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Saturday, 22 March 2014

124. Peering Into the Human Brain



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.