Introduction to Information Thermodynamics on Causal Networks
This presents the main purpose of this thesis. In this thesis, we mainly discuss the relationship between nonequilibrium thermodynamics and information theory from the view point of Maxwell’s demon which is the thought experiment in the 19th century. As a
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Introduction to Information Thermodynamics on Causal Networks
Abstract This presents the main purpose of this thesis. In this thesis, we mainly discuss the relationship between nonequilibrium thermodynamics and information theory from the view point of Maxwell’s demon which is the thought experiment in the 19th century. As a generalization of the study of Maxwell’s demon, we propose the graphical thermodynamic theory of information processing which is applicable to quite a broad class of nonequilibrium dynamics. Characterizing the complex dynamics by the Bayesian networks, we obtain a nobel generalization of the second law of thermodynamics with complex information flow. We also discuss the biophysical meaning of the information flow inside the cell as an application of its theory. At the end of this chapetr, we summarize the organization of this thesis. Keywords Stochastic thermodynamics · Signal transduction · Information theory · Information thermodynamics · Causal networks After the publication of Shannon’s influential paper about an artificial communication [1], the importance of information theory has been increasing and several fields of informational study has been emerging [2, 3]. Our study in this thesis is a challenge for developing a novel field of physics with information, so-called information thermodynamics as a fundamental theory of nonequilibrium physics including biophysics. Nowadays, we can see information device such as a computer everywhere. On the basis of Shannon’s information theory, the information quantity such as the mutual information gives the coding redundancy and the accuracy of information transmission in artificial channel coding [1, 2]. From the viewpoint of the artificial information transmission, the classical information theory has been well established, and we can quantitatively discuss the efficiency of coding and the accuracy of information transmission using the entropic quantities. The classical theory of communication (i.e., the noisy-channel coding theorem) is completely based on the assumption of the existence of artificial coding devices (i.e., the encoder and the decoder). Without artificial channel coding, the physical meaning of informational quantity is elusive in terms of the accuracy of signal transmission. The non-existence of channel coding is crucial in living systems. For example, the biochemical signal transduction network inside or outside cells is an example of nonequilibrium fluctuating dynamics, which © Springer Science+Business Media Singapore 2016 S. Ito, Information Thermodynamics on Causal Networks and its Application to Biochemical Signal Transduction, Springer Theses, DOI 10.1007/978-981-10-1664-6_1
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1 Introduction to Information Thermodynamics on Causal Networks
describes information transmission without artificial coding devices [4, 5]. Many researchers intuitively believe the importance of information flow in biochemical system to maintain life, and several studies have tried out to reveal the role of the information transfer on biochemi
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