18551165

Source:http://linkedlifedata.com/resource/pubmed/id/18551165

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0026605, umls-concept:C0871935, umls-concept:C1533716, umls-concept:C2699782
pubmed:issue	6
pubmed:dateCreated	2008-6-13
pubmed:abstractText	This paper introduces a time- and state-dependent measure of integrated information, phi, which captures the repertoire of causal states available to a system as a whole. Specifically, phi quantifies how much information is generated (uncertainty is reduced) when a system enters a particular state through causal interactions among its elements, above and beyond the information generated independently by its parts. Such mathematical characterization is motivated by the observation that integrated information captures two key phenomenological properties of consciousness: (i) there is a large repertoire of conscious experiences so that, when one particular experience occurs, it generates a large amount of information by ruling out all the others; and (ii) this information is integrated, in that each experience appears as a whole that cannot be decomposed into independent parts. This paper extends previous work on stationary systems and applies integrated information to discrete networks as a function of their dynamics and causal architecture. An analysis of basic examples indicates the following: (i) phi varies depending on the state entered by a network, being higher if active and inactive elements are balanced and lower if the network is inactive or hyperactive. (ii) phi varies for systems with identical or similar surface dynamics depending on the underlying causal architecture, being low for systems that merely copy or replay activity states. (iii) phi varies as a function of network architecture. High phi values can be obtained by architectures that conjoin functional specialization with functional integration. Strictly modular and homogeneous systems cannot generate high phi because the former lack integration, whereas the latter lack information. Feedforward and lattice architectures are capable of generating high phi but are inefficient. (iv) In Hopfield networks, phi is low for attractor states and neutral states, but increases if the networks are optimized to achieve tension between local and global interactions. These basic examples appear to match well against neurobiological evidence concerning the neural substrates of consciousness. More generally, phi appears to be a useful metric to characterize the capacity of any physical system to integrate information.
pubmed:grant	http://linkedlifedata.com/resource/pubmed/grant/DP1 OD000579-01, http://linkedlifedata.com/resource/pubmed/grant/DP1 OD000579-02, http://linkedlifedata.com/resource/pubmed/grant/DP1 OD000579-03
pubmed:commentsCorrections	http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-10667981, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-11598490, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-11603079, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-12748642, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-14622878, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-14641936, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-15522121, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-16186019, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-16641233, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-17412290, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-17483481, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-17704812, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-18000059, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-18246972, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-6953413, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-7567985, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-7812147, http://linkedlifedata.com/resource/pubmed/commentcorrection/18551165-9345546
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/101238922
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Jun
pubmed:issn	1553-7358
pubmed:author	pubmed-author:BalduzziDavidD, pubmed-author:TononiGiulioG
pubmed:issnType	Electronic
pubmed:volume	4
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	e1000091
pubmed:dateRevised	2011-6-14
pubmed:meshHeading	pubmed-meshheading:18551165-Animals, pubmed-meshheading:18551165-Brain, pubmed-meshheading:18551165-Cognition, pubmed-meshheading:18551165-Computer Simulation, pubmed-meshheading:18551165-Consciousness, pubmed-meshheading:18551165-Humans, pubmed-meshheading:18551165-Information Storage and Retrieval, pubmed-meshheading:18551165-Models, Neurological, pubmed-meshheading:18551165-Nerve Net, pubmed-meshheading:18551165-Signal Processing, Computer-Assisted, pubmed-meshheading:18551165-Systems Integration
pubmed:year	2008
pubmed:articleTitle	Integrated information in discrete dynamical systems: motivation and theoretical framework.
pubmed:affiliation	Department of Psychiatry, University of Wisconsin, Madison, Wisconsin, USA.
pubmed:publicationType	Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural