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Found 10 papers, 4 papers with code
Information decomposition reveals hidden high-order contributions to temporal irreversibility
Temporal irreversibility, often referred to as the arrow of time, is a fundamental concept in statistical mechanics.
Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamics
Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and skin conductance.
Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks
These findings open the door to new ways of investigating how and why learning systems employ specific information-processing strategies, and support the principle that the capacity for general-purpose learning critically relies in the system's information dynamics.
Greater than the parts: A review of the information decomposition approach to causal emergence
Emergence is a profound subject that straddles many scientific disciplines, including the formation of galaxies and how consciousness arises from the collective activity of neurons.
Towards an extended taxonomy of information dynamics via Integrated Information Decomposition
Complex systems, from the human brain to the global economy, are made of multiple elements that interact in such ways that the behaviour of the `whole' often seems to be more than what is readily explainable in terms of the `sum of the parts.'
Integrated information as a common signature of dynamical and information-processing complexity
The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanations, creating conflict and often hindering scientific progress.
Learning, compression, and leakage: Minimising classification error via meta-universal compression principles
Learning and compression are driven by the common aim of identifying and exploiting statistical regularities in data, which opens the door for fertile collaboration between these areas.
Causal blankets: Theory and algorithmic framework
We introduce a novel framework to identify perception-action loops (PALOs) directly from data based on the principles of computational mechanics.
Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data
The broad concept of emergence is instrumental in various of the most challenging open scientific questions -- yet, few quantitative theories of what constitutes emergent phenomena have been proposed.
