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Bioelectronic man/machine interfaces and symbiotic intelligent environments.
Increase of complexity of Internet interfaces and the Darwinian process of selective
stabilization of Internet nodes.
Summary of presentation (July 4, 2001)
Joël de Rosnay
Director of Forecasting and Assessment
Cité des Sciences et de l’Industrie, La Villette, Paris, France.
http://www.derosnay.com
[email protected]
The convergence of biology and computers
New interfaces between man and computers are developed. They result from the marriage of
biology and computers. A new fundamental and applied discipline is being born of this
convergence and, more generally, of the hybridization and coevolution of the methodologies
and techniques used in computers and of those used in biology and supramolecular chemistry.
In 1981, I proposed to call this new discipline : biotics (a combination of biology and
informatics). (de Rosnay 1981-2000). Biotics opens the way to the developpement of new
molecular electronic components and circuits (biochips, biotransistors) and bioelectronic
interfaces linking humans, computers, and networks.
Biotics comprises two complementary areas of application: that of analog signals (in this case,
bioelectronics) and that of digital signals (molecular electronics). The construction of a
"biocomputer" based on circuits and memory from DNA or molecular electronics and using
materials compatible with living systems, is part of biotics. (Aldeman 1994, Kolata 1995,
Kari 1997). The field emerged from recent advances in biology, solid-state physics, organic
chemistry, micro-electronics, robotics, and nanotechnology. Today it constitutes a new area of
research with many applications.
Molecular electronic components are currently considered the potential successors to
semiconductors. These synthetic components offer many advantages over traditional
semiconductors: three-dimensional assembly, synthetic materials that allow the custom design
of properties, miniaturization approaching that of biological structures, and possibilities for
interfacing with living systems. (Reed 1999, Joachim 2000, Tour 2000).
New interfaces between the human brain and computers
Direct neurons to machines interfaces have been developed during the last years. Boris
Rubinsky at Caltech has developed a “biotransistor” made of living cells interconnected with
a microprocessor. The cells acts as diodes. (Rubinsky 1999). William L. Ditto, a physicist at
the Georgia Institute of Technology working with a group at the University of Bordeaux in
France, has developed hybrid computers that mate living neurons with silicon circuits. He has
called this new field “neurosilicon computers.” In 1999, he was able to do arithmetic with two
large neurons from leeches, joined together and linked to a personal computer. (Spano and
Ditto, 1999)
Another step on the road to creating biocomputers was achieved by Jerry Pine, a biophysicist
at the California Institute of Technology in Pasadena. He was able to “grow” microcircuits
made of living neurons on top of an array of electrodes. He calls the device the “Neurochip.”
By assigning a specific place to each neuron, it is possible to “listen” to their chatter and
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develop reproducible logic gates out of combinations of neurons.(Regehr and Pine 1988,
Maher and Pine in press, Pine et coll 1996).
A similar type of research has been undertaken by Keiichi Torimitsu at the NTT’s
Biosciences Research Group in Atsugi, Japan. (Niwa and Torimitsu, 1998 His group is trying
to develop an effective interface between computers and the brain. To test this possibility, his
laboratory sent electronic signals to slices of neuronal tissue placed close to tiny electrodes
and researchers monitored the electronic current naturally generated by the neurons when they
communicated with each other. (Torimitsu 1998).
More recently, Miguel Nicolelis of the Duke University Medical Center, has trained two owl
monkeys to control a robotic arm through brain signals. The arm was placed at MIT’s lab for
Human and Machine Haptics and controlled by the monkeys through an Internet interface.
(Nicolelis 2000)
Biological evolution and the Internet as massive parallel multiprocessors
The new symbiotic interfaces between man, computer and networks, creates a massive
parallel multiprocessor. Biological evolution performs like such parallel multiprocessor. The
basis of biological evolution is the three-part process of mutation, competition, and selection.
Random variations occur in the programming of living things (DNA). This results in new
species that are more or less suited to the environment in which the species are in competition.
The fittest survive, are selected — or rather self-select — and transmit to their descendants
the genetic code for survival and competitiveness, the new mutant genes. This process takes
place in parallel within the DNA of billions of individuals in competition for limited
resources. Biological evolution, is therefore comparable to a huge parallel multiprocessor that
seeks solutions to problems by trying out potential solutions and storing those that work in
memory. This is how the diversity of the living world, biodiversity, is created. (de 1995,
2000)
The autocatalytic development of the Internet is an illustration of a coevolutionary process of
order emerging from chaos. Millions of agents acting in parallel according to simple rules also
form a gigantic multiprocessor that can collectively find solutions to complex problems and
adapt to the evolution of its informational ecosystem. As a result of these rules and emergent
properties, the Internet has become an increasingly intelligent planetary metacomputer. It
processes data in parallel, combining the actions of millions of agents testing procedures and
programs in real time in a competitive environment — a process that is not unlike Darwinian
biological evolution. We can therefore expect the Internet to select increasingly powerful
solutions in electronic communications and software applications. Intelligent agents manage
interfaces by interconnecting all the existing networks, allowing people to access information
and act in real time, as do the neurons of the brain. This new planetary neural hypernetwork
functions chaotically, fluidly, and in a way that is constantly reconfigurable, in response to
decisions made in parallel by hundreds of millions of interacting human agents and virtual
robots. In this, it resembles the immune system, the hormonal system, and the nervous
system, three interconnected networks that determine an organism’s psycho-neuroimmunological behavior.
Selective stabilization and reconfiguration of Internet links and nodes
In 1949, in his book The Organization of Behaviour, Donald O. Hebb, a neurophysiologist at
McGill University, in Montreal, proposed a revolutionary new theory of psychological
behavior. (Hebb 1949). According to this theory, the brain constantly reconfigures the
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synapses that transmit nerve impulses. Through the chemical action of activator or inhibitor
hormones, the synapses are reprogrammed as a result of various stimuli. Through the
successive stimulation of neural connections and pathways, whole areas made up of thousands
of neurons are activated and connect so as to form subsets that store information through the
reinforcement of impressions (shapes, colors, sounds, words). These subsets constitute
dynamic networks of neuronal interactions, the brain’s building blocks of information.
I propose to look at the formation and functioning of the Global Brain, (that I call “the
Cybiont”), in a similar way. Human beings, multiple agents in chaotic interaction, are the
neurons of the hypernetwork. The links among them, occurring through computers (and even
more directly through biotic interfaces), are giving rise to a conscious representation of the
“mental” functioning of the Cybiont, a global consciousness that is reflected in the
introsphere. (de Rosnay 1995, 2000). These links are reversible, and they can be reinforced or
inhibited. Autocatalytic processes take place, leading to new concepts, solutions, or ideas. The
Internet today abounds with examples of such processes. (Heylighen 1996)
Jean-Pierre Changeux, of the Institut Pasteur and his collaborators have proposed a model of
epigenesis of neural networks by selective stabilisation of synapses and analysed in these
terms the molecular mechanisms involved in the regulation of acetylcholine receptor genes
expression during the development of the motor endplate. (Changeux 1985, Kerszberg and
Changeux 1992). In particular, they have identified DNA regulatory elements, as first/second
messengers, specifically involved in the regulation of acetylcholine receptor genes
transcription by electrical activity in extra junctional areas, and by "trophic" factors in the
subneural domain. These issues are of relevance for the understanding of long term synaptic
plasticity.
I propose that the selective stabilization of Internet node follows an analogous principle
through HTML links, bookmarks, address books, Web sites, creating a situation of
intercommutability and increasing the complexity of the network. New properties will emerge
from such highly complex system.
Autocatalytic processes, self selection and emergence of new properties
Emergence, mutation, and breakthroughs can be observed in the light of rapid accelerations
resulting from sudden phase transitions. These phenomena are typical of the Internet’s fast
development, and can be seen as autocatalytic systems creating dense “time bubbles” and
fostering the emergence of new properties through rapid phase transitions. To illustrate this
type of self-selection, I propose to adapt to the Internet the random graph model used by
Stuart Kauffman to outline the role of collectively autocatalytic molecular systems in the
origins of life. (Kauffman 1995). His model is based on the interconnection of many buttons
using threads. As Stuart Kauffman puts it, “When there are very few threads compared to the
number of buttons, most buttons will be unconnected. But as the ratio of threads to buttons
increases, small connected clusters begin to form. As the ratio of threads to buttons continues
to increase, the size of these clusters of buttons tends to grow.” A phase transition suddenly
occurs when the ratio of threads to buttons reaches 0.5, and a giant cluster is formed. The rate
of growth of the giant cluster then slows down as the number of isolated buttons and small
clusters decreases. This is represented by the top of the S-shaped curve.
I propose to replace the buttons with web sites (nodes) and the threads with Internet links
(edges). Let’s imagine millions of web sites and millions of links. Beyond a given ratio of
links to web sites (0.5?), a phase transition must occur. With 400 million users, 170 million
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host computers, and an average of 50 links per site (bookmarks and email addresses), new
properties will certainly emerge. What about with 2 billion users, 800 million host computers,
and 500 links per site? With such a giant electronic cluster of interconnected brains and
machines, what will these properties look like? Probably a new form of macrolife becoming
progressively conscious of its own existence and self-maintenance.
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