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Is this a unified theory of the brain?
http://www.newscientist.com/article.ns?id=mg19826586.100&print=true

28 May 2008
Gregory T. Huang

THE quest to understand the most complex object in the known
universe has been a long and fruitful one. These days we know a good
deal about how the human brain works - how our senses translate into
electrical signals, how different parts of the brain process these
signals, how memories form and how muscles are controlled. We know
which brain regions are active when we listen to speech, look at
paintings or barter over money. We are even starting to understand
the deeper neural processes behind learning and decision-making.

What we still don't have, though, is a way to bring all these pieces
together to create an overarching theory of how the brain works.
Despite decades of research, neuroscientists have never been able to
produce their own equivalent of Schrödinger's equation in quantum
mechanics or Einstein's E=mc^2 - a powerful, concise, mathematical
law that encapsulates how the brain works. Nor do they have a
plausible road map towards a "theory of everything", like string
theory in physics. Surely if we can get so close to explaining the
universe, the human brain can't be that hard to crack?

Perhaps it is. The brain is much messier than a physical system. It
is the product of half a billion years of evolution. It performs
myriad functions - reasoning, memory, perception, learning,
attention and emotion to name just a few - and uses a staggering
number of different types of cells, connections and receptors. So it
does not lend itself to being easily described by simple
mathematical laws.

That hasn't stopped researchers in the growing field of
computational neuroscience from trying. In recent years, they have
sought to develop unifying ideas about how the brain processes
information so that they can apply them to the design of intelligent
machines.

Until now none of their ideas has been general or testable enough to
arouse much excitement in straight neuroscience. But a group from
University College London (UCL) may have broken the deadlock.
Neuroscientist Karl Friston and his colleagues have proposed a
mathematical law that some are claiming is the nearest thing yet to
a grand unified theory of the brain. From this single law, Friston's
group claims to be able to explain almost everything about our grey
matter.

It's a controversial claim, but one that's starting to make people
sit up and take notice. Friston's work has made Stanislas Dehaene, a
noted neuroscientist and psychologist at the College of France in
Paris, change his mind about whether a Schrödinger equation for the
brain might exist. Like most neuroscientists, Dehaene had been
pessimistic - but not any more. "It is the first time that we have
had a theory of this strength, breadth and depth in cognitive
neuroscience," he says.

Friston's ideas build on an existing theory known as the "Bayesian
brain", which conceptualises the brain as a probability machine that
constantly makes predictions about the world and then updates them
based on what it senses.

The idea was born in 1983, when Geoffrey Hinton of the University of
Toronto in Canada and Terry Sejnowski, then at Johns Hopkins
University in Baltimore, Maryland, suggested that the brain could be
seen as a machine that makes decisions based on the uncertainties of
the outside world. In the 1990s, other researchers proposed that the
brain represents knowledge of the world in terms of probabilities.
Instead of estimating the distance to an object as a number, for
instance, the brain would treat it as a range of possible values,
some more likely than others.

A crucial element of the approach is that the probabilities are
based on experience, but they change when relevant new information,
such as visual information about the object's location, becomes
available. "The brain is an inferential agent, optimising its models
of what's going on at this moment and in the future," says Friston.
In other words, the brain runs on Bayesian probability. Named after
the 18th-century mathematician Thomas Bayes, this is a systematic
way of calculating how the likelihood of an event changes as new
information comes to light (see New Scientist, 10 May, p 44, for
more on Bayesian theory).

Over the past decade, neuroscientists have found that real brains
seem to work in this way. In perception and learning experiments,
for example, people tend to make estimates - of the location or
speed of a moving object, say - in a way that fits with Bayesian
probability theory. There's also evidence that the brain makes
internal predictions and updates them in a Bayesian manner. When you
listen to someone talking, for example, your brain isn't simply
receiving information, it also predicts what it expects to hear and
constantly revises its predictions based on what information comes
next. These predictions strongly influence what you actually hear,
allowing you, for instance, to make sense of distorted or partially
obscured speech.

In fact, making predictions and re-evaluating them seems to be a
universal feature of the brain. At all times your brain is weighing
its inputs and comparing them with internal predictions in order to
make sense of the world. "It's a general computational principle
that can explain how the brain handles problems ranging from
low-level perception to high-level cognition," says Alex Pouget, a
computational neuroscientist at the University of Rochester in New
York (Trends in Neurosciences, vol 27, p 712).

However, the Bayesian brain is not quite a general law. It is a
collection of related approaches that each use Bayesian probability
theory to understand one aspect of brain function, such as parsing
speech, recognising objects or learning words. No one has been able
to pull all these disparate approaches together, nor explain why the
brain works like this in the first place. An overarching law, if one
exists, should attempt to do this.

This is where Friston's work comes in. In the 1990s he was working
next door to Hinton at UCL. At that time Hinton was beginning to
explore the concept of "free energy" as it applies to artificial
neural networks. Free energy originates from thermodynamics and
statistical mechanics, where it is defined as the amount of useful
work that can be extracted from a system, such as a steam engine. It
is roughly equivalent to the difference between the total energy in
the system and its "useless energy", or entropy.

Hinton realised that free energy was mathematically equivalent to a
problem he was familiar with: the difference between the predictions
made by an artificial neural network and what it actually senses. He
showed that you could solve some tough problems in machine learning
by treating this "prediction error" as free energy, and then
minimising it.

Friston spent the next few years working out whether the same
concept could underlie the workings of real brains. His insight was
that the constant updating of the brain's probabilities could also
be expressed in terms of minimising free energy. Around 2005 he
proposed that a "free energy principle" explains at least one aspect
of brain function - sensory perception.

As a simple example, take what happens when you glimpse an object in
your peripheral vision. At first it is not clear what it is - or, as
Friston would put it, there's a big error between your brain's
prediction and what it senses. To reduce this prediction error,
Friston reasoned that one of two things can happen: the brain can
either change its prediction or change the way it gathers data from
the environment (Journal of Physiology - Paris, vol 100, p 70). If
your brain takes the second option you will instinctively turn your
head and centre the object in your field of view. "It's about
minimising surprise," he explains. "Mathematically, free energy is
always bigger than surprise, therefore if you can minimise free
energy you can avoid surprising encounters with the world."

Friston developed the free-energy principle to explain perception,
but he now thinks it can be generalised to other kinds of brain
processes as well. He claims that everything the brain does is
designed to minimise free energy or prediction error (Synthese, vol
159, p 417). "In short, everything that can change in the brain will
change to suppress prediction errors, from the firing of neurons to
the wiring between them, and from the movements of our eyes to the
choices we make in daily life," he says.

Take neural plasticity, the well-established idea that the brain
alters its internal pathways and connections with experience. First
proposed by Canadian psychologist Donald Hebb in the 1940s, it is
thought to be the basic mechanism behind learning and memory.

Friston's principle accounts for the process by describing how
individual neurons interact after encountering a novel stimulus.
Neuron A "predicts" that neuron B will respond to the stimulus in a
certain way. If the prediction is wrong, neuron A changes the
strength of its connection to neuron B to decrease the prediction
error. In this case the brain changes its internal predictions until
it minimises its error, and learning or memory forming is the
result.

All well and good in theory, but how can we know whether real brains
actually work this way? To answer this question, Friston and others
have focused on the cortex, the 3-millimetre-thick mass of
convoluted folds that forms the brain's outer surface. This is the
seat of "higher" functions such as cognition, learning, perception
and language. It has a distinctive anatomy: a hierarchy of neuronal
layers, each of which has connections to neurons in the other
levels.

Friston created a computer simulation of the cortex with layers of
"neurons" passing signals back and forth. Signals going from higher
to lower levels represent the brain's internal predictions, while
signals going the other way represent sensory input. As new
information comes in, the higher neurons adjust their predictions
according to Bayesian theory. This may seem awfully abstract, but
there's a concrete reason for doing it: it tells Friston what
patterns of activity to look for in real brains.

Last year Friston's group used functional magnetic resonance imaging
to examine what is going on in the cortex during a visual task
(NeuroImage, vol 34, p 1199). Volunteers watched two sets of moving
dots, which sometimes moved in synchrony and at others more
randomly, to change the predictability of the stimulus. The patterns
of brain activity matched Friston's model of the visual cortex
reasonably well. He argues that this supports the idea that top-down
signals are indeed sent downstream to reduce prediction errors.

More recently, Friston's team has shown that signals from higher
levels of the auditory cortex are responsible for modifying brain
activity in lower levels as people listen to repeated and
predictable sounds (Proceedings of the National Academy of Sciences,
vol 104, p 20961). This, too, fits with Friston's model of top-down
minimisation of prediction error.

Despite these successes, some in the Bayesian brain camp aren't
buying the grand theory just yet. They say it is hard to know
whether Friston's results are ground-breaking or just repackaged old
concepts - but they don't say he's wrong. Others say the free-energy
principle is not falsifiable. "I do not think it is testable, and I
am pretty sure it does not tell you how to build a machine which
emulates some aspect of intelligence," says theoretical
neuroscientist Tomaso Poggio of the Massachusetts Institute of
Technology.

Friston disagrees, pointing out that there are experiments that
would definitively test whether or not a given population of neurons
is minimising prediction error. He proposes knocking out a higher
region of the cortex - using transcranial magnetic stimulation, say
- and seeing whether free-energy models can predict how the activity
of a lower region of neurons would change in response.

Several groups are planning experiments along these lines, but they
need to work out exactly which neurons to target. "This would, I
think, be an aspect of the theory that could be proved or
falsified," says Thomas Wennekers, a computational neuroscientist at
the University of Plymouth in the UK.

Meanwhile, Friston claims that the free-energy principle also gives
plausible explanations for other important features of the cortex.
These include "adaptation" effects, in which neurons stop firing
after prolonged exposure to a stimulus like a rattling fan, so after
a while you don't hear it. It also explains other phenomena:
patterns of mirror-neuron activation that reflect the brain's
responses to watching someone else make a movement; basic
communication patterns between neurons that might underlie how we
think; and even the hierarchical anatomy of the cortex itself.

Friston's results have earned praise for bringing together so many
disparate strands of neuroscience. "It is quite certainly the most
advanced conceptual framework regarding an application of these
ideas to brain function in general," says Wennekers. Marsel Mesulam,
a cognitive neurologist from Northwestern University in Chicago,
adds: "Friston's work is pivotal. It resonates entirely with the
sort of model that I would like to see emerge."

So where will the search for a unified theory of the brain go from
here? Friston's free-energy principle clearly isn't the ultimate
theory yet it remains to be tested fully and needs to produce more
predictions of how real brains behave. If all goes well, though, the
outcome will be a concise mathematical law of brain function,
perhaps something as brief and iconic as E=mc^2. "The final equation
you write on a T-shirt will be quite simple," Friston predicts.

On a more practical level, he says the approach will change our
concepts of how the brain works and could help us understand the
deeper mechanisms of psychological disorders, especially those
thought to be caused by faulty connections in the cortex, such as
schizophrenia. It could also shine a light on bigger questions such
as the nature of human consciousness.

There's work still to be done, but for now Friston's is the most
promising approach we've got. "It will take time to spin off all of
the consequences of the theory - but I take that property as a sure
sign that this is a very important theory," says Dehaene. "Most
other models, including mine, are just models of one small aspect of
the brain, very limited in their scope. This one falls much closer
to a grand theory."

The Human Brain - With one hundred billion nerve cells, the
complexity is mind-boggling. Learn more in our cutting edge special
report.

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Weblinks

Karl Friston
http://www.fil.ion.ucl.ac.uk/~karl/
Stanislas Dehaene
http://www.unicog.org/main/pages.php?page=Stanislas_Dehaene
Geoffrey Hinton
http://www.cs.utoronto.ca/~hinton/
The Bayesian Brain (MIT press)
http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=11106