000075872 001__ 75872 000075872 005__ 20191212102144.0 000075872 0247_ $$2doi$$a10.3389/fnbot.2018.00055 000075872 0248_ $$2sideral$$a108409 000075872 037__ $$aART-2018-108409 000075872 041__ $$aeng 000075872 100__ $$0(orcid)0000-0002-3366-4706$$aAguilera, M. 000075872 245__ $$aExploring Criticality as a Generic Adaptive Mechanism 000075872 260__ $$c2018 000075872 5060_ $$aAccess copy available to the general public$$fUnrestricted 000075872 5203_ $$aThe activity of many biological and cognitive systems is not poised deep within a specific regime of activity. Instead, they operate near points of critical behavior located at the boundary between different phases. Certain authors link some of the properties of criticality with the ability of living systems to generate autonomous or intrinsically generated behavior. However, these claims remain highly speculative. In this paper, we intend to explore the connection between criticality and autonomous behavior through conceptual models that show how embodied agents may adapt themselves toward critical points. We propose to exploit maximum entropy models and their formal descriptions of indicators of criticality to present a learning model that drives generic agents toward critical points. Specifically, we derive such a learning model in an embodied Boltzmann machine by implementing a gradient ascent rule that maximizes the heat capacity of the controller in order to make the network maximally sensitive to external perturbations. We test and corroborate the model by implementing an embodied agent in the Mountain Car benchmark test, which is controlled by a Boltzmann machine that adjusts its weights according to the model. We find that the neural controller reaches an apparent point of criticality, which coincides with a transition point of the behavior of the agent between two regimes of behavior, maximizing the synergistic information between its sensors and the combination of hidden and motor neurons. Finally, we discuss the potential of our learning model to answer questions about the connection between criticality and the capabilities of living systems to autonomously generate intrinsic constraints on their behavior. We suggest that these "critical agents" are able to acquire flexible behavioral patterns that are useful for the development of successful strategies in different contexts. 000075872 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/PSI2014-62092-EXP$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2016-80347-R 000075872 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000075872 590__ $$a3.0$$b2018 000075872 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b42 / 133 = 0.316$$c2018$$dQ2$$eT1 000075872 591__ $$aROBOTICS$$b10 / 26 = 0.385$$c2018$$dQ2$$eT2 000075872 591__ $$aNEUROSCIENCES$$b127 / 266 = 0.477$$c2018$$dQ2$$eT2 000075872 592__ $$a0.603$$b2018 000075872 593__ $$aBiomedical Engineering$$c2018$$dQ2 000075872 593__ $$aArtificial Intelligence$$c2018$$dQ2 000075872 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000075872 700__ $$0(orcid)0000-0002-8263-2444$$aBedia, M.G.$$uUniversidad de Zaragoza 000075872 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf. 000075872 773__ $$g12 (2018), 55 [10 pp]$$pFront. neurorobot.$$tFrontiers in Neurorobotics$$x1662-5218 000075872 8564_ $$s521717$$uhttps://zaguan.unizar.es/record/75872/files/texto_completo.pdf$$yVersión publicada 000075872 8564_ $$s11527$$uhttps://zaguan.unizar.es/record/75872/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000075872 909CO $$ooai:zaguan.unizar.es:75872$$particulos$$pdriver 000075872 951__ $$a2019-12-12-10:13:53 000075872 980__ $$aARTICLE