Herramientas de usuario

Herramientas del sitio


proyectos:tfg:bibliografia:albantakis2017

Diferencias

Muestra las diferencias entre dos versiones de la página.

Enlace a la vista de comparación

Próxima revisión
Revisión previa
proyectos:tfg:bibliografia:albantakis2017 [2017/10/27 09:01]
127.0.0.1 editor externo
proyectos:tfg:bibliografia:albantakis2017 [2017/11/15 09:48] (actual)
Joaquín Herrero Pintado
Línea 1: Línea 1:
-====== Albantakis, L. Automata and Animats: From Dynamics to Cause–Effect Structures ======+====== Albantakis, L. Automata and Animats: From Dynamics to Cause–Effect Structures ​(2017) ​======
  
 en Imari Walker, S. (ed), 2017, From Matter to Life – Information and Causality en Imari Walker, S. (ed), 2017, From Matter to Life – Information and Causality
 +
 +
 +LARISSA ALBANTAKIS is a postdoctoral researcher with Giulio Tononi at the
 +Department of Psychiatry at University of Wisconsin–Madison. She
 +received her degree in physics with distinction at the LudwigMaximilians
 +Universitt, Munich, followed by a Ph.D. in computational
 +neuroscience at the Universitat Pompeu Fabra, Barcelona, under the
 +supervision of Gustavo Deco. Her research focuses on the theoretical
 +formulation of the integrated information theory of consciousness and its
 +implications for evolutionary adaptation, emergence, and meaning.
 +
 +In Chapter 14 by Albantakis and
 +Tononi, who consider the distinction between ‘being’ and ‘happening’,​
 +utilising cellular automata (CA) as a case study. Most prior work on
 +dynamical systems, including CA, focuses on what is ‘happening’ – the
 +dynamical trajectory of the system through its state space – that is, they take
 +an extrinsic perspective on what is observed. Often, complexity is
 +characterised using statistical methods and information theory. In a shift of
 +focus to that of causal architecture,​ Albantakis and Tononi consider what the
 +system ‘is’ from its own intrinsic perspective,​ utilising the machinery of
 +integrated information theory (IIT), and demonstrate that intrinsic (causal)
 +complexity (as quantified by integrated information Φ in IIT) correlates well
 +with dynamical (statistical) complexity in the examples discussed. These and
 +similar approaches could provide a path forward for a deeper understanding of the connection between causation and information as hinted at in the
 +beginning of this chapter.
 +
proyectos/tfg/bibliografia/albantakis2017.1509094897.txt.gz · Última modificación: 2017/11/08 02:19 (editor externo)