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Wednesday, 26 July 2017

Scientists confess!! There’s a long way to go in understanding the brain.. July 26,2017



Scientists pour a lot of brainpower into
understanding how their experimental equipment
works.
You don’t want to be fooled into thinking you’ve
made a great discovery because of some quirk in
the apparatus you didn’t know about. Just the
other day, a new paper published online
suggested that the instruments used to detect
gravitational waves exhibited such a quirk,
tricking scientists into claiming the detection of
waves that maybe weren’t really there.
It appears that gravity wave fans can relax,
though. A response to the challenge pretty much
establishes that the new criticism doesn’t
undermine the wave discoveries. Of course, you
never know — supposedly well-established
results sometimes do fade away. Often that’s
because scientists have neglected to understand
the most important part of the entire
experimental apparatus — their own brains.
It’s the brain, after all, that devises experiments
and interprets their results. How the brain
perceives, how it makes decisions and
judgments, and how those judgments can go
awry are at least as important to science as
knowing the intricacies of nonbiotic experimental
machinery. And as any brain scientist will tell
you, there’s still a long way to go before
understanding the brain will get crossed off
science’s to-do list. But there has been progress.
A recent special issue of the journal Neuron
offers a convenient set of “perspective” papers
exploring the current state of understanding of
the brain’s inner workings. Those papers show
that a lot is known. But at the same time they
emphasize that there’s a lot we don’t know.
Glancing at the table of contents reveals the
first lesson about understanding the brain: It’s a
complex problem that needs to be approached
from multiple perspectives.
On one level, there’s the dynamics of electrical
currents that constitute the main signaling
method of the brain’s nerve cells. Then on a
higher level there’s the need to figure out the
rules by which nerve cells make connections
(synapses) and create the neural circuitry for
processing sensory input, learning and behaving.
Another challenge is understanding how nerve
cell networks represent memories and how you
recall what you’ve learned. And it’s essential to
understand how neurobiological processing
conducted by molecules and cells and electrical
signaling gets translated into behaviors, from
simple bodily movements to complex social
interactions.
Nerve cells in the brain, or neurons, are known
to communicate among themselves by
transmitting electrical signals, aided by chemical
signaling at the synapses connecting the
neurons. But there are gaps in understanding
how that process takes the brain from
perceptions to thoughts to actions. Each of
Neuron ’s perspective papers both describes
what’s already known about how the brain works
and offers speculations where scientists lack full
knowledge about how the brain does it jobs.
Much of the effort to explain the brain involves
mapping the electrical signaling throughout the
entire network of nerve cell connections. Per
Roland of the University of Copenhagen, for
instance, discusses how those signals vary in
space and time. He emphasizes the important
balance between signaling that incites neurons
to send signals and the messaging that inhibits
signaling, keeping some neurons quiet.
Sophie Denève and colleagues of the Ecole
Normale Supérieure in Paris also emphasize the
balance between excitation and inhibition in
neural circuitry. That balance is important, they
say, for understanding how the whole brain can
learn to do things based on changes in the
connections between individual neurons.
Somehow the rules governing synaptic
connections between cells enable such “local”
activity to modify the “global” neural circuitry
that carries out the brain’s many functions.
Excitation-inhibition balance, plus feedback from
the global network influencing synapse strength,
“can ensure that global functions can be learned
with local learning rules,” Denève and colleagues
write.
Almost all these approaches to figuring out the
brain involve how it manipulates information. In a
sense, the ultimate key question is how the
brain conducts the mysterious process by which
it absorbs information in the form of lights and
colors, sounds, smells and tactile inputs and
transforms them into physical actions — ideally
behaviors that are appropriate responses to the
inputs. Just (OK, not “just,” but sort of) as in a
computer, the brain transforms input into output;
information about the external world is
manipulated to produce information about how to
react to it.
But because sensory input has its limits, and
some of it is ambiguous, the informational
variables of the external world cannot be gauged
with certainty, Xaq Pitkow and Dora Angelaki of
Baylor College of Medicine and Rice University in
Houston point out in their perspective. So the
brain’s behavioral choices must be based on
some method of computing probabilities to infer
the likely state of the world — and then choosing
the wisest (probably) actions in response.
“It is widely accepted that the brain somehow
approximates probabilistic inference,” Pitkow and
Angelaki write. But nobody really knows how the
brain does it. Pitkow and Angelaki propose that
multiple populations of the brain’s neurons
perform various computations to make
appropriate behavioral decisions.
Patterns of electrical signaling by these neurons
must represent the original sensory stimuli —
that is, the patterns in the stimuli are encoded in
the patterns of electrical signaling among the
neurons. Those neural signaling patterns, in
Pitkow and Angelaki’s description, are then
recoded into another set of patterns; that
process sorts out the important variables in the
environment from those that don’t matter. Those
patterns are then decoded in the process of
generating behavioral actions.
In sum, the brain appears to implement
algorithms for collecting and assessing
information about the environment and encoding
that information in messages that tell the body
what to do. Somehow those algorithms allow the
brain to conduct statistical computations that
combine beliefs about the environment with the
expected outcome of different behaviors.
Pitkow and Angelaki present sophisticated
speculation about the possible ways the brain
could accomplish this task. It’s clearly an
unimaginably complicated process, and figuring
out how the brain does it will require more
sophisticated experiments than neuroscientists
have so far imagined. Much research on brain
function in animals, for instance, offers the
animal a choice of two options, given various
external conditions. But tasks of that nature are
vastly simpler than the jobs that evolution
optimized brains for.
“The real benefit of complex inferences like
weighing uncertainty may not be apparent unless
the uncertainty has complex structure,” Pitkow
and Angelaki argue. “Overly simple tasks” are “ill-
suited to expose the inferential computations
that make the brain special.”
And so truly understanding the brain, it seems,
will require better experiments — using apparatus
that is more fully understood than the brain now
is — of sufficient complexity to be worthy of
probing the brain’s abilities.

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