sets of fully described data we are able to resolve features which can distinguish between models but which have not been investigated in detail in field data. We find that the power-law normal distribution is superior to both the log-normal and logit-normal distributions, and that the data can improve on even this at the high-population cut-off.     

Plasmonically Induced Energy Flow in Monodisperse Quantum Dot Solids

We studied the excitation-power-dependent red shift and broadening of the emission spectra of monodisperse CdSe/ZnS quantum dot solids when they are in close proximity of gold metallic nanoparticles. Our results suggest that these features are the signs of plasmonic enhancement of the interdot energy transfer in such solids leading to (a) efficient funneling of excitons to the locations where quantum dots with large CdSe cores are and (b) near complete depletion of excitons in regions where the quantum dots with incomplete shells or/and small core sizes are located. We studied the impacts of the heat generated by the metallic nanoparticles and discussed the effects of excitation-power-dependent photoionization rates. The uneven spatial distribution of excitons in monodisperse quantum dot solids in the presence of metallic nanoparticles suggests that plasmonic fields can drive significant spatial migration of energy in such structures.     

Information transmission in microbial and fungal communication: from classical to quantum

Microbes have their own communication systems. Secretion and reception of chemical signaling molecules and ion-channels mediated electrical signaling mechanism are yet observed two special ways of information transmission in microbial community. In this article, we address the aspects of various crucial machineries which set the backbone of microbial cell-to-cell communication process such as quorum sensing mechanism (bacterial and fungal), quorum sensing regulated biofilm formation, gene expression, virulence, swarming, quorum quenching, role of noise in quorum sensing, mathematical models (therapy model, evolutionary model, molecular mechanism model and many more), synthetic bacterial communication, bacterial ion-channels, bacterial nanowires and electrical communication. In particular, we highlight bacterial collective behavior with classical and quantum mechanical approaches (including quantum information). Moreover, we shed a new light to introduce the concept of quantum synthetic biology and possible cellular quantum Turing test.     

Signal processing in magnetoencephalography.

The subject of this article is detection of brain magnetic fields, or magnetoencephalography (MEG). The brain fields are many orders of magnitude smaller than the environmental magnetic noise and their measurement represent a significant metrological challenge. The only detectors capable of resolving such small fields and at the same time handling the large dynamic range of the environmental noise are superconducting quantum interference devices (or SQUIDs). The SQUIDs are coupled to the brain magnetic fields using combinations of superconducting coils called flux transformers (primary sensors). The environmental noise is attenuated by a combination of shielding, primary sensor geometry, and synthetic methods. One of the most successful synthetic methods for noise elimination is synthetic higher-order gradiometers. How the gradiometers can be synthesized is shown and examples of their noise cancellation effectiveness are given. The MEG signals measured on the scalp surface must be interpreted and converted into information about the distribution of currents within the brain. This task is complicated by the fact that such inversion is nonunique. Additional mathematical simplifications, constraints, or assumptions must be employed to obtain useful source images. Methods for the interpretation of the MEG signals include the popular point current dipole, minimum norm methods, spatial filtering, beamformers, MUSIC, and Bayesian techniques. The use of synthetic aperture magnetometry (a class of beamformers) is illustrated in examples of interictal epileptic spiking and voluntary hand-motor activity.     

The value of asymmetric signal processing in klinokinesis

Klinokinesis is a behavioral mechanism in which an organism moves toward or away from a stimulus source by altering its frequency of change of direction without biasing its turns with respect to the stimulus field. Previous studies of a variety of organisms have demonstrated that rates of adaptation (or other information processing features) for increases and decreases in stimulus intensity are often very different from one another. In order to determine if such asymmetric signal processing could improve the efficiency of klinokinesis, computer modeling studies were performed. The model involved a simple generic version of klinokinesis in 2 dimensions with the rate of adaptation for increasing intensity varied independently of the rate for decreasing intensity. The effects of three types of noise that limit the performance of the model were tested-intensity noise, motor noise, and developmental noise. The results demonstrated that, with all three types of noise, the two adaptation rates had quite different effects on efficiency. The overall pattern of effects was different for each type of noise. In the cases of intensity noise and motor noise, the optimum combination of adaptation rates had a 3-to 5-fold higher rate for decreases in attractant than for increases, which is similar to what has previously been found with bacteria and nematodes.     

Unsupervised noise removal algorithms for three-dimensional confocal fluorescence microscopy

Algorithms are presented for effective suppression of the quantum noise artifact that is inherent to three-dimensional confocal fluorescence microscopy images of extended spatial objects such as neurons. The specific advances embodied in these algorithms are as follows: (i) they incorporate an automatic and pattern-constrained three-dimensional segmentation of the image field, and use it to limit any smoothing to the interiors of specified image regions and hence avoid the blurring that is inevitably associated with conventional noise removal algorithms; (ii) they are ‘unsupervised’ in the sense that they automatically estimate and adapt to the unknown spatially and temporally varying noise level in the microscopy data. Fast computation is achieved by parallel computation methods, rather than by algorithmic or modelling compromises.The quantum noise artifact is modelled using a mixture of spatially non-homogeneous Poisson point processes. The intensity of each component process is constrained to lie in specific intervals. A set of segmentation and edge-site variables are used to determine the intensity of the mixture process. Using this model, the noise-removal process is formulated as the joint optimal estimation of the segmentation labels, edge-sites and intensity of the mixture Poisson point process, subject to a combination of stochastic priors and syntactic pattern constraints. The computations proceed iteratively, starting from a set of approximate user-supplied, or default initial guesses of the underlying random process parameters. An Expectation Maximization algorithm is used to obtain a more precise characterization of these parameters, that are then input to a joint estimation algorithm.Stereoscopic renderings of processed three-dimensional images of murine hippocampal neurons are presented to demonstrate the effectiveness of the method. The processed images exhibit increased contrast and significant smoothing and reduction of the background intensity while avoiding any blurring of the foreground neuronal structures.     

The impact of stochasticity on the behaviour of nonlinear population models: synchrony and the Moran effect

Environmental variation is ubiquitous, but its effects on nonlinear population dynamics are poorly understood. Using simple (unstructured) nonlinear models we investigate the effects of correlated noise on the dynamics of two otherwise independent populations (the Moran effect), i.e. we focus on noise rather than dispersion or trophic interaction as the cause of population synchrony. We find that below the bifurcation threshold for periodic behaviour (1) synchrony between populations is strongly dependent on the shape of the noise distribution but largely insensitive to which model is studied, (2) there is, in general, a loss of synchrony as the noise is filtered by the model, (3) for specially structured noise distributions this loss can be effectively eliminated over a restricted range of distribution parameter values even though the model might be nonlinear, (4) for unstructured models there is no evidence of correlation enhancement, a mechanism suggested by Moran, but above the bifurcation threshold enhancement is possible for weak noise through phase-locking, (5) rapid desynchronisation occurs as the chaotic regime is approached. To carry out the investigation the stochastic models are (a) reformulated in terms of their joint asymptotic probability distributions and (b) simulated to analyse temporal patterns.     

Multilevel grouped regression for analyzing self-reported health in relation to environmental factors: the model and its application

A method for modeling the relationship of polychotomous health ratings with predictors such as area characteristics, the distance to a source of environmental contamination, or exposure to environmental pollutants is presented. The model combines elements of grouped regression and multilevel analysis. The statistical model describes the entire response distribution as a function of the predictors so that any measure that summarizes this distribution can be calculated from the model. With the model, polychotomous health ratings can be used, and there is no need for a priori dichotomizing such variables which would lead to loss of information. It is described how, according to the model, various measures describing the response distribution are related to the exposure, and the confidence and tolerance intervals for these relationships are presented. Specific attention is given to the incorporation of random factors in the model. The application that here serves as an example, concerns annoyance from transportation noise. Exposure-response relationships obtained with the described method of modeling are presented for aircraft, road traffic, and railway noise.     

Optimum spectral windows to minimize quantum noise of ratiometric intracellular fluorescent probes

When fluorescent indicators are used to measure intracellular ligands in single cells, the quality of the data is usually limited by quantum (shot) noise. For indicators which shift excitation or emission wavelengths upon ligand binding, a ratiometric method is usually employed. In choosing the spectral windows for excitation or collection of fluorescence, there is a trade-off between maximum sensitivity to ligand binding, and maximum collection of light. We show that there is a well-defined optimum choice of windows which minimizes the error caused by quantum noise in the estimated ligand concentration. An algorithm for determining these optimum windows is presented. As an example, we consider the measurement of intracellular calcium by indo-1 fluorescence emission ratio in cardiac myocytes. The optimum wavelength bands for collection of fluorescence are considerably wider than those commonly employed. The use of these windows in a pulsed-excitation time-resolved calcium measurement instrument resulted in improved signal to noise ratio of the calcium signal.