Nature Neuroscience
3, 1211 (2000)
doi:10.1038/81495
What does 'understanding' mean?Gilles Laurent
Division of Biology, California Institute of Technology,
Pasadena, California 91125, USA
laurentg@caltech.edu
When Ed Lewis in my department won a Nobel Prize a few years ago, our chair
organized a party. On my way there, I overheard an illustrious chemist offer,
"Hey, at least one smart biologist," making his colleagues chuckle.
Nothing new in academia, the land of the high-minded yet curiously parochial
primate. Why bring this up? Because science starts with human interactions:
if we want theory and experimental neuroscience to strengthen each other,
we must hope for people with different cultures, expertise, perspectives and
footwear to leave their prejudices at the door and learn to better appreciate
each other's strengths. This is not easy to achieve when human nature makes
us shun the unfamiliar, when the structure of academic institutions imposes
borders between disciplines, and when reductionist approaches alone undeniably
produce so much concrete knowledge. So, if reductionism works so well—as
it has in the history of neuroscience—why should we care about bringing
theory (and theorists) into the kitchen? It all boils down, it seems to me,
to a classical philosophical question: what does 'understanding' mean? Upon
reflection, it is depressing, if not scandalous, to realize how rarely I ask
myself this. As an experimentalist, I would consider most of what my lab does
as descriptive; at best, we try to tie one observation to another through
some causal link. Most of what we try to explain has a mechanistic underpinning;
if not, a manuscript reviewer, editor or grant manager usually reminds us
that is what this game is about. And we all go our merry way filling in the
blanks. This is, in my view, where theorists most enrich what we do. Theorists,
through their training, bring a different view of explanatory power. Causal
links established by conventional, reductionist neurobiology are usually pretty
short and linear, even when experiments to establish those links are horrendously
complex: molecule M phosphorylates molecule N, which causes O; neuron A inhibits
neuron B, 'sharpening' its response characteristics. This beautiful simplicity
is the strength of reductionism and its weakness. To understand the brain,
we will, in the end, have to understand a system of interacting elements of
befuddling size and combinatorial complexity. Describing these elements alone,
or even these elements and all the links between them, is obviously necessary
but, many would say, not satisfyingly explanatory. More precisely, this kind
of approach can only explain those phenomena that reductionism is designed
to get at. It is the classical case of the lost key and the street lamp; we
often forget that the answers to many fundamental questions lie outside of
the cone of light shed by pure analysis (in its etymological sense). I am
interested in neuronal systems. In most cases, a system's collective behavior
is very difficult to deduce from knowledge of its components. Experience with
many systems of neurons under varied regimes could, in theory, eventually
give me a good intuitive knowledge of their behavior: I could predict how
system S should behave under certain conditions. Yet my understanding of it
would be minimal, in the sense that I could not convey it to someone else,
except by conveying all my past experience. This is one of the many places
where theorists can help me. Much of what we need to provide a deeper understanding
of these distributed phenomena may already exist in some corner of the theory
of dynamical systems, developed by mathematicians, physicists or chemists
to understand or describe other features of nature. If it does not, maybe
it can be derived. But the first step is to map my biological system onto
the existing theoretical landscape. This is where the challenge (and fun)
lies—and where sociological forces must be tamed. In brief, neuroscience
is, to me, a science of systems in which first-order and local explanatory
schemata are needed but not sufficient. Reductionism, by its nature, takes
away the distributed interactions that underlie the global properties of systems.
Theoretical approaches provide different means to simplify. We must thus learn
to understand, rather than avoid complexity: simplicity and complexity often
characterize less the object of study than our understanding of it. Maybe
one day, neuroscience textbooks will finally start slimming down. . ..
|
