An Introduction to Transfer Entropy: Information Flow in by Terry Bossomaier, Lionel Barnett, Michael Harré, Joseph T.

By Terry Bossomaier, Lionel Barnett, Michael Harré, Joseph T. Lizier

This ebook considers a comparatively new metric in advanced platforms, move entropy, derived from a sequence of measurements, frequently a time sequence. After a qualitative advent and a bankruptcy that explains the foremost rules from records required to appreciate the textual content, the authors then current info concept and move entropy intensive. A key function of the procedure is the authors' paintings to teach the connection among info stream and complexity. The later chapters display info move in canonical structures, and functions, for instance in neuroscience and in finance.

The ebook should be of price to complicated undergraduate and graduate scholars and researchers within the parts of machine technological know-how, neuroscience, physics, and engineering.

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But the destruction of information during computation does cost, at precisely 1 bit per kT ln(2) Joules of energy, with k being Boltzmann’s constant and T absolute temperature. In a 2013 paper 10 1 Introduction the killer finding by Prokopenko et al. [275, 273] is that information flow, as measured by transfer entropy, requires kT per bit of information transferred. 4 Applications The possible applications of transfer entropy ideas are legion, but work to date has mainly been concentrated in neuroscience, with other work in bioinformatics, artificial life, and climate science (Chap.

This chapter is somewhat more intuitive, less formal and easier to understand at a first reading than the next chapter, which gives the full mathematical details of transfer entropy. 1 Introduction Entropy is one of the most alluring and powerful concepts in the history of science and information. But it initially appeared twice, largely independently. Back in the 19th century, Rudolf Clausius came up with the term in thermodynamics. Nearly a century later, Claude Shannon introduced the idea for communications and his new ideas of information theory, now fundamental to all things computational [304].

Poisson was interested in modelling the probability of discrete events occurring within a certain interval of time. In order to model this process (the Poisson process) he proposed the following probability distribution for a random variable x representing the number of arrivals per unit time: p(x = k) = λ k e−λ , k! 71828 is the base of the natural logarithm and k is the number of events that were observed to have occurred within the given time interval. Suppose now that you have an office during which students can come by and discuss the lectures and course materials with you.

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