Cell Language Theory, The: Connecting Mind And Matter

362 The Cell Language Theory: Connecting Mind and Matter

b2861 The Cell Language Theory: Connecting Mind and Matter “6x9”

information and entropy than before, and (c) the brain as an informational

machine and not an entropic one.

8.5.1 The Definition of Planckian Information, IP

It is generally accepted that there are at least three basic aspects to

information — amount, meaning, and value — although Burgin [37,

p. 130] lists in addition other properties of information such as cost,

entropy, uncertainty, effectiveness, completeness, relevance, reliability,

and authenticity, and so on.

Planckian information is primarily concerned with the amount (and

hence the quantitative aspect) of information. There are numerous ways

that have been suggested in the literature for quantifying information bed-

side the well-known Hartley information, Shannon entropy, algorithmic

information, etc. There are at least 35 such methods listed in [37, pp.

131–133], each method applicable to different aspects of information

determined by the generalized parameter called “infological” (i.e., infor-

mational–ontological) systems [37, pp. 104, 113–114]. The Planckian

information, given by Eq. (8.10), is a new measure of information that

applies to specific processes called the Planckian process defined in (8.8):

Planckian processes are the physicochemical, neurophysiological, bio-

medical, mental, linguistic, socioeconomic, cosmological, or any other

processes that generate long-tailed histograms that obeys or fits the

Planckian distribution equation, PDE. (8.8)

Thus, Statement (8.8) may be viewed as the infological parameter of IP.

As can be seen in Figures 8.6(g), (i), (k), (o), (r), and (t), the curves

labeled “Gaussian” overlap with the rising phase of the PDE curves. The

“Gaussian” curves were generated by the Gaussian-like equation (GLE),

Eq. (8.9), which was derived from the Gaussian equation by replacing its

pre-exponential factor with a free parameter A:

()/2^22

yA= e--xμs (8.9)

The degree of mismatch between the AUC of PDE and that of GLE

can be interpreted as a measure of nonrandomness (and hence order), if

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