000078828 001__ 78828 000078828 005__ 20200716101420.0 000078828 0247_ $$2doi$$a10.1016/j.neunet.2019.03.001 000078828 0248_ $$2sideral$$a111168 000078828 037__ $$aART-2019-111168 000078828 041__ $$aeng 000078828 100__ $$0(orcid)0000-0002-3366-4706$$aAguilera, M. 000078828 245__ $$aIntegrated information in the thermodynamic limit 000078828 260__ $$c2019 000078828 5060_ $$aAccess copy available to the general public$$fUnrestricted 000078828 5203_ $$aThe capacity to integrate information is a prominent feature of biological, neural, and cognitive processes. Integrated Information Theory (IIT) provides mathematical tools for quantifying the level of integration in a system, but its computational cost generally precludes applications beyond relatively small models. In consequence, it is not yet well understood how integration scales up with the size of a system or with different temporal scales of activity, nor how a system maintains integration as it interacts with its environment. After revising some assumptions of the theory, we show for the first time how modified measures of information integration scale when a neural network becomes very large. Using kinetic Ising models and mean-field approximations, we show that information integration diverges in the thermodynamic limit at certain critical points. Moreover, by comparing different divergent tendencies of blocks that make up a system at these critical points, we can use information integration to delimit the boundary between an integrated unit and its environment. Finally, we present a model that adaptively maintains its integration despite changes in its environment by generating a critical surface where its integrity is preserved. We argue that the exploration of integrated information for these limit cases helps in addressing a variety of poorly understood questions about the organization of biological, neural, and cognitive systems. 000078828 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/TIN2016-80347-R 000078828 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000078828 590__ $$a5.535$$b2019 000078828 592__ $$a1.718$$b2019 000078828 591__ $$aNEUROSCIENCES$$b42 / 271 = 0.155$$c2019$$dQ1$$eT1 000078828 593__ $$aCognitive Neuroscience$$c2019$$dQ1 000078828 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b19 / 136 = 0.14$$c2019$$dQ1$$eT1 000078828 593__ $$aArtificial Intelligence$$c2019$$dQ1 000078828 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000078828 700__ $$aDi Paolo E.A. 000078828 773__ $$g114 (2019), 136-146$$pNeural netw.$$tNEURAL NETWORKS$$x0893-6080 000078828 8564_ $$s408874$$uhttps://zaguan.unizar.es/record/78828/files/texto_completo.pdf$$yVersión publicada 000078828 8564_ $$s111925$$uhttps://zaguan.unizar.es/record/78828/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000078828 909CO $$ooai:zaguan.unizar.es:78828$$particulos$$pdriver 000078828 951__ $$a2020-07-16-08:39:45 000078828 980__ $$aARTICLE