**Dr Peter Marcer**, of the British Computer Society Cybernetic Machine Specialist Group, spoke on **Quantum Holography, New Biology, Novel Technology, or What physicists can teach biologists and biology, physics.**

In the 19th century it seemed that the whole of the Natural World could be described in terms of classical physics. In the 20th century, the new quantum physics was needed to supplement this picture in relation to microscopic phenomena, successfully explaining much of elementary particle physics. However, in the new millennium, such explanations will be extended to the entire structure of the whole Universe, cosmological, galactic, solar, planetary, geological, living and social, including the molecular biological, biological, neurological, psychological, noetic, medical, and even mathematical.

The key to such explanations is measurement ie the process of extraction of information, which is the basis of all experimental science and experience. An appropriate starting point is therefore the work of John von Neumann, in relation to measurement in quantum physics. This definition of measurement not only introduces the concepts of information and signal processing into quantum physics as integral phenomena of the natural world, it subsumes classical physics. This leads, he was convinced, to a Grand Unification of the Sciences, with new branches such as quantum cosmology, quantum biology, quantum neuroscience, quantum medicine, etc, within which are included the currently established classical understandings. This is a natural extension of the view, expressed by Feynman, that without quantum physics there can be no chemistry, and therefore by implication, no molecular biology, no biology, nor indeed any of the life sciences.

Examples were given that these classical understandings, however, represent most likely only one half of the full eventual scientific undertanding. In particular, experiments (Gariaev, 1994) carried out at the Institute of Control Sciences of the Moscow Academy of Sciences by the Peter Gariaev group were briefly described, which validate a model, the DNA-wave biocomputer, as the very basis of a quantum molecular biology and the corresponding quantum life sciences; and also, the work of Walter Schempp of Siegen University. His extended exploration of quantum holography via the mathematics of the Heisenberg Lie group as a means to incorporate signal theory into quantum physics (Schempp, 1986), together with this mathematical foundation's experimental validation by means of nuclear magnetic resonance imaging (MRI) machinery (Schempp, 1998), also provide an independently conceived mathematical quantum wavepacket explanation of the workings of DNA (Schempp, 1992, 1993, Marcer and Schempp, 1996) and by implication biology. And one that fits quite exactly into the framework of von Neurmann's quantum measurement theory, and the work carried out in Moscow. A particular claim and the experimental and theoretical evidence to be cited, is, that the currently established understanding of the genetic code based on DNA nucleotide basepairing to which mankind assigns the symbols A,U,C,G, is in fact, only one half of the full scientific story.

**References**

Schempp, W. (1992), "Quantum holography and neurocomputer architectures", *Journal of Mathematical Imaging and Vision*, 2, 279-326.

Schempp, W. (1986), *Harmonic Analysis on the Heisenberg Group with Applications in Signal Theory*, Pitman Notes in Mathematics Series, 14, Longman Scientific and Technical, London.

Schempp, W. (1993), Bohr's Indetermincy Principle In Quantum Holography, Self-adaptive Neural Network Architectures, Cortical Self-organizatrion, Molecular Computers, Magnetic Resonance Imaging and Solitonic Nanotechnology, Nanobiology 2, 109-164.

Schempp, W. (1998), *Magnetic Resonance Imaging, Mathematical Foundations and Applications*, John Wiley, New York.

Marcer, P. and Schempp, W. (1996), "A mathematically specified template for DNA and the genetic code, in terms of the physically realizable processes of quantum holography", *Proceedings of the Greenwich Symposium on Living Computers*, editors Fedorec A. and Marcer P., 45-62. see also Clement B.E.P, Coveney P.V., Marcer P. 1993, "Surreal numbers and optimal encodings for universal computation as a physical process: an interpretation of the genetic code", *CCAI Journal*, 10, 1/2, 149-164.

Gariaev, P. (1994), Wave genome, Public Profit. 279p [in Russian] ; "Fractal presentation of natural language texts and genetic code", Maslow M.U., Gariaev P.P., *2nd International Conference on Quantitative Linguistics*, QUALICO '94, Moscow, September 20-24, 193-194, 1994 ;Gariaev P.P. etc; (http://www.aha.ru/~gariaev)

**Dr Daniel M. Dubois**, of the Centre for Hyperincursion and Anticipation in Ordered Systems, University of Liège, Belgium, gave an Internet presentation from Liège, using a colourful on-screen text. His subject was a **"Survey of Computing Anticipatory Systems with Incursion and Hyperincursion"**.

The main purpose of his paper was to show that anticipation is not only a property of biosystems but is also a fundamental property of physical
systems.

For Newtonian mechanical systems as well as all quantum and relativist systems, the description by local differential equations is
identical to the description by the global Maupertuis Least Action Principle, which states that the trajectory, given by an integral defined by initial and final states, is optimum. So the Aristotelian final cause is implicitly embedded in any system theory and model. Thus such systems are implicit anticipatory systems because they evolve, from an initial state, to a final state which is implicitly embedded in them. In an epistemic way, such implicit anticipatory systems evolve "as if they know their future".

Anticipation is embedded in physical systems. In electromagnetism, for example, a charge moving at velocity v' at time t' creates an electrical field travelling at the light speed c. The electrical field is the field of the charge at the distance r and time t, anticipated from the past distance
r' and time t' = t - r'/c.

Strong and weak anticipations can be defined by incursive and hyperincursive systems..

Definition of an incursive discrete strong anticipatory system: an incursive discrete system is a system which computes its current state at time t, as a function F of its states at past times, ., t-3, t-2, t-1, present time, t, and even its states at future times t+1, t+2, t+3, .. x(t+1) = F(., x(t-2), x(t-1), x(t), x(t+1), x(t+2), .;p) where the variable x at future times t+1, t+2, . is computed in using the equation itself. Such an incursive system is self-referential because it computes its future states from itself and not from a model-based prediction.

Definition of an incursive discrete weak anticipatory system: a weak incursive system is a system which computes its current state at time t, as a function F of its states at past times, ., t-3, t-2, t-1, present time, t, and even its predicted states at future times t+1, t+2, t+3, .. x(t+1) = F (., x(t-2), x(t-1), x(t), x*(t+1), x*(t+2), .;p) where the variable x* at future times t+1, t+2, . are computed in using a predictive model of the system. Such weak anticipatory systems may be related to the Robert Rosen definition: "An anticipatory system is a system containing a predictive model of itself and/or of its environment, which allows it to change state at an instant in accord with the model's predictions pertaining to a latter instant".

Definition of a hyperincursive discrete anticipatory systems: a hyperincursive discrete anticipatory system is an incursive discrete anticipatory system generating multiple iterates at each time step.

It was shown that incursive and hyperincursive anticipatory systems could model properties of biosystems like free will, game strategy, theorem
creation, etc. Anticipation is not only related to predictions but to decisions: hyperincursive systems create multiple choices and a decision process selects one choice. So, anticipation is not a final goal, like in cybernetics and system science, but is a fundamental property of physical and biological systems."

**Reference**

Dubois, Daniel M. (2000), "Review of Incursive, Hyperincursive and
Anticipatory Systems - Foundation of Anticipation in Electromagnetism".
*Computing Anticipatory Systems: CASYS'99 - Third International Conference.*
Edited by Daniel M. Dubois, Published by The American Institute of Physics,
AIP Conference Proceedings 517, pp. 3-30.