Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Larissa, Albantakis"'
Publikováno v:
PLoS Computational Biology, Vol 19, Iss 10, p e1011346 (2023)
The Free Energy Principle (FEP) and Integrated Information Theory (IIT) are two ambitious theoretical approaches. The first aims to make a formal framework for describing self-organizing and life-like systems in general, and the second attempts a mat
Externí odkaz:
https://doaj.org/article/4bd103ae0f54438cba3a8d6009363651
Autor:
Larissa Albantakis, Leonardo Barbosa, Graham Findlay, Matteo Grasso, Andrew M Haun, William Marshall, William G P Mayner, Alireza Zaeemzadeh, Melanie Boly, Bjørn E Juel, Shuntaro Sasai, Keiko Fujii, Isaac David, Jeremiah Hendren, Jonathan P Lang, Giulio Tononi
Publikováno v:
PLoS Computational Biology, Vol 19, Iss 10, p e1011465 (2023)
This paper presents Integrated Information Theory (IIT) 4.0. IIT aims to account for the properties of experience in physical (operational) terms. It identifies the essential properties of experience (axioms), infers the necessary and sufficient prop
Externí odkaz:
https://doaj.org/article/e70066a606554addbfaabd07967b8c0a
Publikováno v:
Entropy, Vol 25, Iss 10, p 1442 (2023)
In response to a comment by Chris Rourk on our article Computing the Integrated Information of a Quantum Mechanism, we briefly (1) consider the role of potential hybrid/classical mechanisms from the perspective of integrated information theory (IIT),
Externí odkaz:
https://doaj.org/article/6cbe314b248b4aeeb5a2403dc15ee0cc
Publikováno v:
Entropy, Vol 25, Iss 3, p 449 (2023)
Originally conceived as a theory of consciousness, integrated information theory (IIT) provides a theoretical framework intended to characterize the compositional causal information that a system, in its current state, specifies about itself. However
Externí odkaz:
https://doaj.org/article/7b5f2173de524ab48263e67d18b00cac
Autor:
William Marshall, Matteo Grasso, William G. P. Mayner, Alireza Zaeemzadeh, Leonardo S. Barbosa, Erick Chastain, Graham Findlay, Shuntaro Sasai, Larissa Albantakis, Giulio Tononi
Publikováno v:
Entropy, Vol 25, Iss 2, p 334 (2023)
Integrated information theory (IIT) starts from consciousness itself and identifies a set of properties (axioms) that are true of every conceivable experience. The axioms are translated into a set of postulates about the substrate of consciousness (c
Externí odkaz:
https://doaj.org/article/e93ee8ee713841ecb1fd9634825e0689
Autor:
Larissa Albantakis
Publikováno v:
Entropy, Vol 23, Iss 11, p 1415 (2021)
Should the internal structure of a system matter when it comes to autonomy? While there is still no consensus on a rigorous, quantifiable definition of autonomy, multiple candidate measures and related quantities have been proposed across various dis
Externí odkaz:
https://doaj.org/article/e29cc273cf0c4bb5a6aa212746a43fcf
Publikováno v:
PLoS ONE, Vol 15, Iss 2, p e0228879 (2020)
Evolving in groups can either enhance or reduce an individual's task performance. Still, we know little about the factors underlying group performance, which may be reduced to three major dimensions: (a) the individual's ability to perform a task, (b
Externí odkaz:
https://doaj.org/article/758c3365115644dba10971baba285b38
Publikováno v:
Entropy, Vol 23, Iss 3, p 362 (2021)
The Integrated Information Theory (IIT) of consciousness starts from essential phenomenological properties, which are then translated into postulates that any physical system must satisfy in order to specify the physical substrate of consciousness. W
Externí odkaz:
https://doaj.org/article/3e8b75c1bae64df4a3a17d15a06ef49f
Publikováno v:
Entropy, Vol 23, Iss 1, p 6 (2020)
Integrated information theory (IIT) provides a mathematical framework to characterize the cause-effect structure of a physical system and its amount of integrated information (Φ). An accompanying Python software package (“PyPhi”) was recently in
Externí odkaz:
https://doaj.org/article/1df860d01d644094a427af00be4bfd80
Autor:
William G P Mayner, William Marshall, Larissa Albantakis, Graham Findlay, Robert Marchman, Giulio Tononi
Publikováno v:
PLoS Computational Biology, Vol 14, Iss 7, p e1006343 (2018)
Integrated information theory provides a mathematical framework to fully characterize the cause-effect structure of a physical system. Here, we introduce PyPhi, a Python software package that implements this framework for causal analysis and unfolds
Externí odkaz:
https://doaj.org/article/bf9b998d3f074db59097928d0a3bd767