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IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks
The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using information…
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Efficient Transfer Entropy Analysis of Non-Stationary Neural Time Series
TLDR
This work combines the ensemble method with a recently proposed transfer entropy estimator to make transfer entropy estimation applicable to non-stationary time series and tests the performance and robustness of the implementation on data from numerical simulations of stochastic processes.
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Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing
TLDR
The algorithm presented—as implemented in the IDTxl open-source software—addresses challenges by employing hierarchical statistical tests to control the family-wise error rate and to allow for efficient parallelization, and was validated on synthetic datasets involving random networks of increasing size.
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Reduced predictable information in brain signals in autism spectrum disorder
TLDR
The study suggests the usefulness of AIS to detect an abnormal type of dynamics in ASD, and the observed changes in AIS are compatible with Bayesian theories of reduced use or precision of priors in ASD.
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Breakdown of local information processing may underlie isoflurane anesthesia effects
TLDR
It is suggested that source entropy changes must be considered whenever interpreting changes in information transfer as decoupling, as this conclusion fits the observation that the synaptic targets of isoflurane are located in local cortical circuits rather than on the synapses formed by interareal axonal projections.
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Quantifying Information Modification in Developing Neural Networks via Partial Information Decomposition
TLDR
It is found that modification rose with maturation, but ultimately collapsed when redundant information among neurons took over, indicating that this particular developing neural system initially developed intricate processing capabilities but ultimately displayed information processing that was highly similar across neurons, possibly due to a lack of external inputs.
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Information-Theoretic Evidence for Predictive Coding in the Face-Processing System
TLDR
Using a novel, assumption-free approach, it is found that face-related prior knowledge and the derived predictions are represented in low-frequency brain activity.
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Anesthesia-related changes in information transfer may be caused by reduction in local information generation
TLDR
In anesthesia research it is an open question how general anesthetics lead to loss of consciousness (LOC), and recent studies investigating information processing under anesthesia focused on changes in information transfer, measured by transfer entropy (TE).
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Measuring spectrally-resolved information transfer
TLDR
A novel algorithm, and its efficient implementation, is presented to identify separately frequencies sending and receiving information in a network and relies on the invertible maximum overlap discrete wavelet transform (MODWT) for the creation of surrogate data in the computation of transfer entropy.
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Learning Time-Series Data of Industrial Design Optimization using Recurrent Neural Networks
TLDR
The feasibility of recurrent neural networks for learning successful design sequences in order to predict promising next design steps in future design tasks is demonstrated.
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