1,067 research outputs found
Detecting Determinism in High Dimensional Chaotic Systems
A method based upon the statistical evaluation of the differentiability of
the measure along the trajectory is used to identify in high dimensional
systems. The results show that the method is suitable for discriminating
stochastic from deterministic systems even if the dimension of the latter is as
high as 13. The method is shown to succeed in identifying determinism in
electro-encephalogram signals simulated by means of a high dimensional system.Comment: 8 pages (RevTeX 3 style), 5 EPS figures, submitted to Phys. Rev. E
(25 apr 2001
Can we identify non-stationary dynamics of trial-to-trial variability?"
Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings
The human ECG - nonlinear deterministic versus stochastic aspects
We discuss aspects of randomness and of determinism in electrocardiographic
signals. In particular, we take a critical look at attempts to apply methods of
nonlinear time series analysis derived from the theory of deterministic
dynamical systems. We will argue that deterministic chaos is not a likely
explanation for the short time variablity of the inter-beat interval times,
except for certain pathologies. Conversely, densely sampled full ECG recordings
possess properties typical of deterministic signals. In the latter case,
methods of deterministic nonlinear time series analysis can yield new insights.Comment: 6 pages, 9 PS figure
Fast detection of nonlinearity and nonstationarity in short and noisy time series
We introduce a statistical method to detect nonlinearity and nonstationarity
in time series, that works even for short sequences and in presence of noise.
The method has a discrimination power similar to that of the most advanced
estimators on the market, yet it depends only on one parameter, is easier to
implement and faster. Applications to real data sets reject the null hypothesis
of an underlying stationary linear stochastic process with a higher confidence
interval than the best known nonlinear discriminators up to date.Comment: 5 pages, 6 figure
Efficient transfer entropy analysis of non-stationary neural time series
Information theory allows us to investigate information processing in neural
systems in terms of information transfer, storage and modification. Especially
the measure of information transfer, transfer entropy, has seen a dramatic
surge of interest in neuroscience. Estimating transfer entropy from two
processes requires the observation of multiple realizations of these processes
to estimate associated probability density functions. To obtain these
observations, available estimators assume stationarity of processes to allow
pooling of observations over time. This assumption however, is a major obstacle
to the application of these estimators in neuroscience as observed processes
are often non-stationary. As a solution, Gomez-Herrero and colleagues
theoretically showed that the stationarity assumption may be avoided by
estimating transfer entropy from an ensemble of realizations. Such an ensemble
is often readily available in neuroscience experiments in the form of
experimental trials. Thus, in this work we combine the ensemble method with a
recently proposed transfer entropy estimator to make transfer entropy
estimation applicable to non-stationary time series. We present an efficient
implementation of the approach that deals with the increased computational
demand of the ensemble method's practical application. In particular, we use a
massively parallel implementation for a graphics processing unit to handle the
computationally most heavy aspects of the ensemble method. We test the
performance and robustness of our implementation on data from simulated
stochastic processes and demonstrate the method's applicability to
magnetoencephalographic data. While we mainly evaluate the proposed method for
neuroscientific data, we expect it to be applicable in a variety of fields that
are concerned with the analysis of information transfer in complex biological,
social, and artificial systems.Comment: 27 pages, 7 figures, submitted to PLOS ON
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