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      From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0

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          Abstract

          This paper presents Integrated Information Theory (IIT) of consciousness 3.0, which incorporates several advances over previous formulations. IIT starts from phenomenological axioms: information says that each experience is specific – it is what it is by how it differs from alternative experiences; integration says that it is unified – irreducible to non-interdependent components; exclusion says that it has unique borders and a particular spatio-temporal grain. These axioms are formalized into postulates that prescribe how physical mechanisms, such as neurons or logic gates, must be configured to generate experience (phenomenology). The postulates are used to define intrinsic information as “differences that make a difference” within a system, and integrated information as information specified by a whole that cannot be reduced to that specified by its parts. By applying the postulates both at the level of individual mechanisms and at the level of systems of mechanisms, IIT arrives at an identity: an experience is a maximally irreducible conceptual structure ( MICS, a constellation of concepts in qualia space), and the set of elements that generates it constitutes a complex. According to IIT, a MICS specifies the quality of an experience and integrated information Φ Max its quantity. From the theory follow several results, including: a system of mechanisms may condense into a major complex and non-overlapping minor complexes; the concepts that specify the quality of an experience are always about the complex itself and relate only indirectly to the external environment; anatomical connectivity influences complexes and associated MICS; a complex can generate a MICS even if its elements are inactive; simple systems can be minimally conscious; complicated systems can be unconscious; there can be true “zombies” – unconscious feed-forward systems that are functionally equivalent to conscious complexes.

          Author Summary

          Integrated information theory (IIT) approaches the relationship between consciousness and its physical substrate by first identifying the fundamental properties of experience itself: existence, composition, information, integration, and exclusion. IIT then postulates that the physical substrate of consciousness must satisfy these very properties. We develop a detailed mathematical framework in which composition, information, integration, and exclusion are defined precisely and made operational. This allows us to establish to what extent simple systems of mechanisms, such as logic gates or neuron-like elements, can form complexes that can account for the fundamental properties of consciousness. Based on this principled approach, we show that IIT can explain many known facts about consciousness and the brain, leads to specific predictions, and allows us to infer, at least in principle, both the quantity and quality of consciousness for systems whose causal structure is known. For example, we show that some simple systems can be minimally conscious, some complicated systems can be unconscious, and two different systems can be functionally equivalent, yet one is conscious and the other one is not.

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          Multilayer feedforward networks are universal approximators

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            The free-energy principle: a unified brain theory?

            A free-energy principle has been proposed recently that accounts for action, perception and learning. This Review looks at some key brain theories in the biological (for example, neural Darwinism) and physical (for example, information theory and optimal control theory) sciences from the free-energy perspective. Crucially, one key theme runs through each of these theories - optimization. Furthermore, if we look closely at what is optimized, the same quantity keeps emerging, namely value (expected reward, expected utility) or its complement, surprise (prediction error, expected cost). This is the quantity that is optimized under the free-energy principle, which suggests that several global brain theories might be unified within a free-energy framework.
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              Approximation by superpositions of a sigmoidal function

              G. Cybenko (1989)
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                May 2014
                8 May 2014
                : 10
                : 5
                : e1003588
                Affiliations
                [1 ]Department of Psychiatry, University of Wisconsin, Madison, Wisconsin, United States of America
                [2 ]RIKEN Brain Science Institute, Wako-shi, Saitama, Japan
                Indiana University, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: GT MO LA. Performed the experiments: MO LA. Analyzed the data: MO LA. Wrote the paper: MO LA GT.

                Article
                PCOMPBIOL-D-13-02024
                10.1371/journal.pcbi.1003588
                4014402
                24811198
                8326ac41-f511-4d35-9dc5-fcf778bc0307
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 18 November 2013
                : 11 March 2014
                Page count
                Pages: 25
                Funding
                This work was supported by a Paul G. Allen Family Foundation grant, by the McDonnell Foundation, and by the Templeton World Charities Foundation (Grant #TWCF 0067/AB41). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Computational Neuroscience
                Coding Mechanisms
                Neuroscience
                Cognitive Neuroscience
                Consciousness
                Computer and Information Sciences
                Neural Networks

                Quantitative & Systems biology
                Quantitative & Systems biology

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