Short Memory Time Research Articles

Time-invariant feed-forward inhibition of Purkinje cells in the cerebellar cortex in vivo.

We performed extracellular recording of pairs of interneuron-Purkinje cells in vivo. A single interneuron produces a substantial, short-lasting, inhibition of Purkinje cells. Feed-forward inhibition is associated with characteristic asymmetric cross-correlograms. In vivo, Purkinje cell spikes only depend on the most recent synaptic activity. Cerebellar molecular layer interneurons are considered to control the firing rate and spike timing of Purkinje cells. However, interactions between these cell types are largely unexplored in vivo. Using tetrodes, we performed simultaneous extracellular recordings of neighbouring Purkinje cells and molecular layer interneurons, presumably basket cells, in adult rats in vivo. The high levels of afferent synaptic activity encountered in vivo yield irregular spiking and reveal discharge patterns characteristic of feed-forward inhibition, thus suggesting an overlap of the afferent excitatory inputs between Purkinje cells and basket cells. Under conditions of intense background synaptic inputs, interneuron spikes exert a short-lasting inhibitory effect, delaying the following Purkinje cell spike by an amount remarkably independent of the Purkinje cell firing cycle. This effect can be explained by the short memory time of the Purkinje cell potential as a result of the intense incoming synaptic activity. Finally, we found little evidence for any involvement of the interneurons that we recorded with the cerebellar high-frequency oscillations promoting Purkinje cell synchrony. The rapid interactions between interneurons and Purkinje cells might be of particular importance in fine motor control because the inhibitory action of interneurons on Purkinje cells leads to deep cerebellar nuclear disinhibition and hence increased cerebellar output.

Memory properties of transformations of linear processes

In this paper, we study the memory properties of transformations of linear processes. Dittmann and Granger (2002) studied the polynomial transformations of Gaussian FARIMA(0,d,0) processes by applying the orthonormality of the Hermite polynomials under the measure for the standard normal distribution. Nevertheless, the orthogonality does not hold for transformations of non-Gaussian linear processes. Instead, we use the decomposition developed by Ho and Hsing (1996, 1997) to study the memory properties of nonlinear transformations of linear processes, which include the FARIMA(p,d,q) processes, and obtain consistent results as in the Gaussian case. In particular, for stationary processes, the transformations of short-memory time series still have short-memory and the transformation of long-memory time series may have different weaker memory parameters which depend on the power rank of the transformation. On the other hand, the memory properties of transformations of non-stationary time series may not depend on the power ranks of the transformations. This study has application in econometrics and financial data analysis when the time series observations have non-Gaussian heavy tails. As an example, the memory properties of call option processes at different strike prices are discussed in details.

SmFRET and DEER Distance Measurements as Applied to Disordered and Structured Proteins

FRET and DEER are two spectroscopic methods that are widely applied for biophysical studies of protein structure. Both methods are based on measuring dipolar interactions - electrical dipoles in case of FRET and magnetic dipoles in case of DEER - between specifically labeled protein sites. The experimental data are then analyzed to derive the distance between the interacting dipoles and relate this distance to the structure of biomacromolecule(s). Molecular volume of EPR labels is generally smaller vs. that of the fluorescent probes and DEER experiments can be carried out by labeling cysteines with identical molecular tags whereas FRET typically relies on orthogonal labeling with distinct donor and acceptor fluorophores. Another essential difference is that FRET can be performed under physiological conditions, but DEER typically requires cryogenic or near cryogenic temperatures because of short phase memory time for nitroxides at ambient conditions. Finally, single molecule (sm) FRET reports on conformation of individual protein molecules whereas DEER provides information on ensemble average. While the distance ranges of these two methods overlaps the direct comparison of FRET and DEER data is rarely found in the literature. Here we report on the distance measurements and conformational states using both smFRET and DEER on three protein systems. We attached probes to a unique pair of cysteines in the neuronal SNARE protein SNAP-25. SNAP-25 is highly disordered in isolation, but it folds into a stable alpha-helix bundle upon forming SNARE complex with syntaxin and synaptobrevin. We also labeled intrinsically disordered Glutamate Receptor Cytoplasmic Domain N2B. Results of smFRET and DEER distances and distance distribution are compared for disordered SNAP-25 and folded SNAP-25 within the SNARE complex and for disordered N2B.

Parameter Estimation Robust to Low-Frequency Contamination

We provide methods to robustly estimate the parameters of stationary ergodic short-memory time series models in the potential presence of additive low-frequency contamination. The types of contamination covered include level shifts (changes in mean) and monotone or smooth time trends, both of which have been shown to bias parameter estimates towards regions of persistence in a variety of contexts. The estimators presented here minimize trimmed frequency domain quasi maximum likelihood (FDQML) objective functions without requiring specification of the low frequency contaminating component. When proper sample size-dependent trimmings are used, the FDQML estimators are consistent and asymptotically normal, asymptotically eliminating the presence of any spurious persistence. These asymptotic results also hold in the absence of additive low-frequency contamination, enabling the practitioner to robustly estimate model parameters without prior knowledge of whether contamination is present. Popular time series models that fit into the framework of this article include ARMA, stochastic volatility, GARCH and ARCH models. We explore the finite sample properties of the trimmed FDQML estimators of the parameters of some of these models, providing practical guidance on trimming choice. Empirical estimation results suggest that a large portion of the apparent persistence in certain volatility time series may indeed be spurious.

Copulas, Chaotic Processes and Time Series: a Survey

In this work we summarize some of recent and classical results on the role played by copulas in the analysis of chaotic processes and univariate time series. We review some aspects of the copulas related to chaotic process, its properties and applications. We also present a review on classical and modern approaches to understand the relationship among random variables in Markov processes as well as short and long memory time series as well as ergodic properties of copula-based Markov processes.

Effectiveness of Mindful Meditation on Attention, Short Term Memory and Visual Reaction Time on Normal Individual

Background of the study: People who have undergone extensive meditation have shown improvement on cognitive performance .It does this by training and bringing mental processes under greater voluntary control and direction them in beneficial ways. This study was designed with the purpose to observe the effectiveness of mindful meditation on attention, short term memory and visual reaction time on normal individual. We examined whether brief mindfulness training affects on cognition. Our findings suggest that two weeks of meditation training can enhance the ability to sustain attention, short term memory and visual reaction time on normal individual. Purpose of the study: To examine whether brief mindful training affects on attention.short term memory and visual reaction time on normal indivisual. Procedure: A total no. of 230 subjects age group(15-25) were taken for the study. Pre evaluation has been done by using Trail making test, Digit span test and Human Benchmark test. Trail making test for attention, Digit span test for short term memory and Human Benchmark test for visual reaction time. These test were carried out to the subjects for pre and post Evaluation. Mindful meditation was provided to subjects for 15 minutes once a day for two weeks. Subject were made to sit comfortably on a mat and asked them to take a deep breath before meditation. Each subject is given a mantra (OM) and is instructed to concentrate through this mantra(OM). Results: The data was statically analyzed by using SPSS software. There is overall increased attention, increase short term memory and decrease reaction time were shown. The P- Value of TMTA1-TMTA2- .000 , TMTB1-TMTB2- .000 , RT1-RT2 - .007 , DTI-DT2 -.000 . The value is < .05 as seen in table-3 so this study accepts the alternate hypothesis. Conclusion: Our findings suggests that two weeks of meditation training can enhance the ability to sustain Attention ,Short term Memory and visual reaction time on normal individual.

On the Bartlett correction of empirical likelihood for Gaussian long-memory time series

Bartlett correction is one of the desirable features of empirical likelihood (EL) since it allows constructions of confidence regions with improved coverage probabilities. Previous studies demonstrated the Bartlett correction of EL for independent observations and for short-memory time series. By establishing the validity of Edgeworth expansion for the signed root empirical log-likelihood ratio, the validity of Bartlett correction of EL for Gaussian long-memory time series is established. In particular, orders of the coverage error of confidence regions can be reduced from $\log^{6}n/n$ to $\log^{3}n/n$, which is different from the classical rate of reduction from $n^{-1}$ to $n^{-2}$.

Real-Time Prediction of Near-Future Traffic States on Freeways Using a Markov Model

A method is proposed to predict the state of traffic for the near future. Traffic conditions are assumed to follow a stochastic process. A Markov model is developed to characterize the transition between traffic states. Unlike previous models in the literature, the state transition probability matrix is assumed to be a function of traffic variables; therefore, the proposed Markov model considers time-varying covariates. The base transition matrix and the effect of each covariate are calibrated to a data set for an urban expressway in Toronto, Ontario, Canada, by using maximum likelihood estimation. Using the transition probabilities of the Markov model, the proposed procedure constructs the empirical distribution of travel speed. The procedure, which can be applied in real time, uses both the empirical distribution of travel speed for different traffic conditions and the predicted transition matrix for the near future. Therefore, the proposed method enables the prediction of both the expected speed value and its distribution for the near future. Finally, a procedure is proposed to improve the prediction results of any travel time prediction method. This procedure uses a short-memory time series model by incorporating the predicted transition probabilities of the proposed Markov model. An evaluation using field data demonstrates this improvement for a simple time series model.

Distribution theory for the studentized mean for long, short, and negative memory time series

We consider the problem of estimating the variance of the partial sums of a stationary time series that has either long memory, short memory, negative/intermediate memory, or is the first-difference of such a process. The rate of growth of this variance depends crucially on the type of memory, and we present results on the behavior of tapered sums of sample autocovariances in this context when the bandwidth vanishes asymptotically. We also present asymptotic results for the case that the bandwidth is a fixed proportion of sample size, extending known results to the case of flat-top tapers. We adopt the fixed proportion bandwidth perspective in our empirical section, presenting two methods for estimating the limiting critical values—both the subsampling method and a plug-in approach. Simulation studies compare the size and power of both approaches as applied to hypothesis testing for the mean. Both methods perform well–although the subsampling method appears to be better sized–and provide a viable framework for conducting inference for the mean. In summary, we supply a unified asymptotic theory that covers all different types of memory under a single umbrella.

Generalized Langevin equation with multiplicative trichotomous noise

The influence of noise flatness and memory-time on the dynamics of a generalized Langevin system driven by an internal Mittag-Leffler noise and by a multiplicative trichotomous noise is studied. In the asymptotic limit at a short memory time the dynamics corresponds to a system with a pure power-law memory kernel for a viscoelastic type friction. However, at long and intermediate memory times the behaviour of the system has a qualitative difference. In particular, a critical memory time and a critical memory exponent have been found, which mark dynamical transitions in the resonant behaviour of the system. The obtained results show that the model considered is quite robust and may be of interest also in cell biology.

Near-IR Electrochromism in Electropolymerized Films of a Biscyclometalated Ruthenium Complex Bridged by 1,2,4,5-Tetra(2-pyridyl)benzene

Reductive electropolymerization of the biscyclometalated ruthenium complex [(vtpy)Ru(tpb)Ru(vtpy)](2+) [vtpy = 4'-vinyl-2,2':6',2″-terpyridine; tpb = 1,2,4,5-tetra(2-pyridyl)benzene] proceeded smoothly on electrode surfaces. Thanks to the strong electron coupling between the ruthenium centers of the individual monomeric units and strong intervalence charge-transfer absorption in the mixed valence state, the produced adherent metallopolymeric films exhibited near-IR electrochromism with tricolor switching, good contrast ratio (40% at 1165 nm), short response time, low-switching voltage, and long memory time.

Correction: Residual empirical processes for long and short memory time series

Remark 3.1. This corollary reflects the effects of the slower convergence rate of the estimated parameter a$n. This fact serves as a reminiscence of the classical Kolmogorov-Smirnov statistics problem when the underlying parame? ters are estimated; see Durbin (1976). When cxq is known, the test statistic (1.5) is still valid, however. As pointed out by the reviewer, when F ? F(x,0) involves an unknown parameter ?, one should consider Kn with F(x) being replaced by F(x,0n). When H < 1/2, it can be shown that the limit distribution of the statistic exists by means of the result of Wu (2003). The closed form of such a limit dis? tribution is rather complicated and does not possess a simple expression, however, and is not presented here.

The ARMA alphabet soup: A tour of ARMA model variants

Autoregressive moving-average (ARMA) difference equations are ubiquitous models for short memory time series and have parsimoniously described many stationary series. Variants of ARMA models have been proposed to describe more exotic series features such as long memory autocovariances, periodic autocovariances, and count support set structures. This review paper enumerates, compares, and contrasts the common variants of ARMA models in today’s literature. After the basic properties of ARMA models are reviewed, we tour ARMA variants that describe seasonal features, long memory behavior, multivariate series, changing variances (stochastic volatility) and integer counts. A list of ARMA variant acronyms is provided.

Residual empirical processes for long and short memory time series

This paper studies the residual empirical process of long- and short-memory time series regression models and establishes its uniform expansion under a general framework. The results are applied to the stochastic regression models and unstable autoregressive models. For the long-memory noise, it is shown that the limit distribution of the Kolmogorov-Smirnov test statistic studied in Ho and Hsing [Ann. Statist. 24 (1996) 992-1024] does not hold when the stochastic regression model includes an unknown intercept or when the characteristic polynomial of the unstable autoregressive model has a unit root. To this end, two new statistics are proposed to test for the distribution of the long-memory noises of stochastic regression models and unstable autoregressive models. (With Correction.)

Response probability and response time: a straight line, the Tagging/Retagging interpretation of short term memory, an operational definition of meaningfulness and short term memory time decay and search time

The functional relationship between correct response probability and response time is investigated in data sets from Rubin, Hinton and Wenzel, J Exp Psychol Learn Mem Cogn 25:1161-1176, 1999 and Anderson, J Exp Psychol [Hum Learn] 7:326-343, 1981. The two measures are linearly related through stimulus presentation lags from 0 to 594 s in the former experiment and for repeated learning of words in the latter. The Tagging/Retagging interpretation of short term memory is introduced to explain this linear relationship. At stimulus presentation the words are tagged. This tagging level drops slowly with time. When a probe word is reintroduced the tagging level has to increase for the word to be properly identified leading to a delay in response time. The tagging time is related to the meaningfulness of the words used-the more meaningful the word the longer the tagging time. After stimulus presentation the tagging level drops in a logarithmic fashion to 50% after 10 s and to 20% after 240 s. The incorrect recall and recognition times saturate in the Rubin et al. data set (they are not linear for large time lags), suggesting a limited time to search the short term memory structure: the search time for recall of unusual words is 1.7 s. For recognition of nonsense words the corresponding time is about 0.4 s, similar to the 0.243 s found in Cavanagh (1972).

Staying positive: going beyond Lindblad with perturbative master equations

The perturbative master equation (Bloch–Redfield) is used extensively to study dissipative quantum mechanics—particularly for qubits—despite the 25-year-old criticism that it violates positivity (generating negative probabilities). We take an arbitrary system coupled to an environment containing many degrees-of-freedom and cast its perturbative master equation (derived from a perturbative treatment of Nakajima–Zwanzig or Schoeller–Schön equations) in the form of a Lindblad master equation. We find that the equation's parameters are time dependent. This time dependence is rarely accounted for and invalidates Lindblad's dynamical semigroup analysis. We analyse one such Bloch–Redfield master equation (for a two-level system coupled to an environment with a short but non-vanishing memory time), which apparently violates positivity. We analytically show that, once the time dependence of the parameters is accounted for, positivity is preserved.

Hamiltonian and Brownian systems with long-range interactions: III. The BBGKY hierarchy for spatially inhomogeneous systems

We study the growth of correlations in systems with weak long-range interactions. Starting from the BBGKY hierarchy, we determine the evolution of the two-body correlation function by using an expansion of the solutions of the hierarchy in powers of 1 / N in a proper thermodynamic limit N → + ∞ , where N is the number of particles. These correlations are responsible for the “collisional” evolution of the system beyond the Vlasov regime due to finite N effects. We obtain a general kinetic equation that can be applied to spatially inhomogeneous systems and that takes into account memory effects. These peculiarities are specific to systems with unshielded long-range interactions. For spatially homogeneous systems with short memory time like plasmas, we recover the classical Landau (or Lenard–Balescu) equations. An interest of our approach is to develop a formalism that remains in physical space (instead of Fourier space) and that can deal with spatially inhomogeneous systems. This enlightens the basic physics and provides novel kinetic equations with a clear physical interpretation. However, unless we restrict ourselves to spatially homogeneous systems, closed kinetic equations can be obtained only if we ignore some collective effects between particles. General exact coupled equations taking into account collective effects are also given. We use this kinetic theory to discuss the processes of violent collisionless relaxation and slow collisional relaxation in systems with weak long-range interactions. In particular, we investigate the dependence of the relaxation time with the system size N and try to provide a coherent discussion of all the numerical results obtained for these systems.

Random field priors for spectral density functions

In this paper we discuss how a Gaussian random field with Matérn covariance function can represent our prior uncertainty about the log-spectral density, g ( ω ) , of a Gaussian, short memory time series. Hyperparameters can be suitably tuned in order to determine the mean square differentiability and the range of autocorrelation of the random field g ( ω ) . However, Bayesian computations cannot be easily performed under such prior elicitations. We suggest therefore to approximate the Gaussian random field priors with a class of Gaussian Markov random fields which preserve the main features of the genuine prior distributions.

Stochastic mean-field dynamics for fermions in the weak-coupling limit

Assuming that the effect of the residual interaction beyond mean-field is weak and has a short memory time, two approximate treatments of correlation in fermionic systems by means of Markovian quantum jump are presented. A simplified scenario for the introduction of fluctuations beyond mean-field is first presented. In this theory, part of the quantum correlations between the residual interaction and the one-body density matrix are neglected and jumps occur between many-body densities formed of pairs of states $D=| \Phi_a > < \Phi_b |/< \Phi_b . |\Phi_a >$ where $| \Phi_a >$ and $| \Phi_b >$ are antisymmetrized products of single-particle states. The underlying Stochastic Mean-Field (SMF) theory is discussed and applied to the monopole vibration of a spherical $^{40}$Ca nucleus under the influence of a statistical ensemble of two-body contact interaction. This framework is however too simplistic to account for both fluctuation and dissipation. In the second part of this work, an alternative quantum jump method is obtained without making the approximation on quantum correlations. Restricting to two particles-two holes residual interaction, the evolution of the one-body density matrix of a correlated system is transformed into a Lindblad equation. The associated dissipative dynamics can be simulated by quantum jumps between densities written as $D = | \Phi >< \Phi |$ where $| \Phi >$ is a normalized Slater determinant. The associated stochastic Schroedinger equation for single-particle wave-functions is given.

The Environmental Long-Memory Space-Time Series Prediction

Temporal predictions by using long memory time series models have recently attracted much attention. In real surveys, such as those on environmental pollution, the observed data have long memory characteristics. In particular, the time series of pollution usually show “persistence” in the sense that their correlation functions decrease to zero at a much slower rate than the exponential rate implied by a short memory time series. In the literature, several contributions for the multistep prediction of univariate long memory time series have been proposed. However, in pollution studies, the phenomena are generally observed in several points of a study area so that a space-time series is available. In this context, we consider a multivariate extension of the univariate methodology in order to develop a multistep long memory space-time series prediction. For an easier evaluation of the procedure proposed, we focus our attention on bivariate long memory space-time series in ecological framework. The two proposed approaches of multistep prediction of the concentration of carbon monoxide in the Bergamo (Italy)-District are compared.