As Ray Kurzweil (2005) keeps emphasizing, the law of accelerating
returns (LOAR) is always operative in the evolution of all information based
technologies, resulting in their exponential growth (Moore's law is just one
example of that). Naturally, progress in the development of better and better
brain-probing technologies is no exception to this. In Part 123 I traced the early history of progress in the
development of experimental techniques for probing the human brain. Our present
capabilities are already mind-boggling, and much more will be coming in the
near future. And needless to say, experiment and theory go hand in hand. Two
examples will illustrate my point.
In Part 122 I told you about the path-breaking Mountcastle
hypothesis, which says:
There is a common function, a common algorithm, that is performed by
all the cortical regions.
Kurzweil (2012) has rightly emphasized the fundamental
importance of this insight: 'A critically important observation about the neocortex is the extraordinary
uniformity of its fundamental structure. This was first noticed by American
neuroscientist Vernon Mountcastle (born in 1918). In 1957 Mountcastle discovered
the columnar organization of the neocortex. In 1978 he made an
observation that is as significant to neuroscience as the Michelson-Morley
ether-disproving experiments of 1887 were to physics. That year he described
the remarkably unvarying organization of the neocortex, hypothesizing that it
was composed of a single mechanism that was repeated over and over again, and
proposing the cortical column as that basic unit'.
Another basic insight is that the basic module of
learning is a module of dozens of neurons (~100) (cf. Part 123). Support for this postulate has come from the
work of Henry Markram. His ambitious Blue Brain Project aims to both
model and simulate the human brain, including the entire neocortex, as also the
old-brain regions such as the hippocampus, amygdala, and cerebellum: 'Reconstructing
the brain piece by piece and building a virtual brain in a supercomputer—these
are some of the goals of the Blue Brain Project. The virtual brain will
be an exceptional tool giving
neuroscientists a new understanding of the brain and a better understanding of
neurological diseases'.
This project is using a scanning-technology tool called the automated patch-clamp robot, with which researchers are 'measuring the
specific ion channels, neurotransmitters, and enzymes that are responsible for
the electrochemical activity within each neuron'. It is 'an automated system
with one-micrometer precision that can perform scanning of neural tissue at
very close range without damaging the delicate membranes of the neurons'. The
scanning technology has been already used for simulating a single neuron (in
2005), a neocortical column consisting of 10,000 neurons (in 2011), and a
neural mesocircuit consisting of 100 neocortical columns (in 2011).
The scientists developed this
method to automate the process of finding and recording information from
neurons in the living brain. It has been shown that a robotic arm guided by a
cell-detecting computer algorithm can identify, and record from, neurons in the
living-mouse brain with better accuracy and speed than a human experimenter.
The automated process eliminates the need for months of training, and provides
long-sought information about the activity of living cells.
Using this
technique, scientists could classify the thousands of different types of cells
in the brain, map how they connect to each other, and figure out how diseased
cells differ from normal cells. To quote the authors (Kodandaramaiah et al.): 'Whole-cell
patch-clamp electrophysiology of neurons is a gold-standard technique for
high-fidelity analysis of the biophysical mechanisms of neural computation and
pathology, but it requires great skill to perform. We have developed a robot
that automatically performs patch clamping in vivo, algorithmically
detecting cells by analyzing the temporal sequence of electrode impedance
changes. We demonstrate good yield, throughput and quality of automated
intracellular recording in mouse cortex and hippocampus'.
As quoted by Kurzweil (2012), Markram wrote in a
2011 paper that while he was 'search[ing] for evidence of Hebbian assemblies (collections
of neurons that are arranged together) at the most elementary level of the
cortex', what he found instead were 'elusive assemblies [whose] connectivity
and synaptic weights are highly predictable and constrained'. He concluded that
'these findings imply that experience cannot mold the synaptic connections of these
assemblies', and speculated that 'they serve as innate, Lego-like building
blocks of knowledge for perception and that the acquisition of memories involves
the combination of these building blocks into complex constructs'.
Here is more from Markram: 'Functional neuronal
assemblies have been reported for decades, but direct evidence of clusters of
synaptically connected neurons . . . has been missing. . . . Since these
assemblies will all be similar in topology and synaptic weights, not molded by
any specific experience, we consider these to be innate assemblies . . .
Experience plays only a minor role in determining synaptic connections and
weights within these assemblies . . . Our study found evidence [of] innate
Lego-like assemblies of a few dozen neurons
. . Connections between assemblies may combine them into
super-assemblies within a neocortical layer, then in higher-order assemblies in
a cortical column, even higher-order assemblies in a brain region, and finally
in the highest possible order in the whole brain . . . Acquiring memories is
very similar to building with Lego. Each assembly is equivalent to a Lego block
holding some piece of elementary innate knowledge about how to process,
perceive and respond to the world. . . When different blocks come together,
they therefore form a unique combination of these innate percepts that
represents an individual's specific knowledge and experience'.
Further evidence for a regular structure of
connections across the neocortex was published in the March 2012 issue of the
journal Science by Van J. Wedeen et al. They write: 'Basically,
the overall structure of the brain ends up resembling Manhattan, where you have
a 2-D plan of streets and a third axis, an elevator going in the third
dimension'.
As Wedeen said in a Science
magazine podcast, 'This was an investigation of the three-dimensional structure
of the pathways of the brain. When scientists have thought about the pathways
of the brain for the last hundred years or so, the typical image or model that
comes to mind is that these pathways might resemble a bowl of spaghetti –
separate pathways that have little particular spatial pattern in relation to
one another. Using magnetic resonance imaging, we were able to investigate this
question experimentally. And what we found was that rather than being
haphazardly arranged or independent pathways, we find that all of the pathways
of the brain taken together fit together in a single exceedingly simple
structure. They basically look like a cube. They basically run in three
perpendicular directions, and in each one of these three directions the
pathways are highly parallel to each other and arranged in arrays. So, instead
of independent spaghettis, we see that the connectivity of the brain is, in a
sense, a single coherent structure'.
A very precise form of scanning technology was used
for revealing the grid-like structure of the connections, involving a variety
of noninvasive scanning technologies, including new forms of MRI, magnetoencephalography,
and diffusion tractography (a method to trace the pathways of fibre bundles in
the brain).
This is incredible stuff! A great triumph of modern
science, and of the scientific method! I re-quote: '. . . we find that all
of the pathways of the brain taken together fit together in a single
exceedingly simple structure. They basically look like a cube. They basically
run in three perpendicular directions, and in each one of these three
directions the pathways are highly parallel to each other and arranged in
arrays'.
As Kurzweil (2012) explains, 'Whereas the Markram
study shows a module of neurons that repeats itself across the neocortex, the
Wedeen study demonstrates a remarkably orderly pattern of connections between
modules. The brain starts out with a very large number of
"connections-in-waiting" to which the pattern recognition modules can
hook up. Thus if a given module wishes to connect to another, it does not
need to grow an axon from one and a dendrite from the other to span the entire
physical distance between them. It can simply harness one of these
connections-in-waiting and just hook up to the ends of the fiber. As Wedeen and
his colleagues write, "The pathways of the brain follow a base-plan
established by . . . early embryogenesis. Thus, the pathways of mature brain
present an image of these three primordial gradients, physically deformed by development".
In other words, as we learn and have experiences, the pattern recognition
modules of the neocortex are connecting to these preestablished connections
that were created when we were embryos' (emphasis added).
This is rather like the field-programmable gate
arrays (FPGAs) I described in Part 111. Humans developed the technology of FPGAs, not
knowing that their own brains have evolved to have a similar configuration and
working principle!
Here is a pictorial summary of the present status
of tools for imaging the human brain (from Kurzweil 2012):
Wedeen, whose
work I mentioned above, is also involved in the truly ambitious Human Connectome Project, which aims
at mapping the wiring diagram of the entire, living human brain. The project aims to be
completed by 2014.
You may also like to watch this Youtube video reporting
some very recent progress in 3D visualization of the brain. Called the Glass Brain, it is a 3D brain visualization that displays source and connectivity data based on real-time EEG, using BCILAB technology and Unity3D.
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