Inference about a change point in experimental neurophysiology

When recording the electrical discharge of single neurons, two questions arise: (1) Does this activity represent changes related to some event of the experiment (for instance, a stimulus)? (2) What is the mean latency time between this event and the change of activity? We propose statistical tools for answering these questions based on the estimation of a change point, for which we have developed a new method. This method selects the most clear-cut change when other changes exist.     

Time, from psychology to neurophysiology: a historical view

Two aspects of psychology and physiology of time are dealt with in this paper: the way time perception was increasingly studied during the 19th century by scientists, including many physicists, and the way the temporal properties of the nervous system were discovered and explored by physiologists. The neurophysiological correlation between both aspects still remains to be explained. The relationship between time consciousness and consciousness mechanisms was often guessed by philosophers and looked for by scientists. It remains a major subject of investigation in neuroscience as well as a philosophical puzzle.     

Visual neurophysiology: recordings from the human primate

Neurophysiological studies in human patients, with experiments of the kind traditionally reserved for monkeys, are beginning to provide valuable insight into the workings of the brain. Taking center stage is the question of which neurons lie at the heart of perception itself.     

Multiplicatively interacting point processes and applications to neural modeling

We introduce a nonlinear modification of the classical Hawkes process allowing inhibitory couplings between units without restrictions. The resulting system of interacting point processes provides a useful mathematical model for recurrent networks of spiking neurons described as Wiener cascades with exponential transfer function. The expected rates of all neurons in the network are approximated by a first-order differential system. We study the stability of the solutions of this equation, and use the new formalism to implement a winner-takes-all network that operates robustly for a wide range of parameters. Finally, we discuss relations with the generalised linear model that is widely used for the analysis of spike trains.     

Stochastic processes influence stationary-phase decisions in Bacillus subtilis

It has recently been proposed that phenotypic variation in clonal populations of bacterial species results from intracellular "noise," i.e., random fluctuations in levels of cellular molecules, which would be predicted to be insensitive to selective pressure. To test this notion, we propagated five populations of Bacillus subtilis for 5,000 generations with selection for one phenotype: the decision to sporulate. In support of the noise hypothesis, we report that none of the populations responded to selection by improving their efficiency of sporulation, indicating that intracellular noise is independent of heritable genotype.     

Stochastic model of template-directed elongation processes in biology

We present a novel modular, stochastic model for biological template-based linear chain elongation processes. In this model, elongation complexes (ECs; DNA polymerase, RNA polymerase, or ribosomes associated with nascent chains) that span a finite number of template units step along the template, one after another, with semaphore constructs preventing overtaking. The central elongation module is readily extended with modules that represent initiation and termination processes. The model was used to explore the effect of EC span on motor velocity and dispersion, and the effect of initiation activator and repressor binding kinetics on the overall elongation dynamics. The results demonstrate that (1) motors that move smoothly are able to travel at a greater velocity and closer together than motors that move more erratically, and (2) the rate at which completed chains are released is proportional to the occupancy or vacancy of activator or repressor binding sites only when initiation or activator/repressor dissociation is slow in comparison with elongation.     

Testing separability in spatial-temporal marked point processes

Nonparametric tests for investigating the separability of a spatial-temporal marked point process are described and compared. It is shown that a Cramer-von Mises-type test is very powerful at detecting gradual departures from separability, and that a residual test based on randomly rescaling the process is powerful at detecting nonseparable clustering or inhibition of the marks. An application to Los Angeles County wildfire data is given, in which it is shown that the separability hypothesis is invalidated largely due to clustering of fires of similar sizes within periods of up to 3.9 years.     

Stochastic processes in particle-number fluctuations in an electron-photon shower

The paper is concerned with the existence and asymptotic character of the nonlinear boundary value problemdG/dt=F(t,G,F, ¦α?β¦) (1) ¦α?β¦dF/dt=g(t,G,F, ¦α?β¦)G(o,¦α?β¦)=k ₁,G(∞,¦α?β¦)=k ₂ (2) as ¦α?β¦→ o+ The discussion is related to the problem of particle-number fluctuations in the theory of cosmic radiation andG andF denote respectively the probability generating functions for the electron distribution in an electron-initiated and a photon-initiated shower. A solution of the system (1) satisfying the boundary conditions (2) is constructed so that specified limiting conditions are fulfilled.     

On the separation of semiperiodic superimposed point processes: application to electromyographic signals.

A method for the separation of semiperiodic (Gaussian probability distribution of intervals with moderate coefficient of variation) superimposed point processes, to be implemented on a digital computer is described. The efficiency of the filter used for the extraction of the underlying event sequences from the pooled array has been investigated by simulation. Approximately 80 percent of the events belonging to the underlying sequence to be extracted were recovered (with the addition of "impurity events"), still preserving the mean rate and coefficient of variation of the original indicidual sequence concerned. The program was used on an example of an actual electromyographic recording (comprised of a number of motor units discharging simultaneously), resolving the number of motor units involved, their mean rates, and the coefficient of variation of the individual action potential sequences.     

Creating spatially continuous maps of past land cover from point estimates: A new statistical approach applied to pollen data

Reliable estimates of past land cover are critical for assessing potential effects of anthropogenic land-cover changes on past earth surface-climate feedbacks and landscape complexity. Fossil pollen records from lakes and bogs have provided important information on past natural and human-induced vegetation cover. However, those records provide only point estimates of past land cover, and not the spatially continuous maps at regional and sub-continental scales needed for climate modelling.We propose a set of statistical models that create spatially continuous maps of past land cover by combining two data sets: 1) pollen-based point estimates of past land cover (from the REVEALS model) and 2) spatially continuous estimates of past land cover, obtained by combining simulated potential vegetation (from LPJ-GUESS) with an anthropogenic land-cover change scenario (KK10). The proposed models rely on statistical methodology for compositional data and use Gaussian Markov Random Fields to model spatial dependencies in the data.Land-cover reconstructions are presented for three time windows in Europe: 0.05, 0.2, and 6 ka years before present (BP). The models are evaluated through cross-validation, deviance information criteria and by comparing the reconstruction of the 0.05 ka time window to the present-day land-cover data compiled by the European Forest Institute (EFI). For 0.05 ka, the proposed models provide reconstructions that are closer to the EFI data than either the REVEALS- or LPJ-GUESS/KK10-based estimates; thus the statistical combination of the two estimates improves the reconstruction. The reconstruction by the proposed models for 0.2 ka is also good. For 6 ka, however, the large differences between the REVEALS- and LPJ-GUESS/KK10-based estimates reduce the reliability of the proposed models. Possible reasons for the increased differences between REVEALS and LPJ-GUESS/KK10 for older time periods and further improvement of the proposed models are discussed.     

Stochastic and deterministic assembly processes in subsurface microbial communities

A major goal of microbial community ecology is to understand the forces that structure community composition. Deterministic selection by specific environmental factors is sometimes important, but in other cases stochastic or ecologically neutral processes dominate. Lacking is a unified conceptual framework aiming to understand why deterministic processes dominate in some contexts but not others. Here we work toward such a framework. By testing predictions derived from general ecological theory we aim to uncover factors that govern the relative influences of deterministic and stochastic processes. We couple spatiotemporal data on subsurface microbial communities and environmental parameters with metrics and null models of within and between community phylogenetic composition. Testing for phylogenetic signal in organismal niches showed that more closely related taxa have more similar habitat associations. Community phylogenetic analyses further showed that ecologically similar taxa coexist to a greater degree than expected by chance. Environmental filtering thus deterministically governs subsurface microbial community composition. More importantly, the influence of deterministic environmental filtering relative to stochastic factors was maximized at both ends of an environmental variation gradient. A stronger role of stochastic factors was, however, supported through analyses of phylogenetic temporal turnover. Although phylogenetic turnover was on average faster than expected, most pairwise comparisons were not themselves significantly non-random. The relative influence of deterministic environmental filtering over community dynamics was elevated, however, in the most temporally and spatially variable environments. Our results point to general rules governing the relative influences of stochastic and deterministic processes across micro- and macro-organisms.     

Stochastic processes in nano-biomachines revealed by single molecule detection

Proteins and their assemblies are in the size of nanometers and are exposed to thermal disturbances. Many molecular processes in these nano-biomachines are stochastic, reflecting the fact that the input energy level is comparable to that of thermal energy. These stochastic properties have been revealed by recently developed single molecule detection techniques. The movement of molecular motors, myosin, and kinesin, has been suggested to be thermally driven. Random thermal movement is biased using the energy of the ATP hydrolysis. Thus, the molecular motors may harness thermal energy. This unique mechanism may be important in understanding the operation of the biosystems.