In this series of blog postings, I'm going to summarize my process of
going from early draft to final product in preparing a grant proposal
for the
National Science Foundation's
Mathematical
Biology Program. We'll see if there is any value in doing this
after the fact. In this post, I'll go over the background behind the
proposal, including both a vague outline of the science
(non-technical, so I'll dispense with references) and the basics of
my proposal preparation process (such as it is) to date.
It's often said that preparing a grant proposal can take six months to
a year of work. I'm sure that some people actually spend that much
time focused purely on developing a proposal, but that doesn't seem a
very attractive way to spend my time. Instead, I've spent my time
(well, yes, more than 6-12 months) actually doing research. At
this point, I've got a firm understanding of the biological,
dynamical, and computing/theoretical basis for my work, I have
preliminary results that demonstrate that I can do the work and that
this is at least a feasible line of inquiry, and I enjoyed the
process. One of the reasons you go into academia is to do
research; why spend maybe 1% of your life merely working on
proposing to do research? Of course, if you need a billion
dollar spacecraft to do your research, then things (such as your
planning time horizon) are a bit different. Computer scientists are,
by comparison, cheap dates.
So, what's my research on? I want to understand brains as computing
devices. Now, this is a tall order, so I've personally "settled" for
trying to understand how very small networks of nerve cells (neurons)
do their thing. You may have heard of all sorts of exciting results in
the neurosciences, and how researchers are closing in on the secrets
of the brain. Couple this with the first, real non-industrial
commercial/near-commercial robots, and it really looks like we're
close. I'm not so confident of that.
First of all, progress in robotics is misleading, because most of it
is in sensors, actuators, and processing power. Little of it derives
from any deep understanding of biological information processing. On
the other hand, it is certainly true that we have entered a golden age
in the neurosciences, with an amazing array of tools that allow us to
gather all sorts of information about brain, network, and cell
function. You may have seen "pictures of the brain working":
functional magnetic resonance imaging (fMRI) that shows how active
different areas of the brain are while a person does some task. This
is often accompanied (for non-technical consumption) by an article
that talks about how much this tells us about how the brain works. OK,
I'm contrary by nature, but in my opinion this tells us very
little about how the brain works. Yes, it narrows the focus of our
inquiry from the entire brain to maybe 10% of it. Yes, it tells us
something about which areas are active at the same time, and even
sequences of activation in time. But we're still talking about the
activity of hundreds of millions, even billions, of neurons. To me,
this is just the peeling of the outer skin of the onion.
I don't want it to seem that I'm saying that these investigations
aren't worth it or that they don't produce great data; they do. It's
just that the brain is so incredibly complex: the most complex object
in the known universe. A research data flow of petabytes per year over
decades may only scratch the surface of what we need to learn before
we understand how the brain works (assuming we're capable of
assimilating so great a flux). This complexity may very well extend to
the smallest level: while many researchers consider individual neurons
to be simple devices (in a computational sense), this is really just
an assumption. The fact is that a good simulation of a neuron,
including its shape and the interactions of its internal molecular
machinery with its external electrical and chemical activity, is a job
for a hefty supercomputer. If this structural complexity shows through
to a neuron's computational complexity, then we're suddenly dealing
a brain as a complex network of billions of supercomputers.
There's so much complexity in nervous systems that entire aspects of
them are almost ignored, or at least given second-class status in the
search for understanding. Just one example: I've mentioned nerve
cells, or neurons, as making up nervous systems and brains. But
there's another class of cells that's actually more numerous in our
brains: glial cells. They're usually dismissed as serving only
structural and physiological functions: scaffolding and waste
disposal. But they can produce external electrical and chemical
activity. What does it do to our view of neural computation if glial
cells play an important role?
Anyway, my research focus for this grant proposal is on error
correction coding: looking at how characteristics of the output of
one neuron could be used to allow other neurons to recover faster if
an error occurs. Think of it as the neural equivalent of a CD still
being playable despite the fact that it's scratched. This is small
enough scale for me to be able to wrap my mind around it. Whether this
is reflective of the true complexity of the subject matter, or merely
the complexity of my mind, is another story.
So, back to the grant proposal. I know what I want to do, I can
explain why I think it's important, I can relate it to biology and
mathematics, and I have a lot of material I've already written about
the subject (four conference papers and a journal paper). Writing
should be a piece of cake, right? I give myself three months to do it,
working part-time, of course (I still have teaching and a journal
paper to finish writing while I'm doing this). More on this to come...
Topics: research, grant proposals.
