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Leaving the framework of physical thermodynamic entropies entirely, the entropy of information was introduced in the context of communication engineering in electrical and electronic devices. The metaphorical machine for the current age of entropy, analogous to the role of heat and steam engines in classical thermodynamics, is the computer. Energy in this context is a relatively trivial property. Ammeters and other monitors of load are unable to discriminate between a computer actively engaged in encoding and computation or one simply maintaining its dynamic memory while resting in computational readiness. This situation is very analogous to the results of early work discussed previously on the metabolic rates and sources of the whole brain’s energy, oxygen and glucose metabolism, by National Institutes of Mental Heath’s Seymore Kety and Louis Sokoloff and the State of Illinois Thudicum Laboratory’s Harold Himwich. Using whole head arterial-venous, energy-in, energy-out, differences, they could not demonstrate differences in rates of whole brain metabolism between states in which the human subjects were engaged in solving mathematical problems or deeply sleep. In today’s brain imaging research, using a variety of physical reflections of the brain’s metabolic activity, it is the differences in regional distributions of metabolic activity that are relatable to subjective and behavioral states, not differences in total amount of energy expended. In graphically coded representations of the regional metabolism of the brain in action, one or another or many areas “light up” and others “grow dark” in correlation with changes in thinking, feeling and action.

The entropy first developed by Claude Shannon was formalized for use in 1948 in what was then called communication theory and now information theory. It represented a measure of the ambiguity and uncertainty that had the potential for being resolved by new knowledge. In this context, entropy and information were obviously complementary descriptors. A message that informs us about which of ten possibilities should be chosen contains less information than one that informs us about the proper choice to be made from among a thousand possibilities. The entropy of communication theory is a measure that is computed on uncertainty. The information reception capacity of a system is dependent upon the amount of

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