neri oxman, an
architect and researcher
currently working for
her Ph.d. in design
and computation
at Mit, formed an
interdisciplinary
research initiative
called Materialecology
that undertakes
design research
in the intersection
between architecture,
engineering,
computation, biology
and ecology. here,
she illustrates how
plants often grow in
fashion to maximize the
surface area of their
branching geometries
while maintaining
structural support.
this work was done in
collaboration with W.
craig carter, professor
of Materials science and
engineering, at the Mit
Media lab and the Mit
computation Group.
For more images,
see http://www.
materialecology.com/.
change what we mean by computation.
Our invitation to you, fellow computer
scientists, is to take part in the uncovering of this wondrous connection.a
nature as inspiration
Among the oldest examples of nature-inspired models of computation are
the cellular automata conceived by
Ulam and von Neumann in the 1940s.
a A few words are in order about the organization
of this article. The classifications and labels
we use for various fields of research are purely
for the purpose of organizing the discourse. In
reality, far from being clear-cut, many of the
fields of research mentioned here overlap, or
fit under more than one category. The general
audience for whom this article is intended, our
respective fields of expertise, and especially
the limited space available for this review affected both the depth and breadth of our exposition. In particular, we did not discuss some
fields of research that have large overlaps with
natural computing, such as bioinformatics,
computational molecular biology, and their
roles in, for example, genomics and proteom-ics. In addition, our explanations of various
aspects, themes, and paradigms had to be
necessarily oversimplified. As well, the space
we devoted to various fields and topics was
influenced by several factors and, as such, has
no relation to the respective importance of the
field or the relative size of the body of research
in that field.
John von Neumann, who was trained
in both mathematics and chemistry,
investigated cellular automata as a
framework for the understanding of
the behavior of complex systems. In
particular, he believed that self-reproduction was a feature essential to both
biological organisms and computers. 40
A cellular automaton is a dynamical system consisting of a regular grid
of cells, in which space and time are
discrete. Each of the cells can be in one
of a finite number of states. Each cell
changes its state according to a list of
given transition rules that determine
its future state, based on its current
state and the current states of some of
its neighbors. The entire grid of cells
updates its configuration synchronously according to the a priori given
transition rules.
Cellular automata have been applied to the study of phenomena as
diverse as communication, computation, construction, growth, reproduction, competition, and evolution. One
of the best known examples of cellular
automata—the “game of life” invented
by Conway—was shown to be computationally universal. Cellular automata
have been extensively studied as an al-
ternative explanation to the phenomenon of emergence of complexity in the
natural world, and used, among others,
for modeling in physics and biology.
In parallel to early comparisons39
between computing machines and the
human nervous system, McCulloch and
Pitts proposed the first model of artificial neurons. This research eventually
gave rise to the field of neural computation, and it also had a profound influence on the foundations of automata
theory. The goal of neural computation was twofold. On one hand, it was
hoped that it would help unravel the
structure of computation in nervous
systems of living organisms (How does
the brain work?). On the other hand, it
was predicted that, by using the principles of how the human brain processes information, neural computation
would yield significant computational
advances (How can we build an intelligent computer?). The first goal has
been pursued mainly within the neurosciences under the name of brain
theory or computational neuroscience,
while the quest for the second goal has
become mainly a computer science
discipline known as artificial neural
networks or simply neural networks. 5