ual neurons and connecting the simulations together.
While there’s a difference between
“should be possible” and “is actually
practical,” this is the general idea behind the claim that we can understand
the brain computationally. Assuming
it’s right, it has some important consequences. If computers can simulate
brains (and by similar reasoning, humans in general), then they can solve
any problem humans can solve, if only
by simulating a human. But since we
know that computers can’t solve certain problems (the uncomputable problems), that would mean that humans
can’t solve uncomputable problems either. In other words, it imposes a fundamental limit on human knowledge.
Although we’re a long way from being
able to fully simulate a brain, computational neuroscience—in which scientists try to understand neural systems
as computational processes—is an important and growing area of biological
research. By modeling neural systems
as computational systems, we can better understand their function. And in
some experimental treatments, such as
cochlear implants, we can actually replace damaged components with computational systems that are behaviorally
equivalent to the biological tissue.
SO WHAT IS COMPU TATION?
Computation is an idea in flux. Our
culture is in the process of renegotiating what it thinks the concepts computation and computer really mean.
Computation lies on a conceptual fault
line where small changes can have major consequences to how we view the
world. That makes it a very interesting
field to follow right now.
It’s an interesting exercise to type
“define computation” into Google and
look at the definitions you get. Search
results include “determining something by mathematical or logical methods,” which doesn’t apply well to the
idea of DNA being a computer program,
or “finding a solution to a problem from
given inputs by means of an algorithm,”
which is essentially the functional model that, as we saw, doesn’t apply well to
things like computer games or anti-lock
brake systems. Another possible answer is that computation is “
information processing,” although that simply
Turing argued if
a computer could
fool humans into
thinking it was
human, then it
would have to be
considered to be
though it wasn’t
begs the question of what we mean by
A pragmatic definition would be
that computation is what (modern,
digital) computers do, so a system is
“computational” if ideas from computers are useful for understanding
it. This is probably the closest definition to how real people use the term in
practice. As we’ve seen, there are a lot
of different kinds of systems that are
computational under this view. Computation is then a set of metaphors, a
lens through which we can view and
describe other phenomena.
But that isn’t a very satisfying definition. Another view would be that
computation is wrapped up with the
study of behavioral equivalence. Under
this view, computation is the process
of producing some desired behavior
without prejudice as to whether it is
implemented through silicon, neurons, or clock work.
Whatever definition we adopt, it’s
clear that our society is undergoing
profound changes as a result of both
computational technology and computational metaphors. These raise a
number of psychological and social issues we need to think about:
• What kinds of computational systems are easy for people to use?
• How can we design systems that
people find more rewarding and
• How can we design large systems
that are easy for humans to understand?
• What are the risks of information
technology? Do we really want to
make airplanes that are controlled
by computers, so that if program
crashes, the plane does too? Do
databases and surveillance tech-
nology give governments and cor-
porations too much power?
Computation is a broad, rich field.
It has had deep influences on our lives
and culture. If your PC, cell phone, and
Web access suddenly disappeared, you
would probably have to radically recon-figure your life, even though these have
only been widely available in the last 15
years. It’s unlikely that the changes to
society are going to stop any time soon.
By better understanding computation,
we can help make sure those changes
are for the better.
Ian Hors will is an associate professor of computer
science at North western University. He is a member
of the Department of Electrical Engineering and
Computer Science, where he is Director of the Division
of Graphics and Interactive Media, and was cofounder
of Northwestern’s Animate Arts Program. His research
interests include interactive entertainment technologies,
and cognitive modeling for virtual characters, particularly
modeling of emotion and personality. He received his Ph. D.
in computer science from the Massachusetts Institute of
Technology in 1993. He has been the chair of the standing
committee of the Association for the Advancement of
Artificial Intelligence’s Fall and Spring Symposium Series,
as well as the 2009 International Conference on the
Foundations of Digital Games.
[ 1] Turing, A. On computable numbers, with an application
to the Entscheidungsproblem. In Proceedings of the
London Mathematical Society, Series 2, 42 (1936),
230-265. Note that Turing machines were more
a conceptual design than a practical engineering
design. Although Turing did work on the design of
actual working hardware, his work on Turing machines
came before the construction of real computers was
[ 2] Minsky and Blum proved in the 1960s that a “
two-counter machine” was Turing complete. A two-counter
machine is a machine whose only data representation
is a pair of numbers (non-negative integers, in fact),
and whose only commands are to add or subtract 1
from one of the numbers and to check whether one of
them was zero.
[ 3] Turing, A. Computing machinery and intelligence.
Originally published by Oxford University Press on
behalf of MIND (the Journal of the Mind Association)
59, 236 (1959), 433-60; http:// www.abelard.org/
[ 4] Although widely accepted, the computational vie w is
not universally accepted. See for example, Searle, J.
R. Minds, brains, and programs. Behavioral and Brain
Sciences 3, 3 (1980), 417-457; http:// www.bbsonline.
© 2012 ACM 1528-4972/12/03 $10.00