Biomedical Engineering Reference
In-Depth Information
All aspects of adaptation of organisms to
their environment, including the appearance of
intelligent behavior, have been used as (bio-)
inspiration. The field of
neural networks
[2]
, for
example, has taken inspiration from the struc-
ture and function of nervous tissue in higher-
order animals, including the brains of humans.
The field of
fuzzy logic
[3]
took inspiration from
human cognitive processes with the ability to
think in noncrisp terms. The field of
artificial
immune systems
[4]
has taken inspiration from
the elaborate adaptive systems within higher-
order beings that enable them to defend them-
selves against intruders. The field of
ant colony
optimization
[5]
has taken inspiration from the
distributed nature of ant colonies and their
apparently purposeful molding of the environ-
ment. The field of
swarm intelligence
[6]
has
taken inspiration from different sorts of ani-
mals organizing their behavior in swarms,
flocks, or schools in order to achieve macro-
effects (on the level of the entire swarm) from
micro-causes (behavior of individuals). The
field of
artificial life
[7]
has taken inspiration
from the very beginnings and basics of life to
find ways to produce behavior akin to living
behavior for the benefit of, e.g., computer
games or for the simulation of alternatives to
living matter, in order to better understand life
on Earth.
May this list suffice for the moment. It is not
exhaustive, and year after year new ideas are
being proposed for computation derived from
biological systems. Probabilistic reasoning,
machine learning, emergence of novelty, com-
plex adaptive systems, social behavior, intelli-
gence, sustainability, and survival are all terms
that can be related to and studied in models of
bioinspired computing. Essential to these mod-
els is the idea that a distributed system of inter-
acting entities can bring about effects that are
not possible for single entities or entities isolated
from each other to produce.
In this chapter let us focus, however, on one
particular paradigm within the area of bio-inspired
an important and dynamically changing part
of their environment. Ultimately, behavior
requires intense processing of information, both
for survival and for the benefit of an organism.
Behavior of individuals is studied in a branch of
biology called
ethology
, the behavior of species in
their interaction with the environment is stud-
ied in ecology, and the dynamics of species over
time is the subject of population and evolution-
ary biology. Molecular biology considers the
regulation of behavior on the molecular level.
From the lowest level of molecules to the high-
est level of evolution of species, this dynamic is
about reception and processing of information
and the appropriately executed actions follow-
ing from the results of such
computation.
Given this context, it is no wonder that com-
puter scientists and engineers have embraced the
paradigms of biology and tried to extract ideas
from the living world to apply them in man-made
computing environments such as computers and
robots. Robots are actually the application area of
bioinspiration closest to actual living organisms,
since they can be said to possess a body, a struc-
ture that has to act in the real world. Less obvious,
yet very active, is the area of bioinspired comput-
ing, where researchers try to extract more or less
abstract principles and procedures from living
organisms and realize them in a computational
(algorithmic, software) setting.
There is full agreement in the sciences now
that the generation of successive sequences of
species in what has been called the
tree of life
is a
product of evolution, governed by the principles
of Darwin's theory of natural selection
[1]
. Evo-
lution and its models are the source of bioinspi-
ration that we shall discuss in this chapter in
more detail. In a way, this is the most fundamen-
tal part of biology because it is the driving mech-
anism for the diversity of life on our planet.
However, to set this in context, we want to at
least mention in the remainder of this section
several other examples of bioinspired comput-
ing, not necessarily in the temporal order of their
development.