2,111 research outputs found
Measuring information-transfer delays
In complex networks such as gene networks, traffic systems or brain circuits it is important to understand how long it takes for the different parts of the network to effectively influence one another. In the brain, for example, axonal delays between brain areas can amount to several tens of milliseconds, adding an intrinsic component to any timing-based processing of information. Inferring neural interaction delays is thus needed to interpret the information transfer revealed by any analysis of directed interactions across brain structures. However, a robust estimation of interaction delays from neural activity faces several challenges if modeling assumptions on interaction mechanisms are wrong or cannot be made. Here, we propose a robust estimator for neuronal interaction delays rooted in an information-theoretic framework, which allows a model-free exploration of interactions. In particular, we extend transfer entropy to account for delayed source-target interactions, while crucially retaining the conditioning on the embedded target state at the immediately previous time step. We prove that this particular extension is indeed guaranteed to identify interaction delays between two coupled systems and is the only relevant option in keeping with Wiener’s principle of causality. We demonstrate the performance of our approach in detecting interaction delays on finite data by numerical simulations of stochastic and deterministic processes, as well as on local field potential recordings. We also show the ability of the extended transfer entropy to detect the presence of multiple delays, as well as feedback loops. While evaluated on neuroscience data, we expect the estimator to be useful in other fields dealing with network dynamics
Reducing the Bias of Causality Measures
Measures of the direction and strength of the interdependence between two
time series are evaluated and modified in order to reduce the bias in the
estimation of the measures, so that they give zero values when there is no
causal effect. For this, point shuffling is employed as used in the frame of
surrogate data. This correction is not specific to a particular measure and it
is implemented here on measures based on state space reconstruction and
information measures. The performance of the causality measures and their
modifications is evaluated on simulated uncoupled and coupled dynamical systems
and for different settings of embedding dimension, time series length and noise
level. The corrected measures, and particularly the suggested corrected
transfer entropy, turn out to stabilize at the zero level in the absence of
causal effect and detect correctly the direction of information flow when it is
present. The measures are also evaluated on electroencephalograms (EEG) for the
detection of the information flow in the brain of an epileptic patient. The
performance of the measures on EEG is interpreted, in view of the results from
the simulation study.Comment: 30 pages, 12 figures, accepted to Physical Review
Reconstructing phase dynamics of oscillator networks
We generalize our recent approach to reconstruction of phase dynamics of
coupled oscillators from data [B. Kralemann et al., Phys. Rev. E, 77, 066205
(2008)] to cover the case of small networks of coupled periodic units. Starting
from the multivariate time series, we first reconstruct genuine phases and then
obtain the coupling functions in terms of these phases. The partial norms of
these coupling functions quantify directed coupling between oscillators. We
illustrate the method by different network motifs for three coupled oscillators
and for random networks of five and nine units. We also discuss nonlinear
effects in coupling.Comment: 6 pages, 5 figures, 27 reference
Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System
Peer reviewedPublisher PD
Single-trial multiwavelet coherence in application to neurophysiological time series
A method of single-trial coherence analysis is presented, through the application of continuous muldwavelets. Multiwavelets allow the construction of spectra and bivariate statistics such as coherence within single trials. Spectral estimates are made consistent through optimal time-frequency localization and smoothing. The use of multiwavelets is considered along with an alternative single-trial method prevalent in the literature, with the focus being on statistical, interpretive and computational aspects. The multiwavelet approach is shown to possess many desirable properties, including optimal conditioning, statistical descriptions and computational efficiency. The methods. are then applied to bivariate surrogate and neurophysiological data for calibration and comparative study. Neurophysiological data were recorded intracellularly from two spinal motoneurones innervating the posterior,biceps muscle during fictive locomotion in the decerebrated cat
A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data
A great improvement to the insight on brain function that we can get from
fMRI data can come from effective connectivity analysis, in which the flow of
information between even remote brain regions is inferred by the parameters of
a predictive dynamical model. As opposed to biologically inspired models, some
techniques as Granger causality (GC) are purely data-driven and rely on
statistical prediction and temporal precedence. While powerful and widely
applicable, this approach could suffer from two main limitations when applied
to BOLD fMRI data: confounding effect of hemodynamic response function (HRF)
and conditioning to a large number of variables in presence of short time
series. For task-related fMRI, neural population dynamics can be captured by
modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI
on the other hand, the absence of explicit inputs makes this task more
difficult, unless relying on some specific prior physiological hypothesis. In
order to overcome these issues and to allow a more general approach, here we
present a simple and novel blind-deconvolution technique for BOLD-fMRI signal.
Coming to the second limitation, a fully multivariate conditioning with short
and noisy data leads to computational problems due to overfitting. Furthermore,
conceptual issues arise in presence of redundancy. We thus apply partial
conditioning to a limited subset of variables in the framework of information
theory, as recently proposed. Mixing these two improvements we compare the
differences between BOLD and deconvolved BOLD level effective networks and draw
some conclusions
CaP: Cardiovascular Disease Prediction using a Delta Layer based Center Vector Activation-centric DCNN
Cardiac disease stands as a primary contributor to mortality, representing a prevalent category of chronic and life-threatening conditions. Therefore, early detection is imperative. While existing research has sought to predict heart disease (HD) through Electrocardiogram (ECG) signals, there remains room for enhancement. This study introduces a novel approach for early HD detection based on the Delta Layer with Center Vector Activation-centric Deep Convolutional Neural Network (DLCVA-DCNN) within its research framework, namely: CaP. Initially, the input ECG signals undergo preprocessing using a Weighted Covariance Kalman Filter (WCKF) to eliminate noise. Subsequently, the preprocessed data is bifurcated: one branch transforms it into a binary image, while the other decomposes the signal to identify peak segments. The decomposition employs the Bivariate Ensemble Empirical Mode Decomposition (BEEMD) method, and the Pan-Tompkins Algorithm (PTA) is applied to ascertain the highest-frequency segments. The coupling information is then extracted from these peaks. Simultaneously, depth features are extracted from the binary image. The Linear Approximate Functional Walrus Optimization Algorithm (LAFWOA) is employed to select pertinent features from the coupling and depth features. These selected features are input into the DLCVA-DCNN classifier to discriminate disease and standard signals. The experimental analysis compares the proposed methodology with conventional frameworks based on performance metrics, revealing that the proposed approach achieves higher accuracy than existing techniques
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