Variability Measures of Positive Random Variables

During the stationary part of neuronal spiking response, the stimulus can be encoded in the firing rate, but also in the statistical structure of the interspike intervals. We propose and discuss two information-based measures of statistical dispersion of the interspike interval distribution, the entropy-based dispersion and Fisher information-based dispersion. The measures are compared with the frequently used concept of standard deviation. It is shown, that standard deviation is not well suited to quantify some aspects of dispersion that are often expected intuitively, such as the degree of randomness. The proposed dispersion measures are not entirely independent, although each describes the interspike intervals from a different point of view. The new methods are applied to common models of neuronal firing and to both simulated and experimental data

Experimental cross-correlation Nitrogen Q-branch CARS Thermometry in a Spark Ignition Engine

A purely experimental technique was employed to derive temperatures from nitrogen Q-branch Coherent Anti-Stokes Raman Scattering (CARS) spectra, obtained in a high pressure, high temperature environment (spark ignition Otto engine). This was in order to obviate any errors arising from deficiencies in the spectral scaling laws which are commonly used to represent nitrogen Q-branch CARS spectra at high pressure. The spectra obtained in the engine were compared with spectra obtained in a calibrated high pressure, high temperature cell, using direct cross-correlation in place of the minimisation of sums of squares of residuals. The technique is demonstrated through the measurement of air temperature as a function of crankshaft angle inside the cylinder of a motored single-cylinder Ricardo E6 research engine, followed by the measurement of fuel-air mixture temperatures obtained during the compression stroke in a knocking Ricardo E6 engine. A standard CARS program (SANDIA’s CARSFIT) was employed to calibrate the altered non-resonant background contribution to the CARS spectra that was caused by the alteration to the mole fraction of nitrogen in the unburned fuel-air mixture. The compression temperature profiles were extrapolated in order to predict the auto-ignition temperatures

Geometry and dimensionality reduction of feature spaces in primary visual cortex

Some geometric properties of the wavelet analysis performed by visual neurons are discussed and compared with experimental data. In particular, several relationships between the cortical morphologies and the parametric dependencies of extracted features are formalized and considered from a harmonic analysis point of view

Untenable nonstationarity: An assessment of the fitness for purpose of trend tests in hydrology

The detection and attribution of long-term patterns in hydrological time series have been important research topics for decades. A significant portion of the literature regards such patterns as ‘deterministic components’ or ‘trends’ even though the complexity of hydrological systems does not allow easy deterministic explanations and attributions. Consequently, trend estimation techniques have been developed to make and justify statements about tendencies in the historical data, which are often used to predict future events. Testing trend hypothesis on observed time series is widespread in the hydro-meteorological literature mainly due to the interest in detecting consequences of human activities on the hydrological cycle. This analysis usually relies on the application of some null hypothesis significance tests (NHSTs) for slowly-varying and/or abrupt changes, such as Mann-Kendall, Pettitt, or similar, to summary statistics of hydrological time series (e.g., annual averages, maxima, minima, etc.). However, the reliability of this application has seldom been explored in detail. This paper discusses misuse, misinterpretation, and logical flaws of NHST for trends in the analysis of hydrological data from three different points of view: historic-logical, semantic-epistemological, and practical. Based on a review of NHST rationale, and basic statistical definitions of stationarity, nonstationarity, and ergodicity, we show that even if the empirical estimation of trends in hydrological time series is always feasible from a numerical point of view, it is uninformative and does not allow the inference of nonstationarity without assuming a priori additional information on the underlying stochastic process, according to deductive reasoning. This prevents the use of trend NHST outcomes to support nonstationary frequency analysis and modeling. We also show that the correlation structures characterizing hydrological time series might easily be underestimated, further compromising the attempt to draw conclusions about trends spanning the period of records. Moreover, even though adjusting procedures accounting for correlation have been developed, some of them are insufficient or are applied only to some tests, while some others are theoretically flawed but still widely applied. In particular, using 250 unimpacted stream flow time series across the conterminous United States (CONUS), we show that the test results can dramatically change if the sequences of annual values are reproduced starting from daily stream flow records, whose larger sizes enable a more reliable assessment of the correlation structures

Kódování feromonového signálu olfaktorními neurony motýla Agrotis ipsilon

i Abstract The main objective of the thesis is to describe differences in the activity of male A. ipsilon olfactory receptor neurons (ORNs) when stimulated by different temporal dynamics of the concentration of the conspecific female pheromone. First, under the artificial situation of constant pulse stimulation, and second, with a fluctuating signal resembling the natural situation. For this purpose, the experimental data were collected in the collaborating laboratory (Dr. P. Lucas, INRAe, Versailles, France) by employing a novel olfactometer system that enables precise temporal control of the pheromone delivery to individual sensilla. Using the R programming language, we analyzed various descriptors of the response reliability, randomness, and variability, as well as the information content of the evoked activity. The results are interpreted in the context of the classical efficient coding hypothesis, which states that sensory neurons are evolutionarily adapted to natural stimuli. The main finding is that although the response variability is widely spread across the ORN population, sometimes with no visible difference between the constant and fluctuating stimulation types, the fluctuating stimulus is usually encoded with systematically higher reliability, as revealed by the inspection of individual ORNs....Hlavním cílem této diplomové práce je popsat, zdali a jak se liší aktivita olfaktorních recepčních neuronů u samců A. ipsilon při stimulaci samičím pohlavním feromonem s různou časovou dynamikou koncentrace, tedy buďto umělým konstantním pulsem nebo přerušovaným signálem podobným přirozené stimulaci. Za tímto účelem byla ve spolupracující laboratoři (Dr. P. Lucas, INRAe, Versailles, France) získána experimentální data za použití nového olfaktometrického systému umožňujícího přesnou kontrolu nad časováním dodávek feromonu k sensillu. Byla provedena analýza odpovědi pomocí řady různých kvantifikátorů spolehlivosti, náhodnosti a variability v programovacím jazyce R. Výsledky byly interpretovány v kontextu klasické hypotézy efektivního kódování, která říká, že senzorické neurony jsou evolučně adaptovány na přirozenou stimulaci. Hlavní zjištění je, že ačkoli variabilita odpovědi celé populace ORN na fluktuující nebo konstantní stimulaci se ne vždy liší, na úrovni individuálních neuronů je odpověď na fluktuující stimulaci zpravidla méně variabilní, a tedy spolehlivější, než na stimulaci konstantní. Diplomová práce rovněž shrnuje důležitá fakta a hypotézy týkající se neuronálního kódování a olfakce u Lepidoptera.Department of PhysiologyKatedra fyziologiePřírodovědecká fakultaFaculty of Scienc

Scout motor performance analysis and prediction study /PAPS/

Scout motor performance analysis and predictio

A modeling study of the history-dependence of conduction delay in unmyelinated axons

Conduction delay in an axon is the time required for an action potential to propagate between two positions. It is a function of the axon’s passive membrane properties, voltage-gated ion channels and the Na+/K+ pump, and can be substantially affected by neuromodulators. The conduction delay of action potential, generated by the pyloric dilator (PD) neuron unmyel i nated motor axon in the stomatogastric nervous system, shows significant variability with ongoing bursting or Poisson stimulation. When the axon is stimulated, the mean value (Dmean) and coefficient variation of conduction delay (CV-D) slowly increase with time (slow timescale effect), and the relationship between delay and instantaneous stimulus frequency (Fi nst) is non-monotonic (fast timescale effect). This dissertation investigates how the history-dependence of conduction delay is generated and the contributions of different ionic currents to conduction delay. This dissertation is comprised of three parts. In the first part, we build a biophysical model that includes several characterized ionic currents and the Na+/K+ pump in order to unmask the mechanisms underlying the history dependence of conduction delay. This model captures both the slow and fast timescale effects of conduction delay obtained from the realistic burst stimulation and Poisson stimulation at different mean frequencies. Additionally, the effects of a neuromodulator (dopamine) and a channel blocker (CsCl) on the history-dependence of conduction delay were also accurately captured by the biophysical model. Specifically, the Na+/K+ pump plays a critical role in the slow increase of Dmean and CV-D. At the fast timescale, the non-monotonic relationship between conduction delay and Finst is captured by the dynamical properties of INa. Furthermore, we systematically investigated the contributions of different ionic currents on conduction delay and spike shape parameters (i.e., duration, trough and peak voltages) with realistic burst stimulation protocols. Specifically, we found that only INa substantially affects the variability of conduction delay. Based on this observation, in the second part of the dissertation, we intended to use the dynamical parameters of INa to build an equation to accurately predict the variability of conduction delay. We found that conduction delay is mostly determined by the opening rate of the Na+ activation variable prior to the action potential (αm(VT)), and the closing rate of its inactivation variable at the peak (flh(VP)). Consequently, we developed an empirical equation for conduction delay in our model using multivariate linear regression of the Poisson stimulation data. The resulting equation accurately predicted the history-dependence of conduction delay on novel data. In our model data both αm and βh are almost linear functions of their respective voltage variables (VT and VP) in the voltage ranges observed. We, therefore, simplified our empirical equation and the new equation can also accurately predict the history dependence of conduction delayin the model. More importantly, it provides accurate predictions of conduction delay from experimental measurements of action potential voltage trajectories in the motor axon without need of computational modeling. In the third and final part of the dissertation, I will develop a decoding technique to investigate the functional relationship between conduction delay and the history activity in the PD axon. Using biological data obtained from representative experiments of the PD axon with Poisson stimulation, all the parameters in the decoding technique are determined after a routine optimization process. With these optimized parameters, the decoding model can accurately predict the conduction delay only from the stimulus time. A similar technique is developed and applied to explore and predict the voltage facilitation exposed by the cpv2-a muscle. These results show that conduction delay is affected by the short- and long-term history activity in the PD axon. The conductance-based biophysical model, the empirical equations and the decoding technique, which were developed in this dissertation, provide quantitative tools to explore the mechanisms of history-dependence of conduction delay, and predict conduction delay both in the model results and in the experimental measurements

TERA for Rotating Equipment Selection

This thesis looks at creating a multidisciplinary simulation tool for rotating plant equipment selection, specifically gas turbines, for the liquefaction of natural gas (LNG). This is a collaborative project between Shell Global Solutions and Cranfield University in the UK. The TERA LNG tool uses a Techno-economic, Environmental and Risk Analysis (TERA) approach in order to satisfy the multidisciplinary nature of the investigation. The benefits of the tool are to act as an aid to selection, operations and maintenance planning and it also acts as a sensitivity tool for assessing the impact of changes in performance, environmental and financial parameters to the overall economic impact of technology selection. The aim is to not only select technology on the basis of techno-economics but also on the basis of risk analysis. The LNG TERA tool is composed of a number of modules starting with the performance simulation which calculates the thermodynamic conditions in the core of the engine. Next, life estimates of the hot gas path components are made using a mixture of parametric and probabilistic lifing models for the turbine first stage blades, coatings, and combustor liner. This allows for a risk analysis to be conducted before maintenance and economics issues are dealt with. In parallel, emissions estimations are made based on empirical correlations. The modelling exemplifies a methodology which is uniquely applied to this application and there are no studies previous to this which look at so many aspects before making conclusions on plant machinery selection. Comparisons have been done between industrial frame engines based on the General Electric Frame 9E (130 MW) and Frame 7EA (87 MW) engines as well as more complex cycles involving aero-derivation and inter-cooling such as the LM 6000 (42 MW) and LMS 100 (100 MW). Work has also been carried out to integrate the tool to Shell based systems in order to utilise the database of information on failure and maintenance of machinery as well as its performance. The results of the integrated TERA show a clear favour for the aero-derivative engines and the main benefit is the fuel saving, though the life of the hot gas path components is deteriorated much faster. The risk results show that the industrial frame engines have a wider variation in expected life compared to aero-derivatives, though the industrial frames have longer component lives. In the context of maintenance and economics, the aero-derivative engines are better suited to LNG applications. The modular change out design of the aero- derivatives also meant that time to repair was lower, thus reducing lost production. Application of the LNG TERA tool was extended to power generation whereby a series of 6 engines were simulated. The changes required to the modelling were minimal and it shows the flexibility of the TERA philosophy. This study was carried out assuming a given ratio of load split between the engines and hence is sensitive to the way an operator demands power of the engine as opposed to LNG application where the operator tries to drive the engine as hard as possible to get the most production out of the train. The study was limited in the modes of failure which were investigated, a major further work would be to extend the methodology to more components and incorporate fatigue failure. Further, the blade creep and probabilistic coating models were very sensitive to changes in their respective control parameters such as coating thickness allowances and firing temperature. The contribution to the project from the MBA is the statistical techniques used to conduct the risk analysis and data handling as well as financial management techniques such as the Net Present Value (NPV) methodology for project evaluations

Investigating the feasibility of characterising gasoline autoignition using a motored engine apparatus

Development of a predictive octane model is a potentially useful tool for designing fuel blends for meeting octane specifications. One of the approaches adopted is through chemical kinetic modelling of the autoignition properties of constituent compounds. The results obtained from models, however, are dependent on experimental data for validation. It was the intention of this thesis to provide empirical data that could be used confirm a recently proposed autoignition model based upon the results obtained from chemical kinetics modelling. Motored engines have been used extensively for the investigation of autoignition properties of fuels. They are useful in interpreting results from conventional ignition delay measuring systems as well as giving practical insight into the process of autoignition in spark ignition engines. The conditions required for autoignition reactions to take place are easily produced in a motored engine with a suitable compression ratio. A single cylinder engine was modified so that the inlet conditions could be adjusted and n-heptane was tested in the device. Fuelling was controlled with an injection system which was calibrated for n-heptane use in the engine. A range of inlet conditions were determined that would enable peak conditions in the engine to result in autoignition of the fuel. The autoignition data was then used in describing the ignition delay characteristics of the fuel and the range of interest, the so called negative temperature coefficient region. Autoignition experiments were performed in the engine and the data was analysed by the comparison of measured autoignition reactions with predicted reaction times; the predictions were calculated using the new empirical autoignition model. Direct analysis of the model resulted in good correlation of measured and predicted overall autoignition reaction times, with improved correlation of cool flame reaction times with initial temperature adjustment. Modification of initial temperature values in the indirect model application (whereby traces were generated using an engine model with autoignition prediction capabilities) resulted in similar observances. These initial results led to the conclusion that the temperature and Arrhenius parameter adjustments necessary to obtain a perfect fit in the autoignition model were indicative of errors involved in the temperature measurement or in the fuel metering. Recommendations for further work on the engine would be the investigation of a dynamometer system that would be free from noise transmission during operation and that would enable experimentation with lower engine speeds. Further work on the inlet system would be the installation of shielded thermocouples and a quicker acting heater controller. A fundamental change in fuel metering calibration is required. Further recommendation is that a variable compression ratio engine should be used to enable the attainment of a wider range of readings for fuel characterisation and possibly eradicate the problems experienced with fuelling

On the Origin of Sensory Errors

Estimation of perceptual variables is imprecise and prone to errors. Although the properties of these perceptual errors are well characterized, the physiological basis for these errors is unknown. One previously proposed explanation for these errors is the trial-by-trial variability of the responses of sensory neurons that encode the percept. Initially, it would seem that a complicated electrophysiological experiment would need to be performed to test this hypothesis. However, using a strong theoretical framework, I demonstrate that it is possible to determine statistical characteristics of the physiological mechanism responsible for perceptual errors solely from a behavioral experiment. The basis for this theoretical framework is that different stochastic distributions (e.g., Poisson, Gaussian, etc.) will behave differently under temporal constraints. The results of this model connect easily with existing psychophysical techniques; additionally, I extend the theory here and show that it can generate realistic tuning curves that can predict perceptual acuity as a function of stimulus magnitude and duration. Following the analytical work, I performed the necessary experiments to test the model. I demonstrate that the physiological basis of perceptual error has a constant level of noise (i.e., independent of stimulus intensity and duration). By comparing these results to previous physiological measurements, I show that perceptual errors cannot be due to the variability during the encoding stage. Further, I show a very close fit between the theoretically generated tuning curve and the behavioral results, which gives more insight into the error generation mechanism. Finally, I find that the time window over which perceptual evidence is integrated lasts no more that ~230ms. I discuss these results and others, and speculate on sources of error that may be consistent with my behavioral measurements