The Global Warming controversy is ended. Right.

Take a look at the graph above. It shows the results of global temperature measurements over a span of some 100-200 years as compiled by four groups: NASA Goddard Institute for Space Studies, NOAA (National Oceanic and Atmospheric Administration), United Kingdom Meteorology Office Climatic Research Unit (hadCRU), and the Berkeley Earth Surface Temperature Project. Notice that the graph lines are almost identical and they all show a strong trend in global warming.

This is not exactly news, is it? No, but one line in the graph of particular interest is from a report that is making its way toward official release. It’s important because the data for that line on the graph is from those skeptical of scientific measurement of global warming. The Berkeley Earth Surface Temperature Project was begun by University of California physics professor Richard Muller, a man highly critical of the manner in which climate scientists were gathering and manipulating their data. Initial project funding included sources that generally contribute to climate change denial. Muller’ stated goal was to establish an independent source of climate data that would be thoroughly vetted for bias and error.

The Berkeley team, ten scientists led by Robert Rohde, a specialist in climatology with large data sets, included Saul Perlmutter, this year’s Nobelist in physics. The goal was to assemble a merged set of climate data from surface weather stations, check it for various errors, bias, or other distortions, analyze it with new and existing statistical methods, and provide public access to all the data and results. The result of the multi-year project is a database of 1.6 billion records of climate data, and a report that is now available at the website [Berkeley Earth Surface Temperature], which is heading for peer review and publication. Though not final, this is the official report.

The report is based on data collected from about 40,000 weather stations around the world. It will be difficult to impugn the source, as the Berkeley project explicitly stated that the quality of weather station reporting was sufficiently reliable and more importantly did not reflect the contention that modern ‘urban heat islands’ (the heat generated by cities and roads) affected a significant number of measurements. I would say that this data was also subjected to scrupulous statistical analysis, although that will have to wait for the peer review process to be validated. The essential results, in the words of Dr. Muller:

“Our biggest surprise was that the new results agreed so closely with the warming values published previously by other teams in the US and the UK,” said Professor Muller. “This confirms that these studies were done carefully and that potential biases identified by climate change sceptics did not seriously affect their conclusions.”

[Source: BBC News]

The findings of the Berkeley project agree that the global temperature has increased 1 degree Celsius since 1950 and the trend is up. In short, global warming is real.

So here we are in 2011, more than twenty years after the first warnings about the rise in global temperatures, and most people in the United States still think there is no global warming. The Republican Party has virtually enshrined climate change denial as part of its platform. At the same time, the U.S. military is planning for the effects of global warming on world politics and conflict. The energy industry is preparing a full-scale development of the Arctic petrochemical fields as the ice recedes, and plans are already in motion for shipping routes through the Arctic seas. Many countries, especially island states around the world, are making plans for rising coastal waters. This is what some like to call cognitive dissonance, the discrepancy between what people choose to believe and what is actually happening around them.

Will the addition of one more global warming report, albeit from a group inclined to be skeptics, have an impact? Watch your favorite media outlets. Will the results be highlighted? Will they be mentioned even once? Then judge for yourself.

First a little background: As a simple description, methylation is a switch mechanism for DNA. Chemically, methylation of DNA takes place when a methyl group (a molecule of carbon and hydrogen, CH₃) is added to either cytosine or adenine (two of the bases of DNA). The addition of a methyl group disables (turns off) the corresponding gene, that is, the gene is no longer available to guide the production of protein. Removing the methyl group, demethylation, reverses the process (turns it on). Methylation or demethylation is used within the genome to set up a pattern of active and inactive genes so that, among other things, cells become specialized. For example, a particular methylation pattern directs a cell to become a heart muscle. That pattern in then passed on (inherited) through subsequent cell divisions. The overall process is called epigenetic regulation and is one of if not the principle means of determining cell development, as in a stem cell that becomes a heart cell and stays that way through the life of an organism. It is also used to modify cell functioning in response to environmental conditions. As a rule, changes in epigenetic regulation are not inheritable through the egg or sperm (intergenerational), although the number of known exceptions is growing. The discovery and study of methylation and epigenetics is not much more than thirty years old with the bulk of the research starting in the 1990’s – it is a very young field.

Recent research in methylation continues to expand its reach. Neuroscientists primarily at the Johns Hopkins Brain Science Institute and led by Hongiun Song have been working on the role of methylation in the genome of brain cells (neurons). In previous research they had discovered that the brain cells of mice could be induced to faster growth through electric shock, which was decreasing the amount of DNA methylation. In the recent work, published in Nature Neuroscience [28 August 2011, paywalled, Neuronal activity modifies the DNA methylation landscape in the adult brain] they sequenced the genome of electrically stimulated and non-stimulated mouse brains and compared the results. They found that stimulated brains decreased or increased methylation (cytosine methylation) by 1.4%.

More significantly, they discovered that demethylation was taking place in neurons that were non-dividing on a large scale. This was surprising, as existing scientific consensus held that non-dividing cells were basically passive and the methylation would change very little, if at all. In other words, methylation in brain cells was almost exclusively for directing cell development.

“It was mind-boggling to see that so many methylation sites — thousands of sites — had changed in status as a result of brain activity,” Song says. “We used to think that the brain’s epigenetic DNA methylation landscape was as stable as mountains and more recently realized that maybe it was a bit more subject to change, perhaps like trees occasionally bent in a storm. But now we show it is most of all like a river that reacts to storms of activity by moving and changing fast.”

[Source: Johns Hopkins Medical Center]

Dr. Song’s expression is rather poetic, but the impact of this finding could be far-reaching. It implies that the methylation process in neurons has a function other than cell development (via cell division). What that function is and how exactly it works remains high on the research agenda. Since it is known that in other circumstances methylation is a means of responding to the environment, it is possible that a similar process is at work in the brain. I should mention that other studies have already forwarded the idea that methylation, or some form of epigenetics, may be the basis of memory.

Specifically: Neutrinos produced by the Super Proton Synchrotron at CERN in Geneva Switzerland were directed toward the Gran Sasso facility deep within the mountain of the same name, a distance of some 730km (455 mi). All other nuclear particles won’t go that far, but among the many weird particles in physics, neutrinos take the prize. In this case, the relevant fact is that neutrinos pass through matter – rock, metal, water – like matter doesn’t exist. So, 0.0024 seconds after the neutrinos are produced in Switzerland, they show up passing through bricks of photographic film in Italy. The interesting part, gathered from the OPERA experiment, is that at least some of the neutrinos show up 60 nanoseconds faster than if they were travelling at the speed of light. That’s 0.00000006 of a second faster. The results are published at arXiv [22 September 2011, Measurement of the neutrino velocity with the OPERA detector in the CNGS beam].

If the experimental results are correct, this is…like…awesome. I’m being deliberately obtuse. I know the number is very small; but that is absolutely and relatively not the point. If the speed of light can indeed be broken, a whole lot of physics needs to be re-thought and Einstein probably needs to be reburied. At the moment the implications of the discovery seem to be driving much of the coverage, but it’s way too early for anything other than thought experiments. Real science needs to do its thing. Discoveries of this magnitude must be verified, repeated, challenged and confirmed or not confirmed. The scientists involved are, obviously, no dummies. They are well aware of the burden of proof. They did what they could to check, re-check, examine and criticize their methods and results. Now they have released the information, which they consider preliminary, to the rest of the scientific community. Have at it! Which indeed they will because as Carl Sagan was fond of saying: Extraordinary claims require extraordinary evidence. Besides, nothing gets scientists engaged so much as something that threatens to destroy their work.

If you polled 100 physicists this morning, probably 98 of them would say something like, “What interesting results! No doubt wrong, of course.” The probabilities are that’s correct. Equipment calibration, measurement error, unidentified forces – there are many ways an experiment of such delicacy and precision can go wrong. However, the original scientists did their homework; they established a six-sigma level of confidence in their results. Five sigma would normally suffice to get attention.

However, don’t hold your breath waiting for a resolution. It will take years to play out. New experiments to recreate the measurements will need to be put in place; that alone could take months to years. Undoubtedly there will be some new theoretical modeling. There will be controversy. So be patient; there should be a number of interesting moments yet to come.

Neuroscientists have known for some time that nerve endings near the critical junction points between nerve cells, the synapses, are filled with tiny sacs (like microscopic bubbles) containing chemicals called neurotransmitters. Neurons work by sending a pulse of electro-chemical energy along the length of the cell (through nerve fibers of the axons) until it reaches a synapse – a gap between the end of one neuron and the beginning of another. For the pulse to cross this gap it must trigger the release of neurotransmitters stored in vesicles (those tiny sacs) that cross the gap. Depending on how much and what kinds of neurotransmitters are released, they may fire triggers in the next neuron and so the nerve impulse passes on, from toe to brain in some cases. This is not, as you can tell, the convenient metaphor of an electrical charge travelling down a wire. We are not ‘wired,’ actually; it’s more like tenuously and conditionally connected, something like your computer connected to Wi-Fi.

This is not the most efficient or high speed way of transmitting an electrical signal (parenthetically, some types of neuron actually do act like an efficient ‘wire’); so why are most neurons set up this way? That’s one of the big questions in neuroscience. I mention this because it’s also been clear for some time that the function of the synaptic vesicles and their neurotransmitters is to ‘filter’ or ‘weigh’ the meaning or importance of a pulse coming across the synapse.

An important sub-question for neuroscientists concerns the nature of the vesicles. There are two kinds, identified many years ago because some of them sit around the very end of neuron and release the neurotransmitters when an impulse arrives, and others (the majority) are pooled-up nearby. As far as scientists could tell, the two kinds of vesicles were identical, except that one was in a ‘resting’ pool and the others were in the ‘recycling’ pool. How do these two pools of vesicles interact? What roles are played? The chemistry involved, especially in the composition of the neurotransmitters and their function in transferring electrical information from one neuron to another, is very complicated. It all added up to a lot of unknowns, a mystery, if you like.

A research team at the University of California San Francisco (USA) under Robert Edwards and publishing in the journal Neuron [11 August 2011, paywalled, v-SNARE Composition Distinguishes Synaptic Vesicle Pools] has developed the first evidence that the two vesicles are not the same, that they are defined by having different proteins on their surface.

By using very high powered microscopes and ‘labeling’ specific proteins with glowing markers derived from jellyfish, they were able to determine that the vesicles in the resting pool have a high concentration of the protein VAMP7 on their surface. From this they were able to learn that the particular protein is involved in regulating the release of neurotransmitters, and that there are connections between the behavior of ‘resting’ and ‘recycling’ vesicles.

At this point, it would seem the appropriate reaction might be, “That’s it?” Yes, for the moment. Like a lot of important advances in science, this is one of those seemingly little keystone pieces. It’s a starting point, a piece of knowledge, upon which a much greater edifice will eventually be built. Now that scientists know that the two kinds of vesicles are different in their protein composition and that the protein(s) involved are significant in the release of neurotransmitters, it opens up a vast new area of research. It also points in the direction of that big question in neuroscience, how (and eventually why) do synapses use vesicles to transmit information? It appears that proteins may be at the basis of answering that question. This is consistent with the discoveries across the board in biochemistry that the role of proteins is far more crucial and complex than previously thought. Thus the burgeoning of the new field of proteomics, the study of proteins.

I was debating whether this story has real ‘impact’ or is it just another one of those many steps in the process of scientific discovery. Well, it is that, but I think that in this case science is dealing with one of the most fundamental biological mechanisms, finally dealing with it at the molecular level. In there, we can suspect, might be some answers to how the brain (all the trillions of connections and billions of neurons) turns into ‘mind’ and ‘consciousness.’ This discovery is a long way and maybe decades from that, but just learning that vesicles are different in their use of proteins points neuroscience in a new and potentially important direction.

It would be futile in a blog entry to attempt a satisfying description of supersymmetry and its relationship to the Standard Model of particle physics. Anyway, this piece is about scientists reacting to new information, not about the inner workings of the theory. Still, for convenience: supersymmetry, often shortened to SUSY, is a theory that relates to symmetry between certain elementary particles that are similar to those in the Standard Model but have different characteristics – so called superparticles. Symmetry for these superparticles consists of having for every type of boson a corresponding type of fermion. This relationship forms what are known as superpartners. Superpartners are not considered to belong to the Standard Model of physics.

Supersymmetry was developed in large part to deal with certain problems in the Standard Model. For example, the problem that there is more matter in the universe than we can detect – the problem of so called dark matter. Supersymmetry provides the existence of superparticles to explain dark matter. It also can explain the Higgs boson and some aspects of cosmology. As many a physicist will tell you, SUSY is a lovely theory, earning the high praise of ‘elegant.’ Just one problem, a big one: There is no evidence that superparticles exist.

One of the reasons for building the very expensive high speed colliders, most notably the Large Hadron Collider (LHC) in Switzerland, is to look for superparticles. Up to the point where the LHC came on line, other colliders at lower levels of energy had failed to provide conclusive evidence for the existence of superparticles. It was hoped that the LHC would find them at higher levels. So far, it has not.

That was the message given to physicists at the Lepton-Photon Conference (August, 2011) in Mumbai, India. Most specifically, data from the LHC Beauty experiment, which looked at the frequency of decay for B-meson particles as an indicator of the presence of superparticles (more superparticles, more frequent decay), came up negative. Other LHC experiments also failed to definitively isolate a superparticle at a variety of energy levels.

Generally this might be called bad news. I’m certain many scientists don’t consider it good news. However, as a piece of science, it’s just news – a report on new data. One way to look at it is as a spectrum of data from a variety of experiments. Over the years, colliders in various parts of the world have run experiments attempting to show, directly or indirectly, that superparticles exist. One of the main elements in the experiments was the level of energy at which the ‘collisions’ of particles took place. It was always suspected that superparticles would not appear except at very high energy levels. As expected, at lower energy levels, nothing was detected. Then the Tevatron particle accelerator (Chicago, USA) seemed to show a higher B-meson decay level. The LHC, running at Tevatron and higher energy levels, now has been unable to confirm this.

The data from these tests is enormous in volume and often tricky to analyze; it can take months or years and even then results are almost always ‘tentative.’ Nevertheless, cumulatively and at an ever greater range of energy levels, no convincing evidence for superparticles has been found. Physicists, like every other scientists, have their skeptics and nay-sayers, whose role it is to raise a hand and say – enough. In this kind of science, which is very expensive, the hands go up early (often before experiments are run) and starting waving vigorously when experiments don’t show positive results. Those hands are now joined by quite a few moderate supporters of supersymmetry.

This does not mean that SUSY is dead. Far from it, as bad news typically stimulates the creative juices in a search for other forms of supersymmetry, usually far more complicated forms, to explain the lack of data. Still, data drives the theory in the end. Lack of data suggests the need for a new theory. The news will also spur those physicists who are working on alternative explanations for dark matter and other problems with the Standard Model. Indeed, it will spur a few to increase their questioning of the Standard Model.

This is called scientific ferment. It’s a kind of purgatory for practicing scientists – old theories appear to be dying, new theories are unproven – and a lot of careers are in the balance. It’s hard to be gracious and some individuals fail under pressure. Others rise to the occasion and boost new ideas. To the rest of the world, the uncertainty may seem like chaos. Witness the hash made of conditional scientific consensus in climate science by the deniers of global warming. Supersymmetry, whether it exists or not, doesn’t decide the fate of the universe (or..probably not), but it’s fundamental science so in an intellectual sense, at least, the stakes are high.

The results from the LHC are not totally conclusive, so there will be a call for more experiments and even bigger colliders. Others will say, we’ve run out the string, we can’t afford to go further. The question for everyone in the field of particle physics is: Where do we go from here? Arriving at an answer to that question will be an interesting process, even for non-physicists.

I can see the “?” form over your head. I’ll explain in a bare moment, but first the “Why?” Using the new DNA, biochemists will be able to create proteins that mimic disease conditions, or components of diseases – and turn them on or off as part of experimental testing. Doing this should give scientists much greater insight into the role of proteins in diseases and how to control them. In short, it creates a kind of ‘sandbox’ (controlled) environment to test hypotheses about diseases and how they work. Now, for a bit of explanation…

The place to start is with the concept of phosphorylation. This is what happens to proteins when a phosphate group (PO4) is added, it activates (or deactivates) the protein (often an enzyme) like a chemical on-off switch. Phosphorylation is one of the most fundamental of biochemical processes, used within cells for an untold number of chemical pathways, and the subject of an enormous amount of research. Interestingly, although phosphorylation is crucial to the timing and control of many biological processes, it is not something directly controlled by DNA. It falls into the area known as epigenetics, genetic (reproducible and sometimes inheritable) characteristics that occur largely outside of the usual DNA/RNA mechanics.

What the Yale researchers did is take the phosphorylation capability and code it into the genome, into the DNA of E. coli, a remarkable technical feat. They added the ability to synthesize phosphoserine, a key compound in the phosphorylation of enzymes (among other things), and added it to the natural bag of genetic tricks – using synthetic biology to produce a new form of natural biology (so to speak).

The outcome of adding new DNA to E. coli is that the bacteria can produce ‘natural’ proteins, proteins that scientists choose to create, that have the phosphorylation capability built into them.

“Essentially, we have expanded the genetic code of E. coli, which allows us synthesize special forms of proteins that can mimic natural or disease states,” said Jesse Rinehart co-corresponding author of the research. “What we have done is taken synthetic biology and turned it around to give us real biology that has been synthesized.”

[Source: EurekAlert]

This does sound like a new sandbox to play in, but there are probably rules and limitations built into this sandbox that are yet to be discovered. I’m using the sandbox analogy to emphasize that while the ability to synthesize phosphorylate proteins via DNA is a powerful concept, it may yet prove to have problems in practice. It’s certainly possible that synthesizing disease proteins with phosphorylation built-in is a key to understanding the interaction of living proteins and disease conditions. However, there is more – much more – to the complexity of biological processes that is not well understood, especially in the area of epigenetics. A few years down the road, and the techniques used in this research may be deemed ‘flawed’ or ‘too difficult’ or any number of other conditions that will sideline the technique. Or this could be the beginning of a highly successful approach that opens the research doors to the understanding of many forms of disease. Hopefully so.

The next steps for the Yale team is to actually manufacture proteins for diseases such as cancer(s), type 2 diabetes and hypertension (for example) and put them into experimental environments where they can be observed in vivo or in vitro.

Believe me, if this information is all you’ve heard or remembered (if, of course, you’ve seen anything at all); you’ve just caught sight of the first icy pinnacle of the above water iceberg.

As is typical, what gets the most attention is the thing someone made, in this case the neural core chips. In this case, that misses something far more important – the history and progress of a specific research unit within IBM, the Cognitive Computing group, and its chief scientist, Dharmendra Modha. The neural core chip isn’t some one-off research product; it’s a component that researchers decided was necessary to make progress in a massive research program that began in 2006. Funded to the tune of many tens of millions of dollars [most recently$21 million by the Defense Advanced Research Projects Agency (DARPA) for Phase 2 of the Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project], the Cognitive Computing group encompasses the efforts of IBM’s Almaden Research Center, IBM’s T. J. Watson Research Center and five academic institutions (Columbia University; Stanford University, Cornell University; University of California, Merced; and University of Wisconsin, Madison).

The Cognitive Computing group first made news in 2007 with a mouse-scale brain simulation, followed by a rat-scale brain simulation, then in 2008 a cat-scale brain simulation and finally a simulation of a monkey brain. At each step the simulation required a much bigger supercomputer and it became apparent to the researchers that a traditional computer with enough power to achieve a human-scale simulation would require so much energy, it would probably incinerate itself. Yet the human mind doesn’t (usually) incinerate, in fact, it operates rather nicely at about 10 watts, a rather dim bulb. Modha and his team came to realize that if computers were going to achieve human level mental complexity, they too would have to use less energy. This demanded a different model of computing, hardware and software, than the current mainstream (von Neumann) computers. The new model, as expressed by the tiny, low power building blocks of the neural cores is the cognitive computer.

The Big Picture here is the analogy Modha uses, comparing traditional computers with the left side of the human brain and the cognitive computer (composed of neural cores) with the right side of the brain. In this picture you need both sides to make a complete brain. It’s a useful analogy but as usual beware analogies. The cognitive computer, if and when it gets built, will not be a brain hemisphere. For one thing, as neuroscientists will freely admit, we don’t know how the brain works, much less how thinking works. Yes, there has been great progress in understanding the physical and chemical processes of the brain – enough progress so that the knowledge can inspire ideas like the cognitive computer, but there is no one-to-one correspondence between what we currently know about the brain and the ability to design artificial intelligence.

In an important sense, it doesn’t matter. The inspiration may be enough. For example, in the neural cores IBM has created a silicon chip that weaves the function of memory (RAM) into the function of processing (CPU) so close together (45 nanometers) – like neurons and synapses in the brain – so that an enormous amount of energy is saved at each moment of processing (a thousand times less energy than a transaction in a standard computer). The neural core chips run cool, so to speak, and achieve something already accomplished in the brain without needing to be exact copies of the brain. Indeed, there is nothing biological in the neural core chips.

Another crucial element of the cognitive computer that usually is unmentioned in a discussion about the neural chips is the software. Software in this case isn’t the traditional programming – logic, rules, step-wise sequences – of the von Neumann type computer, instead it is the means of interconnecting the operation of the neural cores so that the system learns. Learning is its programming; something like it is for all animals.

It’s impossible to do justice to this research in something short of a book (an e-book anyway). It’s also possible to overlook the possibility that this approach won’t lead to the results the researchers want. In any case, the cognitive computer, which I repeat doesn’t exist yet, does not get to sentient artificial intelligence. It will, however, be a really big step in that direction. Whether the notion of cognitive computers is ever completely realized, the process of developing the notion will teach us a great deal, for we remain the ultimate in learning machines.

The basis of this approach, phase-change materials (PCM) is not new at all. Solid, liquid and gas are the phases of most materials, for example, H2O – ice, liquid water, water vapor. As the materials pass from one phase to another (solid-solid, solid-liquid, solid-gas, liquid-gas) they give up or store heat. Some materials exchange more heat energy than others and those are the ones identified as phase-change materials. For example, salt hydrates, fatty acids and various paraffins are PCMs and have been used to store heat since the late 1800’s.

It’s the property of dramatically changing energy level that interests computer scientists. A phase-change material that starts at one low energy state (0) and after an electrical charge has a detectably high energy state (1) can be the basis for a memory storage device, or a calculation register. These are the basic components of a digital computer, and that’s why PCM materials are on the way to use in commercial memory devices. What is relatively ‘new’ is the attempt to use PCM materials that in some rudimentary way emulate neurons (brain cells).

Specifically, a paper from a team at the University of Exeter (Cornwall, UK) published in Advanced Materials [22 June 2011, paywalled, Arithmetic and Biologically-Inspired Computing Using Phase-Change Materials] gives credit to being inspired by biological systems (read: neural synapses) in their search to find an electronic equivalent. There are two key points to their approach: One is that the device they built can do both memory storage and digital processing simultaneously. Second, they are using materials that react photonically, that is, their light reflecting properties change.

The device they built as proof of concept is called a memflector because it retains a direction of optical reflectance (the direction in which the light refracts). The memflector is often compared to another recent development, the memristor, which retains a level of electrical resistance depending on the direction of current flow. The Exeter memflector uses a special phase change material known as a chalcogenide (one of 16 elements in the periodic table) in this case an alloy of germanium, antimony and tellurium.

The memflector is where the ‘synaptic-like’ behavior occurs. Somewhat like a synapse it is responsive to aggregating electrical impulses, an ‘integrate and fire’ response pattern. This also sets the optical reflectance of the memflector. The memflector package, according to the researchers, constitutes a ‘hardware neuron.’ In their own words:

Lead author Professor David Wright of the University of Exeter said: “Our findings have major implications for the development of entirely new forms of computing, including ‘brain-like’ computers. We have uncovered a technique for potentially developing new forms of ‘brain-like’ computer systems that could learn, adapt and change over time. This is something that researchers have been striving for over many years.”

[Source: Exeter University]

This does sound like PR gush. The skeptic in me wants to say, “could be.” In any case, the work here is rudimentary, a proof of concept, and a very long way from demonstrating ‘brain-like’ capability, much less a ‘new form of computing.’ That doesn’t mean a phase-change computer won’t happen. What’s important now is how many others pick up the line of research. I don’t doubt the approach is in some scientific respects ‘reasonable’ but other scientists, particularly ones with a commercial eye, will quickly evaluate this approach for problems in manufacturing and scaling.

Whether this particular approach works or not, I think the take-away on it is that many scientists are not satisfied with the mainstream notion that digital computing as we now know it is sufficient for duplicating or emulating human brain function. Many have said that throwing yottaflops (that’s 10 to the 24th floating point operations per second) of processing speed at ‘brain-like’ calculations won’t add up to ‘brain-like’ functionality. Some other kind of computer, perhaps the phase-change computer, will be necessary.

Philosophers and scientists have been pondering, poking and experimenting around the concept and physical reality of memory for centuries. Saying, “We’re a lot closer now,” is probably true, but like determining the properties of an elephant, what we know is probably bits and pieces. That said, as new pieces are added, something that resembles a working hypothetical framework is emerging, and science thrives on frameworks that answer questions and lead to testable results.

A new piece, and potentially a very important new piece, has been added by John Lisman and Zalman Kekst at the Lisman Laboratory at Brandeis University (Boston, Massachusetts, USA) and published in the Journal of Neuroscience [22 June 2011, paywalled, Role of the CaMKII/NMDA Receptor Complex in the Maintenance of Synaptic Strength]. In short, memory appears to be related to proteins that exist in the unique space between neurons called the synapse.

The finding, which I’ll describe in more detail in a moment, is not in itself surprising. Neuroscientists have suspected for some time that proteins are involved in the memory process. It figures, because proteins are the ‘building blocks of biology.’ They are the most flexible, adaptable, and varied of all the biochemical materials. Why wouldn’t memory, which probably requires trillions of coding possibilities, make use of proteins? Well, it hasn’t always been seen that way. Among the many models of how memory works, it was held for some time that neurons themselves, brain cells, were created, shaped and connected to create memory. That model is in the process of being superseded by findings that indicate memory is more likely created in the synapses between neurons.

I won’t get deeply into neuroscience 101, but perhaps you’re familiar with the image of a neuron with a long ‘tail,’ an axon lead out from it and coming up next to – but not touching – another axon from another neuron. The tiny space between axons is the synapse, where the most complicated of all electrobiochemistry takes place, and where a bioelectrical impulse (action potential) ‘jumps,’ is transmitted, between axons. Only the jumping isn’t always the point. Neuroscientific research is reaching consensus that the ‘strength’ of the synapse (to oversimplify, its electrical charge) in some brain neurons is an element of memory encoding. Increase the strength and a bit of information is stored. Decrease the strength below a certain point, and that information is erased.

I’m using some terminology used around computer memory because it’s familiar. Care should be taken not to assume that what happens in the synapses is like computer memory. At this point, it’s only an analogy.

This model or hypothesis about memory has been the focus of research for about two decades, and as I mentioned it has been suspected that proteins are involved. Now, according to the new findings, a specific protein complex has been identified. It has an unmemorable (!) name: Calcium/calmodulin-dependent protein kinase II or CaMKII, combined with a NMDAR glutamate receptor to make the CaMKII/NMDAR complex, which doesn’t help much. This complex determines how strong a synapse is, which translates into how well memory is stored.

A long chain of research, which revealed the presence of this complex in the material of the synapse, led to specific experiments based on simple logic: More of the complex in the synapse would indicate a strong memory; less of the complex (or none) would indicate a weak or non-existent memory. It’s kind of the put-in-take-out sort of experiment that can lead to solid results – which in this case is what happened. Using neuron segments from the hippocampus of rats, an area long associated with memory formation, the scientists used a chemical to dissolve the complex, which should lead to a loss of memory. It did. Learning, the reverse process, has been shown to increase the quantity of the complex.

So, according to this research, at least one protein complex has been associated with memory formation and loss. No one is claiming this is the end of the story. Obviously, there are other proteins or other chemicals involved, some of which may not be fully discovered. Perhaps there are aspects to the proteins, such as the configuration (folding) that play a role. The precise mechanism of protein formation in this complex is not fully understood. Of course, the 64 trillion connection question, is what, if any, is the code used for memory? The upshot is that neuroscientists don’t know yet how memory works, BUT isolating at least one of the crucial protein complexes involved is a big step in the (hopefully) right direction.

The impact of this finding is not that the elephant in the room has been fully revealed, but that the blind men attempting to figure out what it is have another piece of fact to help shape the next round of exploration. If you’ve ever seen a room full of excited blind men dancing around an elephant…neither have I, but I presume that neuroscientists celebrate their own prospects.

Three words stand out that elevate the importance: graphene, circuit and IBM. Graphene as you probably have heard by now is the non-new pure carbon material with unsuspected properties that were, in part, made practical to the world of research as recently as 2004 (and resulted in a Nobel Prize for the effort). Since then, the pace of research and application development has been nothing short of astonishing. Significantly, one of the leaders in that research has been IBM. [SciTechStory: Graphene transistors] IBM was among the first to produce a working transistor using graphene (2009-2010), which at the time was considered difficult because graphene is not naturally a semiconductor (unlike silicon, for example). However, even the first working graphene transistor IBM built was already twice as fast as a comparable silicon transistor. That meant full speed ahead, in more ways than one.

As one IBM engineer put it, a working transistor is nice but it means nothing until it’s connected to something. That was the next step, which IBM just announced. That’s where the word circuit comes in, specifically in what is called an integrated circuit (IC) meaning that many transistors are connected to perform a task. In this first instance, IBM scientists at the Thomas J. Watson Research Center (New York, USA) constructed what is called a broadband radio-frequency mixer, used in radio applications to process signals at different frequencies. It’s a standard IC component, and while what IBM built what is essentially a ‘proof of concept’ device, it demonstrates that graphene transistors can be integrated (which was not easy).

That’s where IBM manufacturing expertise becomes crucial. Proof of concept and prototypes in the laboratory are one thing, commercial products in mass production are quite another. While IBM is not saying it can mass produce graphene ICs, this announcement makes that implication plausible. IBM solved some of the trickiest technical problems such as protecting the ultra thin (1 atom thick) layer of graphene during the process of etching with electron beam lithography – a standard industry process but applied to graphene instead of silicon.

Ultimately what may be important is that IBM is apparently gaining confidence in graphene as a potential or even probable replacement material for silicon. So far, the FET (Field Effect Transistor) made of graphene can be faster and the integrated circuit it makes can be more flexible and heat resistant than silicon. With the predicted upper limit of speed for silicon approaching within the decade, graphene is a powerful candidate for a replacement. The stakes are huge, nothing short of a new generation of electronic components. If IBM is the first to make graphene transistor devices in mass production; that is almost certainly where the industry will go.