When people gesture while talking, it is usually for one of two
reasons. If they are sure of themselves, hand movements can bring
emotion and conviction to the words. If they are not so sure, they
might be using the hand waving to convince you that they are right in
general, and that the details are not important anyway. The latter is
often the case when people talk about the brain.
It is not that what goes on beneath our scalp is a complete
mystery. Modern neuroscience began over 100 years ago, when pioneering
neuroanatomists began to unearth the basic architecture of the central
nervous system. Since then, the field has grown significantly and today
its largest conference, the Society for Neuroscience Annual Meeting,
attracts over 30,000 attendees. Thanks to the popularity of the subject
and the seemingly never-ending technical advances, scientists are now
churning out masses of data at every level of analysis. Every day, they
fill databases with gene sequences and protein interactions, map out
networks of nerve cells, and even record the differing roles of each
brain region in behaviour.
So why all the uncertainty? If all these experts are working so
hard, why have we not found a cure for Alzheimer’s, understood how you
can tell a cat from a dog in a split second, or explained why I feel
like a person, and not just “a pack of neurons”, as suggested by
Francis Crick? The answer lies in the sheer complexity of the brain. A
human adult has about 100 billion neurons inside their head, all
working away at their own little chores. Scientists have extensive
knowledge about the different cell types, their make-up, how they are
wired together, and ideas about what most of them are doing. But the
leap from this to something that can do a crossword puzzle is a big
one. It is not an impossible problem, exactly. Just a hard one.
Like other scientific disciplines before it, neuroscience is now
reaching a stage where enough facts are known to start building
general, and maybe even mathematical theories about how it all works.
Computational neuroscience is the field that develops and tests these
theories. That is not to say that experiments will ever become
obsolete. Even relatively mature fields, such as physics, need the
constant challenge of real life experiments to show who is right and
who is wrong. The aim of computational neuroscience right now is to
gather existing experimental data, try to fit it together in some
coherent way, and go on to make suggestions and predictions for future
experiments.
So how does someone tapping away at a computer in a dusty old office
study the brain? They do it by trying to build theoretical models. A
good example of this is a popular method called compartmental
modelling, often used to examine the behaviour of single neurons (nerve
cells). Since each neuron in our brain computes and transmits
information using electrical signals, it is possible to think of each
of them as a small, individual electrical circuit, made from the same
basic elements - resistors, capacitors and the like - that control your
mobile phone. In principle I could go, soldering iron in hand, and
physically make a model neuron with these building blocks. Some people
do. The downside is that it is a very time and resource-sapping
process. It’s much easier to build a virtual circuit on your computer.
With enough constraining experimental data, these types of
single-cell models can become quite detailed and include ion channels,
complex molecular interactions and the varying shapes of real neurons.
Once you have set up your model, you can test your hand-waving ideas
explicitly and see if they fit together in a logical way. Another great
advantage to this approach is that you can also do experiments on your
virtual cell that are difficult, impossible or even immoral in real
life, keeping animal rights activists happy in the process.
With enough data, these models can make specific statements about
the real world. For example, an elegant study by Agmon-Snir and
colleagues (Nature, 1998) looked at time-difference detecting neurons
in the auditory brainstem. Imagine you are watching a tennis match from
the stands. The grunting noise from the player on your left side will
reach your left ear slightly earlier in time than your right ear. The
further to the left the player is, the bigger the time difference. Your
brain uses this information to tell which direction a sound came from.
One puzzle was why, among the neurons involved, the ones that respond
to higher-frequency sounds are smaller than those that deal with
lower-frequency sounds. Agmon-Snir and colleagues created realistic
computational models of these cells and showed that the higher
frequency sound signals are optimally handled by neurons with shorter
branches, because of noisy signal transmission.
Of course this single-cell example looks at just one of the many
levels at which the brain could be studied. David Marr, an influential
early theorist, defined three levels at which we can analyse a
computational system such as the brain: the computational level, the
algorithmic level and the implementation level. The first level
identifies the computations that are to be performed. An example in the
visual system would be motion detection. The second level determines
the strategy used to perform this task. A computer programmer would
call this the choice of algorithm. The third level looks at the
physical implementation of this strategy, which in the case of the
brain is the network of neurons. To a certain extent, experimental
neuroscience has focused on this last ‘nuts-and-bolts’ level.
Theoretical neuroscientists, however, have been working on all three
levels; everything from the detailed biophysics of ion channels to more
abstract full-brain models.
After defining a problem at a certain level, the theorist must
design the model, taking into account several factors. Firstly, a model
that is too detailed can be just as difficult to draw conclusions from
as the real thing, which would render it mostly useless. To paraphrase
Einstein, a model should include just enough detail to explore the
question at hand and no more. Secondly, in many cases little
experimental data is available to con- strain the model and ensure it
reflects reality. The data must also be of high enough quality;
substandard data will give you substandard results (a principle known
among computer programmers as GIGO: Garbage In, Garbage Out). Thirdly,
even with the extraordinary speed of modern computers, some simulations
can take days or even weeks to run. For this reason, modellers may not
want to include all the details, and may instead use approximations.
Fortunately, computer processing power continues to increase every
year. Many modern studies are based on ideas that others had decades
ago but simply lacked the computational resources to implement at the
time.
Neuroscience used to be a divided field, with the experimentalists
complaining that the theorists were fiddling around with abstract ideas
that would never work in a real brain, and the theorists criticising
the experimentalists for filling the literature with reams of boring
data. These divisions are rapidly fading. Many researchers are
realising that steady progress will require a two-way flow of ideas.
Even more scientists are actively blurring the lines by adapting
methods from both approaches. This inter-disciplinary outlook will
ensure that exciting times lie ahead for our understanding of the brain
and, in many ways, of ourselves.
Refs:
- The role of dendrites in auditory coincidence detection.
H Agmon-Snir, C E Carr, J Rinzel.
Nature (1998) 393 (6682) 268-72
PMID: 9607764
- Computational neuroscience.
T J Sejnowski, C Koch, P S Churchland.
Science (1988) 241 (4871), 1299-306
PMID: 3045969
