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