information required to gain knowledge of an event is dependent upon the probability of its occurrence. log2(0.5) = 1 is the maximal entropy when modeling the equilibrium entropy of an independent random 0,1, (heads or tails) series of informational states as might result from flipping a fair coin a large number of times. This value would be maximal when the coin was fair, p(heads, tails) = 0.5, and the entropy would be 2(number of allowed states)x0.5(probability of occupying each state)x/og10 (0.5) = 0.693147...or in bits, log2(0.5) = 1. More generally, if system’s behavior is distributed equally among its possible states, the Shannon entropy is maximal and equal to the logarithm of the number of defined states, for example, log2 (2) = 1. Shannon’s classical equation about information content says the amount of information, / = -p /og2 p, measured in bits. The minus sign in this reciprocal relation indicates that the information content of data, /, goes up as the probability of occurrence of the observed data, p, goes down. Since soon we will be talking about brains and their various styles of information encoded content as well as its transmission, we note the other famous Shannon theorem dealing with limits on the channel capacity, C, for information transport is C = Wlog2(1+S/N) where W is bandwidth, the range of frequencies available for information transport, S is the strength of the signal and N is the strength of the noise. Recall that the /og2(1) = 0 so only the signal-to-noise ratio, S/N contributes to the value of the product of the multiplication by bandwidth, W. Transparent clinical examples come from studies of the perceptual and cognitive decline in normal geriatric patients in which the range of aural frequencies (W) heard without augmentation decreases with age as does the frequency range (W) observed in their resting brain waves. The inattentiveness of the obsessively worried ruminator can be used as an example of brain channel capacity being redu