An Introduction to Transfer Entropy: Information Flow in Complex Systems

An Introduction to Transfer Entropy : Information Flow in Complex Systems
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Information Flow in Complex Systems

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We highlight a simple form of Transfer Entropy where conditioning is minimal such that 5 The idea is that, if causes at causal lag , then for any lag since due to the fact that should provide the most information about the change of to. Smirnov, D. Embed Size px. The same increase of entropy results from a quasistatic irreversible variant of the Joule expansion. Direct access to the library catalogue.

Description This book considers a relatively new metric in complex systems, transfer entropy, derived from a series of measurements, usually a time series. After a qualitative introduction and a chapter that explains the key ideas from statistics required to understand the text, the authors then present information theory and transfer entropy in depth.

Six types of interacting oscillators, including phase-amplitude, amplitude-amplitude, and component-amplitude cross-frequency transfer entropy as well as their inverse transfer entropies, are within our scope in untangling the brain connectivity.

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Challenges with nonlinear and nonstationary patterns are designed to validate the robustness of the proposed method. We suggest this approach could be effective in identifying driving and responding elements of interacting oscillators across different time scales.

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Authors: Bossomaier, T., Barnett, L., Harré, M., Lizier, J.T. This book considers a relatively new metric in complex systems, transfer entropy, derived from a series of measurements, usually a time series. After a qualitative introduction and a chapter that explains the key ideas. Editorial Reviews. From the Back Cover. This book considers a relatively new metric in complex An Introduction to Transfer Entropy: Information Flow in Complex Systems 1st ed. Edition, Kindle Edition. by Terry Bossomaier ( Author).

Meanwhile, an atlas of interacting oscillators in sleep is constructed. High-frequency amplitude can inversely drive low-frequency phase stronger than the standard phase-amplitude coupling, and the low-frequency amplitude can be the driving force to the high-frequency amplitude in addition to the low-frequency phase.

Estimating the Information Flow in Deep Neural Networks

Unlike the standard phase-amplitude coupling, the proposed cross-frequency transfer entropy is applicable to quantify the interactions across phases, amplitudes, or even the components without methodological adjustments. Meanwhile, the exploration of causal relationship enables the identification of the driving force of information flow. Article :. Date of Publication: 28 June DOI: