MIT's New Silicon Brain Mimic -- One Synapse at a Time

With about 400 transistors, the silicon chip can simulate the activity of a single brain synapse — a connection between two neurons that allows information to flow from one to the other. The researchers anticipate this chip will help neuroscientists learn much more about how the brain works, and could also be used in neural prosthetic devices such as artificial retinas, says Chi-Sang Poon, a principal research scientist in the Harvard-MIT Division of Health Sciences and Technology.

Poon is the senior author of a paper describing the chip in the Proceedings of the National Academy of Sciences the week of Nov. 14. Guy Rachmuth, a former postdoc in Poon’s lab, is lead author of the paper. Other authors are Mark Bear, the Picower Professor of Neuroscience at MIT, and Harel Shouval of the University of Texas Medical School. _MIT

MIT
This is an important -- but very small -- step toward the understanding of how individual neurons and synapses work. And since there are 100 billion neurons in the brain, each with roughly 10,000 synapses, there is still a long way to go before one can claim to "mimic" the brain. In fact, with its emphasis on the study of ion channels, the researchers are taking this chip deeper, toward the molecular level, rather than upward to the global level of brain function.

The MIT researchers designed their computer chip so that the transistors could mimic the activity of different ion channels. While most chips operate in a binary, on/off mode, current flows through the transistors on the new brain chip in analog, not digital, fashion. A gradient of electrical potential drives current to flow through the transistors just as ions flow through ion channels in a cell.

“We can tweak the parameters of the circuit to match specific ion channels,” Poon says. “We now have a way to capture each and every ionic process that’s going on in a neuron.”

Previously, researchers had built circuits that could simulate the firing of an action potential, but not all of the circumstances that produce the potentials. “If you really want to mimic brain function realistically, you have to do more than just spiking. You have to capture the intracellular processes that are ion channel-based,” Poon says.

The new chip represents a “significant advance in the efforts to incorporate what we know about the biology of neurons and synaptic plasticity onto CMOS [complementary metal-oxide-semiconductor] chips,” says Dean Buonomano, a professor of neurobiology at the University of California at Los Angeles, adding that “the level of biological realism is impressive.

The MIT researchers plan to use their chip to build systems to model specific neural functions, such as the visual processing system. Such systems could be much faster than digital computers. Even on high-capacity computer systems, it takes hours or days to simulate a simple brain circuit. With the analog chip system, the simulation is even faster than the biological system itself.

Another potential application is building chips that can interface with biological systems. This could be useful in enabling communication between neural prosthetic devices such as artificial retinas and the brain. Further down the road, these chips could also become building blocks for artificial intelligence devices, Poon says.

...When the researchers included on their chip transistors that model endo-cannabinoid receptors, they were able to accurately simulate both LTD and LTP. Although previous experiments supported this theory, until now, “nobody had put all this together and demonstrated computationally that indeed this works, and this is how it works,” Poon says. _MIT

Barely born, and researchers are already getting it high on endocannabinoids! But seriously, the model is not the synapse, and certainly not the brain. Being able to "accurately simulate" a phenomenon is not the same thing as revealing exactly what is going on. Models have the potential to give you better ideas, but if you are not careful they can badly mislead you.

This line of research is good news for neuro-molecular biologists and for neuroscientists who study low level activities of neurons and synapses. It is certainly important research.

Most Al Fin cognitivists, on the other hand, are eager to learn more about how the human brain works, and how we could make it more functional -- better able to achieve its goals. Achieving such a feat would obviously require an entirely different approach to the research than Chi-Sang Poon et al have taken.

We Are All Cyborgs Now: Soft-Material Memristor Brain Augmentation

One of the most-discussed memristor characteristic is its synaptic biomimesis. “State-of-the-art computers have difficulty mimicking the operation of the brain,” [NCSU Professor] Dickey notes. “Memristors, on the other hand, are effective at mimicking synapses. If you were interested in only mimicking brain function, then solid-state memristors would be more practical because they contain many more memory elements and are much more optimized at this point. One of the things distinguishing our work is that the device behaves like a memristor and has other properties similar to the brain. Conventional electronics tend to be rigid, 2-D, moisture-intolerant, and operate using electrons; the brain, in contrast, is soft, 3-D, wet, and operates using ions and in addition to adopting many of these properties, our device is composed of biocompatible hydrogels.” _Physorg

Human brains are marvelous biological machines, but they could be a lot better. It will prove easier to augment the human brain technologically than to replace it altogether with a cognitive machine. The invention of a soft-material, biocompatible computing architecture would allow the implantation of computing devices into the human body. North Carolina State University scientists and engineers have begun to invent what they hope can be such a material -- the soft-material memristor.

Prof. Orin Velev, Prof. Michael Dickey, and graduate students Hyung-Jun Koo and Ju-Hee So, have devised a new class of easily fabricated memristors based entirely on so-called soft matter – hydrogels doped with polyelectrolytes sandwiched with liquid metal electrodes – that operate using ionic conductance in aqueous systems rather than conventional electron transport.

...In essence, this suggests that in addition to having the potential to realize memristor-based neuromorphic structures, the polysaccharide hydrogel core of these devices is biocompatible, could possibly be interfaced with live neural and other tissue, and could lead to three-dimensional soft circuits and their in vivo operations.

...Going forward, Dickey continues, “We hope to take advantage of the fact the water-based gels in the device are biocompatible, and could in principle be integrated with biological species, such as cells, enzymes, proteins, and tissues. We also made no attempt to optimize the memory capacity in our prototypes, which is an area for improvement. Finally, we’re working to understand the subtle aspects of the operating mechanism.” _PO

They are still in the very early stages, but the possibility of an implantable soft, biocompatible brain augment is too important to overlook.

Quite a few different interfacing techniques could be used, but the optical approach would seem to be the least intrusive for tissues such as the brain, which are sensitive to electromaqnetic forces. Optical materials have high bandwidth and may be less likely to be bio-rejected than electrically conductive materials. Some people have discussed optical brain control in the context of optogenetics.

Another fascinating type of bio-to-machine interface is the piezoelectric interface being developed at Georgia Tech. The piezoelectric interface can be operated by exquisitely subtle mechanical movements, such as a muscle fibre twitch. In other words, a thought -- even a subconscious though -- could cause a pattern of muscle twitches which would activate a particular machine command or subroutine via the piezoelectric interface.

The human brain was not evolved for the ultra-long lifetime of a next level human. Cell debris accumulates, DNA repair mechanisms begin to fail, immune systems weaken, hormonal support falls off, etc. Scientists are learning a lot about how normal aging leads to memory loss in even the sharpest minded senior citizens. The intricate network of cellular connections in the brain slowly loses definition and resolving power.

Well-designed brain implants could sense this process occurring and engineer work-arounds to compensate for the changes. Long term solutions would require a rejuvenation treatment to restore -- or improve -- the resolving power of brain networks, but sometimes work-arounds are the best one can do at the time.

Where would you place your soft bio-compatible brain implant? There isn't a lot of room inside the skull itself, but implants could be placed under the scalp in a relatively unobtrusive manner as long as they were not too large. Alternatively, some women might choose to place their augments in the breast area, and some men might choose augments shaped to serve as muscle implants. If the connections to the interface are via optical fibre, the distance from anywhere on the human body to the brain is negligible, in terms of the speed of light. The interface itself would need to be placed close to the brain.

Depending upon its sophistication, an implanted brain augment could come to know how an individual's brain works quite well, over a period of time. Such augments could even learn how to simulate their hosts in a rudimentary way. The possibilities arising from such pseudo-emulation are worth considering, but perhaps not here and now. (See Old Man's War by John Scalzi)

It is important to stress that these NCSU memristors are not at all close to anything that could be used as a brain augment. But it seems to be the goal of the researchers there to develop biocompatible sensors and intelligent interfaces using these materials. It is not a long stretch from there to an implantable computer augmentation for the brain.

Although memristors are often referred to as neuromimetic or synaptomimetic, in the aggregate, memristor computing devices will function nothing like the brain. But they will not need to. They will only need to function like competent and clever computers that provide reliable memory and I/O capability for mental computations, speculations, and interfacing with the outside world -- including the ability to control machines mentally and to communicate remotely with machines and other individuals who have similar augments.

Human Brain Project Moves Toward Human Cortex Model

Spiegel
Henry Markram's Human Brain Project in Lausanne, is competing for funding from the FET Flagship Initiative, to the tune of 1 billion Euros, disbursed over a ten year period. Markram's goals are extremely ambitious, and unprecedented. He aims to model the human cerebral cortex to an exquisite degree of precision. Markram expects that his model of the human brain will be so exact, that he will be able to study inaccessible brain diseases and devise impossible brain cures by using his model. He may be right. But in only ten years?

Scientists are paying particular attention to the cerebral cortex. This layer on the outside of brain, only a few millimeters thick, is the most important condition of it evolution. It is the starting point for efforts to understand what makes us tick -- and for endeavors to find solutions when things go wrong. Our brain builds its version of the universe in the cerebral cortex. The vast majority of what we see doesn't enter the brain through the eye. It is instead is based on the impressions, experiences and decisions in our brain.

Markham already completed important preparatory work for the computer modeling of the brain with his Blue Brain Project, an attempt to understand and model the molecular makeup of the mammalian brain. He modeled a tiny part of a rat brain, a so-called neocortical column, at the cell level. To understand what one of these columns does, it's helpful to imagine the cerebral cortex as a giant piano. There are millions of neocortical columns on the surface, and each of them produces a tone, in a manner of speaking. When they are simulated, the columns produce a symphony together. Understanding the design of these neocortical columns is a holy grail of sorts for neuroscientists.

It is important to understand the rules of communication among the nerve cells. The individual cells do not communicate at random, but instead seek specifically targeted communication partners. The axes of nerve cells intersect at millions of different points, where they can form a synapse. This makes communication between individual neurons possible. In a recent article in the journal Proceedings of the National Academy of Sciences, Markram writes that such connections are also developed entirely without external influence. This could indicate a sort of innate knowledge that all people have in common. Markram refers to it as the "Lego blocks" of the brain, noting that each person assembles his own world on the basis of this innate knowledge. _Spiegel

The object of study for the Human Brain Project may be the most complex dynamic system in the universe. The attempt would be impossible without the most sophisticated computing hardware and software available. And one must have more than a mere fistful of Euros to acquire such advanced goodies.

Modeling all of this in a computer is extremely complex. Markram's current model encompasses tens of thousands of neurons. But this isn't nearly enough to come within striking range of the secret of our brain. To do that, scientists will have to assemble countless other partial models, which are to be combined to create a functioning total simulation by 2023.

The supercomputers at the Jülich Research Center near Cologne are expected to play an important role in this process. The brain simulation will require an enormous volume of data, or what scientist Markram calls a "tsunami of data." One of the challenges for scientists working under Thomas Lippert, head of the Jülich Supercomputing Centre, is to figure out how to make the computer process only a certain part of the data at a given time, but without completely losing sight of the rest. They also have to develop an imaging method, such as large, three-dimensional holograms, to depict the massive amounts of data.

All it takes is a look at the work of Jülich neuroscientist Katrin Amunts to understand the sheer volume of information at hand. The team she heads is compiling a detailed atlas of the human brain. To do so, they cut a brain into 8,000 slices and digitized them with a high-performance scanner. The brain model generated in this way consists of cuboids, each measuring 10 by 10 by 20 micrometers, and the size of the data set is three terabytes. Brain atlases with higher resolutions, says Amunts, would probably consist of more than 700 terabytes _Spiegel

The answer to the question posed above is: No, this goal cannot be met within a time frame of ten years. Because the challenge is not merely quantitative -- a matter of compiling the precise assembly of terabytes to create a brain atlas. The goal is to create a dynamic, interactive model of incredible plasticity -- a model which changes itself moment to moment. The "700 terabyte" requirement mentioned above is just the starting point -- the bare beginning -- in the assembly of such a dynamic and ever-changing model.

But the problem is even harder -- much, much harder. The quantitative complexity -- even in dynamic flow -- is nothing when compared to the qualitative complexity, which is nowhere near to being solved by Markram's team.

The project as described in brief above is an excellent starting point. Much can be learned from such an approach. But starting points do not necessarily point directly toward the end that one seeks. Rather, they point somewhere "out there." It is for the questers to continuously adjust their headings -- and often they are forced to adjust their goals.

Good luck to Henry and his team -- with the funding and with the ongoing project. It is an ambitious goal worthy of any scientist.

Double Plus Overhype Ado About Artificial Synapse?

There's a news story replicating on the web right now about a "Functioning Synapse Created Using Carbon Nanotubes," for instance here and here.

....the circuit has not actually been constructed, so the "apparatus" photo there is kind of silly. It just gives the false impression that a synapse model was actually built physically with analog components.

...[ed: all that we have is] an electrical circuit schematic that in turn depends on certain SPICE models of carbon nanotube FETs (which have apparently been available since 2006). So in other words, this circuit is a particular model of a synapse being simulated with a simple circuit. _Science20

Samuel Kenyon points out at the Science20 article linked above, that the "artificial synapse" is only a simulated circuit using the SPICE electronic simulation program. But the overhype is doubly overdone, because even if the researchers had actually built a real, physical circuit that functioned as an "artificial synapse", it would still not put them any closer to actually building an "artificial brain."

PDF Source (via Science20) This overhyped excitement is reminiscent of IBM scientist Dharmendra Modha's claim that he had built a computer that was the equivalent of "a cat's brain." Initially, most science blogs (except Al Fin) accepted Modha's claim at face value. Then when Henry Markram came out publicly to refute the claim, Modha backed off and clarified, and almost everyone agreed in the end, that it was all pretty much ado about nothing.

It is the same thing here, where science and tech blogs initially rush to accept exaggerated claims in press releases. Then, little by little, sceptics step up and insist upon clarifications and qualifications of claims, until the claims are downgraded to the point that eventually no one can remember what the fuss was about.

In the case of "the artificial synapse", it is important to understand why this real world device is not even a synapse, much less a possible ingredient for an artificial brain.

The USC and Stanford researchers have designed a computer model of "an artificial synapse," not an actual artificial synapse. But even if it were a real synapse, would engineers be able to use it to assemble an artificial brain? No. And the reason why one thing does not naturally lead to the next is crucial to an understanding of how real brains work -- real brains being the only working proof of concept of intelligence in the known universe.

Artificial intelligence enthusiasts will rush to say that working brains do not necessarily have to work just like the bio-brains we know now. But then, what is the point of emulating a tiny component of a bio-brain in the first place, if you cannot use it to build a functioning brain, as we understand it? In other words, if your objective is to build a new class of brain, why start with a poor imitation of a low level component of a bio-brain? Why not start with something "better" from the get-go? [By "better", I mean faster, more versatile, etc etc]

Here is the reason: Because artificial intelligence researchers do not have a clue as to how to build an intelligent brain. And so they are practising a subtle form of cargo cult science.

It's okay. We all understand that rents and utilities must be paid, the price of gasoline is high, everything costs money. Academics must publish or perish, and getting research grants to build "artificial synapses" does sound kind of sexy. Anything to keep the lights on, right?

But all the same, it is important to understand that brains are not the plural of "synapse." It is time to stop pretending that one has made progress toward AI, when nothing of the sort has happened.

More: It is important to understand that the bulk of the exaggeration comes from press releases and media coverage. Here is the actual conclusion from the research study referred to:

A carbon nanotube synapse typical of cortical synapses has been designed and simulated using SPICE. While the simulations were successful, the design of a single typical synapse is only a small step along the path to a synthetic cortex. The variations in synapses, including inhibitory synapses, will be the focus of future research. Predicting the interconnection capabilities of nanotube circuits is also important in understanding the future prospects for a synthetic cortex. _PDFeve.usc.eduPDF

Unfortunately, as computer modelers try to more realistically model the events in the brain at cellular and molecular levels, computing power and computing time demands explode out of control very rapidly. More, the researchers above do not seem to understand the key facts of brain function upon which conscious intelligence is balanced: time-dependent high level cross-brain synchronisation (via evolved white matter pathways) of evolved multiple modular (grey matter) brain centers from brain stem to neocortex, dancing alongside sensory input, jostled by memory, under the changing lights of emotion, and swept up in hormonal tides and chaotic flows of molecules...

More complex than one imagines? More complex than one can possibly imagine. The job is simply too hard for intelligent design. Only evolution will do. We need to get better at intelligently designing evolution. ;-)

Micro-Electronic Brain Implant Supervises Brain Re-Wiring

When the human brain is damaged from trauma, stroke, infection, or tumour etc., the damaged tissue does not re-grow itself spontaneously. Instead, the person must learn to compensate for the loss of function. Some brain plasticity may occur, as undamaged parts of the brain take responsibility for some of the functions which the destroyed parts previously carried out. But damaged brain does not heal.

Researchers at Case Western Reserve University intend to change that, by using implanted electronics devices which can help teach the brain how to re-wire itself to allow disconnected parts of the brain to become connected -- and functional -- again.

Pedram Mohseni, a professor of electrical engineering and computer science at Case Western Reserve University, and Randolph J. Nudo, a professor of molecular and integrative physiology at Kansas University Medical Center, believe repeated communications between distant neurons in the weeks after injury may spark long-reaching axons to form and connect.

Their work is inspired by the traumatic brain injuries suffered by ground troops in Afghanistan and Iraq.

...Mohseni has been building a multichannel microelectronic device to bypass the gap left by injury. The device, which he calls a brain-machine-brain interface, includes a microchip on a circuit board smaller than a quarter. The microchip amplifies signals, called neural action potentials, produced by the neurons in one part of the brain and uses an algorithm to separate these signals – brain spike activity - from noise and other artifacts. Upon spike discrimination, the microchip sends a current pulse to stimulate neurons in another part of the brain, artificially connecting the two brain regions.

...During the next four years, they expect to understand the ability to rewire the brain in a rat model and to determine whether the technology is safe enough to test in non-human primates. If tests show the treatment is successful in helping recovery from traumatic brain injury, the researchers foresee the possibility of using the approach in patients 10 years from now. _Eurekalert

Here is an abstract of a paper published by Mohseni in an IEEE publication from 2008:

This paper reports on the design, implementation, and performance characterization of a high-output-impedance current microstimulator fabricated using the TSMC 0.35 mum 2P/4M n-well CMOS process as part of a fully integrated neural implant for reshaping long-range intracortical connectivity patterns in an injured brain. It can deliver a maximum current of 94.5 muA to the target cortical tissue with current efficiency of 86% and voltage compliance of 4.7 V with a 5-V power supply. The stimulus current can be programmed via a 6-bit DAC with an accuracy better than 0.47 LSB. Stimulator functionality is also verified with in vitro experiments in saline using a silicon microelectrode with iridium oxide (IrO) stimulation sites. _IEEEXplore

The technology for such interventions is in the early stages. The researchers are also working on devices which can be used for a broad range of neurological and psychiatric conditions, and in conjunction with neurosurgery and standard post-surgical rehabilitation.

Eventually such devices will probably be implanted into a damaged area of brain, along with an artificial matrix seeded with a person's own stem cells and growth factors. The devices will be wired to "bridge" from healthy brain on one side of the lesion to healthy brain on other sides of the lesion (corresponding to interrupted pathways). The electronic signals will not only help guide a re-wiring of the brain, but they should also guide the re-growth of new replacement brain tissue of specific replacement types.

Anyone who has read the science fiction novel "Old Man's War" by John Scalzi, should recognise some of the intent behind the early stage, rudimentary devices being developed at Case Western -- and to see where the technology may be heading.

More on a related topic from Brian Wang

We Are All Cyborgs Now:

The idea that a chip can interface between inputs and outputs of certain brain area is a very new concept in scientific circles, Prof. Mintz notes, although movies and TV shows about bionic humans have been part of the popular culture for decades. _ReNaChip

The chip sits just below the skin, on top of the skull. It senses brain activity via implanted electrodes, and it knows when to fire stimulatory pulses to the precise parts of the brain where they are needed -- to restore the desired brain function.

For now, the chip, called the Rehabilitation Nano Chip (or ReNaChip), is hooked up to tiny electrodes which are implanted in the brain. But as chips become smaller, the ReNaChip could be made small enough to be "etched" right onto the electrodes themselves.

For therapeutic purposes, though, only the electrodes will be inserted into the brain. "The chip itself can be implanted just under the skin, like pacemakers for the heart," says Prof. Mintz, who is currently conducting experiments on animal models, "ensuring that the brain is stimulated only when it needs to be."

One of the challenges of the proposed technology is the size of the electrodes. The researchers hope to further miniaturize deep brain electrodes while adding more sensors at the same time says Prof. Mintz. His Tel Aviv University colleague and partner Prof. Yossi Shaham-Diamond is working on this problem.

The international multidisciplinary team, includes other researchers from TAU — Prof. Hagit Messer-Yaron and Dr. Mira Kalish — and partners from Austria, England and Spain, regularly converge on the TAU campus to update and integrate new components of the set-up and monitor the progress of the chip in live animals in Prof. Mintz's lab. _Source

More here, here, here, and here.

The ReNa chip would function as a type of "nano-controller", riding herd over specific brain centers -- depending upon the person's needs. Initially, the chips will be used for brain rehabilitation and to modulate the effects of various neuropathologies. Eventually, the chips will be used to treat behavioural problems. Climate change deniers and the like. You will be assimilated. Resistance is futile.

Brains Like Ours?

Terrence Sejnowski is a Princeton trained physicist who found his way into neurobiology via a Harvard postdoc.  He is currently the head of the Computational Neurobiology Laboratory at the Salk Institute for Biological Studies in La Jolla.  Sejnowski suggests that humans may begin to create "brains like ours" sooner than most people think.

Last November, IBM researcher Dharmendra Modha announced at a supercomputing conference that his team had written a program that simulated a cat brain. This news took many by surprise, since he had leapfrogged over the mouse brain and beaten other groups to this milestone. For this work, Modha won the prestigious ACM Gordon Bell prize, which is awarded to recognize outstanding achievement in high-performance computing applications.

However, his audacious claim was challenged by Henry Markram, a neuroscientist at the Ecole Polytechnique Fédérale de Lausanne and the leader of the Blue Brain project, who announced in 2009 that: "It is not impossible to build a human brain and we can do it in 10 years.". In an open letter to IBM Chief Technical Officer Bernard Meyerson, Markram accused Modha of “mass deception” and called his paper a “hoax” and a “scam.”

...Unfortunately, the large-scale simulations from both groups at present resemble sleep rhythms or epilepsy far more closely than they resemble cat behavior, since neither has sensory inputs or motor outputs. They are also missing essential subcortical structures, – such as the cerebellum that organizes movements, the amygdala that creates emotional states and the spinal cord that runs the musculature. Nonetheless, from Modha’s model we are learning how to program large-scale parallel architectures to perform simulations that scale up to the large numbers of neurons and synapses in real brains. From Markram’s models, we are learning how to integrate many levels of detail into these models. In his paper, Modha predicts that the largest supercomputer will be able to simulate the basic elements of a human brain in real time by 2019, so apparently he and Markram agree on this date; however, at best these simulations will resemble a baby brain, or perhaps a psychotic one.... _SciAm

And from there, Sejnowski unfortunately veers off to briefly discuss "intelligent communications systems", then quickly ends his article. In other words, Sejnowski doesn't actually tell us anything about when we might expect to create "brains like ours."

Ray Kurzweil predicts human level computing by 2029, but Mitch Kapor is betting that Ray is wrong. Henry Markram's prediction for an artificial human brain by 2019 goes far beyond what the Blue Brain website is willing to predict, or what some of his more sober colleagues at Lausanne are willing to claim. Ben Goertzel and Peter Voss each believe they are on the trail of artificial general intelligence (AGI), and see no reason why they cannot achieve their goal.

Noah Goodman -- a researcher at MIT's Cognitive Science Group -- is quite forthcoming and honest in this interview at Brian Wang's NextBigFuture. Goodman says that "we could achieve human-level AI within 30 or 40 years", but he also admits that it could take longer.

A startling new approach to massively parallel computing comes from Michigan Technological University working with a research team in Japan.

In their work, instead of wiring single molecules/CA cells one-by-one, the researchers directly build a molecular switch assembly where ∼300 molecules continuously exchange information among themselves to generate the solution. This molecular assembly functions similarly to the graph paper of von Neumann, where excess electrons move like colored dots on the surface, driven by the variation of free energy that leads to emergent computing...

...By separating a monolayer from the metal ground with an additional monolayer, the NIMS/MTU team developed a generalized approach to make the assembly sensitive to the encoded problem. The assembly adapts itself automatically for a new problem and redefines the CA rules in a unique way to generate the corresponding solution.

"You could say that we have realized organic monolayers with an IQ" says Bandyopadhyay. "Our monolayer has intelligence."

Furthermore, he points out that this molecular processor heals itself if there is any defect. It achieves this remarkable self-healing property from the self-organizing ability of the molecular monolayer.

"No existing man-made computer has this property, but our brain does: if a neuron dies, another neuron takes over its function" he says.

With such remarkable processors that can replicate natural phenomena at the atomic scale researchers will be able to solve problems that are beyond the power of current computers. Especially ill-defined problems, like the prediction of natural calamities, prediction of diseases, and Artificial Intelligence, will benefit from the huge instantaneous parallelism of these molecular templates.

According to Bandyopadhyay, robots will become much more intelligent and creative than today if his team's molecular computing paradigm is adopted. _Nanowerk

Most intelligent observers of the AI field who have been able to take a step back and view the phenomenon from the perspective of many decades of history, are forced to conclude that a new physical substrate -- other than von Neumann architecture supercomputers -- will be necessary before anything close to a human-level AGI can be built.

Whether using memristors, qubits, molecular monolayers, fuzzy logic - enabled neural nets with genetic algorithmic ability, or physical substrates and architectures not yet envisioned or announced, AGI researchers of the near to intermediate future will eventually make rapid strides toward useful machine intelligence -- once the right architectural substrate is discovered.

In the meantime, cognitive scientists are learning a great deal about how the brain works, and how artificial mechanisms may better emulate brain function. I suspect that both Modha and Markram (along with several other prognosticators including Kurzweil) may have allowed wishful thinking to get the better of them, when making timeline predictions.

In the dramatic history of genetic science, only after the breakthrough of Watson and Crick could molecular biology explode into the present and future. The ongoing history of artificial intelligence is still lacking its "Watson and Crick." Progress can be made, but not the explosive progress that is necessary to approach human level intelligence.

Memristor Update and Video

The discovery of the memristor derives from the search for a rigorous mathematical foundation for electronics by a young electronics engineer at the University of California, Berkeley, Leon O. Chua. Chua’s analysis suggested there was a fourth foundational circuit element missing from the standard trio of resistor, capacitor and inductor. He called it “memristor.” In 1971, he published a seminal paper on this missing basic circuit element.

...Jumping forward to 2010, the work of Dr. Wei Lu’s University of Michigan team now confirms that memristor circuits indeed behave like synapses. Lu’s team used a mixture of silicon and silver to join two metal electrodes, mimicking how synapses allow neurons to learn new firing patterns — not unlike a slime mold’s ability to anticipate events. The timing of electrical signals in two neurons anticipates how later messages can jump across the synapse between them. When a pair fires, the given synapse becomes more likely to pass later messages between the two. “Cells that fire together, wire together,” says Lu.

Just like a synapse, the memristor changes its resistance in varying levels. Dr. Lu found that memristors can simulate synapses because electrical synaptic connections between two neurons can seemingly strengthen or weaken depending on when the neurons fire. “The memristor mimics synaptic action,” Lu concludes. Dr. Nadine Gergel-Hackett at NIST acknowledges the Michigan team’s successful creation of a brain synapse analog. “This work is a large step towards the realization of biology-inspired computing,” she says. _hplus

This electronic approach to an artificial synapse may help in the quest to build a bottom-up artificial brain. The neo-cortex contains about 10 billion neurons, with each neuron making approximately 10,000 connections (or more). The synaptic approach to cognition is only one of several approaches that neuroscientists and cognitive scientists have taken. But it is a fairly good place to start.

Neurons are also connected to each other via "gap junctions" -- direct ion exchange pathways between neurons. Other cells are also involved intimately with neuronal function -- the glial cells. No one actually understands how the brain generates cognitive activity, or consciousness.

But if scientists can create realistic artificial brains that can be tested and monitored in exquisite detail at every stage of input, output, and anything in between, it is quite likely that we will learn something.

How Can Machine Intelligence Copy the Brain?

Intelligent machines that not only think for themselves but also actively learn are the vision of researchers of the Institute for Theoretical Science (IGI) at Graz University of Technology...They have been co-ordinating the European Union research project "Brain-i-Nets" (Novel Brain Inspired Learning Paradigms for Large-Scale Neuronal Networks) for three years... _SD

The Brain-i-Nets project at Graz University of Technology is quite different from Henry Markram's Blue Brain Project in Lausanne.  While Blue Brain aims to create a silicon substitute for the brain for neuroscience and neuropathology research, the Graz project aims to create actual machine intelligences by learning from a detailed and intense study of intact brains. Brain-i-Nets is attempting to abstract the significant aspects of dynamic cortical network plasticity, by using advanced real-time imaging of intact brain networks as they are working and changing.

The scientists want to design a new generation of neuro-computers based on the principles of calculation and learning mechanisms found in the brain, and at the same time gain new knowledge about the brain's learning mechanisms.

The human brain consists of a network of several billion nerve cells. These are joined together by independent connections called synapses. Synapses are changing all the time -- something scientists name synaptic plasticity. This highly complex system represents a basis for independent thinking and learning. But even today there are still many open questions for researchers.

"In contrast to today's computers, the brain doesn't carry out a set programme but rather is always adapting functions and reprogramming them anew. Many of these effects have not been explained," comments IGI head Wolfgang Maass together with project co-ordinator Robert Legenstein. In co-operation with neuroscientists and physicists, and with the help of new experimental methods, they want to research the mechanisms of synaptic plasticity in the organism.

...The three-year project is financed by the EU funding framework "Future Emerging Technologies" (FET), which supports especially innovative and visionary approaches in information technology. International experts chose only nine out of the 176 applications, among which was "Brain-i-Nets."
_SD

Brain-i-Nets Website

The overall long-term vision of this project is

to develop new design principles for adaptive, reconfigurable very-large-scale hardware systems implementing novel learning rules inspired by biological neural networks in vivo.

Learning mechanisms implemented in the brain appear to be much more robust and flexible than those currently used in neurally inspired computing systems. To confer the superior adaptive and computational capabilities of biological neural systems to large-scale recurrent neural hardware systems and other novel massively parallel computing devices, new and more sophisticated learning rules are needed.

Our long-term vision is that the learning rules for global gating of local learning, identified and explored in this project, will become ideal candidates for implementation in hardware. Conceptually the interaction of local factors that can be monitored and stored at the site of each connection with one or a few global factors is very attractive for hardware implementation. Previous collaborations of several partners of the project have shown that networks of spiking neurons can be implemented in a truly large-scale, parallel, mixed analog-digital hardware system. The inclusion of learning rules that go beyond the classical Hebbian or STDP rules for unsupervised learning, by including a third factor representing for example information on saliency or reward, will advance the hardware into a regime where a much broader class of learning tasks can be solved by these ultra-rapid machines. __Brain-i-Nets

Besides the Graz University of Technology, scientists from University College London, the University of Heidelberg, the University of Zurich, Ecole Polytechnique Federale de Lausanne, and the Centre National de la Recherche Scientifique of France are also partnering in the project.

Al Fin neuroscientists consider this project to be one of the most sophisticated approaches to the biomimetic creation of machine intelligence to this date. For such a project to be truly successful, it is likely to require the expertise of scientists, technologists, and engineers from North America and East Asia, as well as those at the European centres mentioned above.

Blue Brain Tour in Lausanne: What Is Happening?

Bluebrain | Year One from Couple 3 Films on Vimeo.

Henry Markram brings us up to date on the aims of Blue Brain.  H/T MachinesLikeUs

The Swiss Blue Brain Project is meant to provide a "machine model" of the brain, for purposes of neuroscientific study.  It does not mean to build a machine intelligence.  Instead, Blue Brain is a massive simulation of brain function down to the level of ion channels within individual neurons.

Markram expects the project to provide a means for solving the diseases of the brain -- including Alzheimer's, Parkinson's, Huntington's, and others -- at a cellular and molecular level.

Clearly, Blue Brain is a massively ambitious and expensive project.  How soon will it begin to pay for itself by solving the critical problems of brain degeneration and other deficits of brain function?  It is impossible to say.

The video segment above is the "year one" report out of a "ten year project" to document the Blue Brain project on film.

The Brain Is Far More Complex Than Believed

We have been bombarded with predictions that human-level artificial intelligence will be developed "within ten years." These predictions inevitably come from persons with a computer science, engineering, or artificial intelligence background. Such  prognosticators understand algorithms and / or electrical circuits, but do they understand how consciousness is created? Do they comprehend the basis for the only human-level intelligence that exists: the human brain? Clearly not.

This respected Harvard team of neuroscientists has its hands full studying a nematode nerve network of 4 measly neurons! They are hoping to expand their study to include more than 4 neurons soon.

Meanwhile, there is the problem of "The Other Brain", the glial network.

Glia communicate by broadcasting chemical messages. Moreover, glia can sense information flowing through neural circuits and alter the communications between neurons at synapses! Glia, we now know, have receptors for detecting the flow of ions generated by neurons firing electrical impulses and for sensing the neurotransmitters neurons release at synapses. Glia intercept these signals and act upon them to increase or decrease the transmission of information across synapses and speed or slow the transmission of electrical information through axons.


These recent discoveries open an entirely new dimension into brain function. Glia are involved in all aspects of nervous system health and disease. They can control neuronal communication, development of the fetal brain, generation of new neurons in the adult brain, participate in epilepsy, Alzheimer's disease, mental illnesses such as depression and schizophrenia, and they provide a new mechanism of learning that operates beyond synapses. _RDouglasFields

Henry Markram's Blue Brain project is the one supercomputer project that seems to be taking into account much (but not all) of the brain's complexity.   But Markram's project is not trying to create human-level AI.  It is trying to create a simulated brain that can be used to better understand brain function and diseases of the brain.   Markram is a neuroscientist, so his goals are focused more on the realities of the brain than on the goal of "human-level AI."

The best of the "brain simulations" by AI workers is the simulation by Dharmendra Modha's IBM team. It barely simulated one of the most basic functions of the visual cortex, at average speeds less than 1 / 100th that of a mammalian brain. It required over $1 million a year to power its processors, and much more to cool the supercomputer. For something with the same number of neurons as a cat brain, Modha's simulation was a hugely inefficient use of energy -- compared to a cat.

There are some interesting new hardware tools coming along, such as Chua's memristors, meminductors, and memcapacitors. Alice Parker's BioRC project at USC may hold some future promise.

The point is not that a human-level AI would have to emulate the human brain in great detail. That would be a rather pointless extravagance, when crafty abstraction can save space, time, energy, and effort. But new hardware and software tools are needed, obviously. One cannot build a slow-functioning silicon retard occupying a skyscraper-sized building requiring a nuclear reactor to power and mega-tons of refrigeration to cool, and call it a human-level brain.

Anyone wanting to build a human-level machine intelligence will need to understand much of what consciousness entails, and how the human brain achieves it.

It is possible that an insect-level machine brain of appropriate size and energy-consumption might be developed within 5 to 10 years. A rodent-level machine brain of appropriate dimensions might be developed within 10 to 20 years, if we are lucky. A human-level brain may not take more than 5 years to achieve after the rodent-level brain, but shrinking it to the size and energy-efficiency of a human brain may take longer.

Humans are stupid. But they are also the most powerful general-purpose intelligence known to us. Machine intelligence of human-level or better would generate significant changes to human societies. Modern conventional human governments do not want to lose control of this particular revolution. If they do, it might be the last mistake they ever make.

Abstracting the Brain: A Simpler Emulation?

Researchers at Washington University in St. Louis have taken a three-neuron feedback microcircuit model and abstracted the microcircuit to a single "neuron" that behaved in an identical way to the more complex microcircuit. In other words, without losing any complexity of behaviour, they have simplified their model. This is an important concept for cognitive scientists who wish to simulate brain behaviour with hardware. Being able to conceptualise simplified models exhibiting an equal behavioural richness, will allow scientists to design elegant new hardware that should be both faster and use far less energy to operate.

Looking for a way to explain to the students in his Physics of the Brain class how delayed feedback produces complexity in a circuit, Ralf Wessel, Ph.D., associate professor of physics in Arts & Sciences, came up with a toy neural circuit simple enough to be stepped through time iterations at the blackboard. He used it to show his students that if there was feedback among the neurons, simple constant inputs could produce a long-period oscillation in their outputs.

Wessel then asked Matthew S. Caudill, Ph.D., graduate research assistant in physics, to create a computer model of the circuit so that it could be explored more thoroughly. As they worked with three-neuron microcircuit they realized it was very like one students in Wessel's neurophysiology lab were studying.

That circuit, which consists of three neurons and their feedback projections, has a simple task: to detect motion in the chicken's field of view. One neuron in the area called the optic tectum because it sits on the "roof" of the brain, sends axons to others in a knob of tissue called the nucleus isthmi. The neurons in the isthmi send projections back to the optic tectum, either directly to the neuron from which they got their input or back to the rest of the tectum (the crucial feedback loops).

There are similar microcircuits in the optical processing areas of reptilian and mammalian brains.

The microcircuit's behavior could be captured mathematically by three equations, each of which describes one neuron's output in terms of its inputs and parameters called synaptic weights, the standard way of expressing the strength of the connection between two neurons. Looking at the equations, Wessel and Caudill recognized that they could be reduced by algebraic substitution to one equation with two parameters (derived from the original five synaptic weights).

"It is as if," says Caudill, "the system of three neurons was reduced to one abstract neuron that does the same thing, follows the same rule, as the more complicated circuit. " _Physorg

This is a long way from a cortical lobe -- or even a cortical column -- but it is a conceptual foundation that may allow for further abstraction.

Most people who understand both the brain and computers, will understand that new hardware and software is absolutely necessary to better approach the brain's level of complex (but remarkably stable) behaviours. But understanding how to build the architecture for the new hardware has been slow in coming.

It is the opinion of Al Fin cognitive scientists that much of the brain's complexity is a red herring, a distraction from what the brain is actually doing. We will have to approach this "essence of braininess" from both the top-down, and the bottom-up.

Henry Markram's "Blue Brain" approach includes all the complexity of neuronal processes and ion channels because it is meant to simulate brain function well enough to demonstrate actual brain pathology -- such as Parkisonism or Alzheimer's. Most cognitive scientists just want to design a machine that can intentionally initiate learning, thinking, and acting much like a human does.

Brain Reverse Engineering

... to fully realize the brain’s potential to teach us how to make machines learn and think, further advances are needed in the technology for understanding the brain in the first place. Modern noninvasive methods for simultaneously measuring the activity of many brain cells have provided a major boost in that direction, but details of the brain’s secret communication code remain to be deciphered. Nerve cells communicate by firing electrical pulses that release small molecules called neurotransmitters, chemical messengers that hop from one nerve cell to a neighbor, inducing the neighbor to fire a signal of its own (or, in some cases, inhibiting the neighbor from sending signals). Because each nerve cell receives messages from tens of thousands of others, and circuits of nerve cells link up in complex networks, it is extremely difficult to completely trace the signaling pathways.

Furthermore, the code itself is complex — nerve cells fire at different rates, depending on the sum of incoming messages. Sometimes the signaling is generated in rapid-fire bursts; sometimes it is more leisurely. And much of mental function seems based on the firing of multiple nerve cells around the brain in synchrony. _GrandEngineeringChallenge

Respected Neuroscientist Henry Markram recently objected to claims for cat-scale "brain simulation" by an IBM team led by Dharmendra Modha. Markram stated that the IBM team's claims were a "hoax."

Modha's team nevertheless won the Gordon Bell prize for its efforts, and in computer science circles Modha's achievement was considered a respectable one.

The National Academy of Engineering considers the Reverse Engineering of the Brain to be a worthy project, as does DARPA -- which is helping to fund Modha's team.

It is important to understand the differences between Markram's and Modha's approaches to brain simulation, in order to make sense of the public disagreements. Markram -- an accomplished neuroscientist -- is attempting to simulate the brain down to the level of synapses and ion channels on individual neuronal processes (see video above). Modha is merely simulating "neurons" as points or nodes in an extensive network of nodes. Modha is a computer scientist and his simulation is just what you would expect from a computer scientist.

The two types of simulations are meant to serve completely different purposes. Markram intends for his simulation to teach neuroscientists how the brain works on a dynamic level never before approached by neuroscience. Modha wants to build a simulation that can bring human-level massively parallel computing to complex machine systems. Modha eventually wants to build thinking machines. Markram wants to open a window on real time brain function that will help neuroscientists solve the difficult problems of brain function and pathology.

What Modha is building has very little to do with a rat brain, a cat brain, a human brain, or even an insect brain. But if he and his team want to call their work a "brain simulation", no one but Markram has objected so far. Well, Markram and Al Fin. ;-)

There is room for dozens of approaches to reverse engineering the brain, or more. The only condition should be that researchers not mis-represent the nature of what they are doing.

Micro-Nubbin Neuron-Chip Interface

This "micro-nubbin" nerve interface-chip from IMEC provides convenient "docking stations" for nerve processes to interface. The chip is meant to serve as an experimental "eavesdropper" -- to listen in on communication between neurons. Initially, it will provide a surface for nerves to grow and interface. Scientists hope to learn something from recording how networks of neurons communicate among themselves.

IMEC presents a unique microchip with microscopic nail structures that enable close communication between the electronics and biological cells. The new chip is a mass-producible, easy-to-use tool in electrophysiology research, for example for fundamental research on the functioning and dysfunctioning of the brain. Each micronail structure serves as a close contact-point for one cell, and contains an electrode that can very accurately record and trigger in real-time the electrical activity of an individual electrogenic cell in a network.


...IMEC's new micronail chip is the ideal instrument to study the communication mechanisms between cells. The electrodes in IMEC's micronail chip are downsized to the size of cells and even smaller. They consist of tiny nail structures made of a metal stem covered with an oxide layer, and a conductive (e.g. gold or titaniumnitride) tip. When cells are applied on the chip surface, their cell membrane strongly engulfs the nail structures, thereby realizing an intimate contact with the electrode. This very close contact improves the signal-to-interference ratio enabling precise recording of electrical signals and electrical stimulation of single cells. _SD

This research is fairly mundane, as described. But Al Fin neuroscientists understand where the science is heading, and are quite excited.

The micro-nubbin chips will need further miniaturisation, and will have to be made biocompatible. In the lab, neuronal and glial proto-cells will be cultured, in contact with the micro-nubbins. The chip + precursor cells + selected growth factors will be implanted intra-cranially, and anchored to the underside of the skull. The cultured and anchored cells will send processes into the white matter of the brain via an intricate system of artificial portals -- in essence engineered artificial white matter paths from the interface to merge with established white matter pathways.   These soft tissue penetrations of the cortex would be composed of the individual's own cells, and firmly immersed within soft tissue so as not to cause damage to other structures.

Al Fin neuroscientists envision roughly a dozen of these micro-nubbin brain/machine interfaces at specific areas of the skull -- depending upon the brain systems to be interfaced. Each nubbin-hub interface will allow for roughly a thousand or more neuron-chip interface points. Visual, auditory, olfactory, motor, and memory systems will be targeted -- among others.

Clearly the initial applications will be military. First, to provide prosthetic control of artificial limbs, and to provide optic and auditory input to soldiers, sailors, airmen, and marines who have suffered brain damage. As the operational ability of the chips improves, they will be used to compensate for subtler forms of brain damage and functional impairment in military injuries.

As the chips are perfected they will be used to create nerve-machine interfacing with advanced weapons systems and remote reconnaissance systems. Then the chips will be implanted into elite combat infantry operatives.

Imagine being able to see well beyond the electromagnetic spectrum of visible light. Or to be able to hear well beyond the auditory spectrum of 20 Hz to 20 KHz. Having the ability to distinguish subtle smells better than a bloodhound. Those would be simple beginnings with much more complex capabilities downloaded later -- as improved versions are developed.

Brain-machine interfacing will allow for a wide variety of expanded human senses and function, as well as rich virtual reality and augmented reality settings. Auxiliary memory and calculation systems as well as other advantages of complex interfacing with highly advanced information systems, would also be available.

Before all these things can be done, such micro-nubbin chips will have to become "smart enough" to understand neuronal code.  That is the purpose of the initial lab studies with cultured neuronal nets.

More On IBM's BlueMatter "Brain Simulation"

Popular Mechanics provides information on a recent IBM "brain simulation" of 1.6 billion simulated neurons -- an attempt to "simulate the human visual cortex". Here are some fascinating details from PM:

Modha's billion-neuron virtual cortex is so massive that running it required one of the fastest supercomputers in the world—Dawn, a Blue Gene/P supercomputer at Lawrence Livermore National Laboratory (LLNL) in California.


Dawn hums and breathes inside an acre-size room on the second floor of the lab's Terascale Simulation Facility. Its 147,456 processors and 147,000 gigabytes of memory fill 10 rows of computer racks, woven together by miles of cable. Dawn devours a million watts of electricity through power cords as thick as a bouncer's wrists—racking up an annual power bill of $1 million. The roar of refrigeration fans fills the air: 6675 tons of air-conditioning hardware labor to dissipate Dawn's body heat, blowing 2.7 million cubic feet of chilled air through the room every minute.


Dawn was installed earlier this year by the Department of Energy's National Nuclear Security Administration (NNSA), which conducts massive computer simulations to ensure the readiness of the nation's nuclear weapons arsenal. Modha's team worked with Dawn for a week before it was transitioned to NNSA's classified nuclear work. For all of its legendary computing power, Dawn still ran Modha's 1.6 billion neurons at only one-six-hundredth the speed of a living brain. A second simulation, with 1 billion neurons, ran a little faster—but still only at one-eighty-third of normal brain speed.


These massive simulations are merely steps toward Modha's ultimate goal: simulating the entire human cortex, about 25 billion neurons, at full speed. To do that, he'll need to find 1000 times more computing power. At the rate that supercomputers have expanded over the last 20 years, that super-super computer could exist by 2019. "This is not just possible, it's inevitable," Modha says. "This will happen." _PM

Can you imagine $1 million a year just to power the processors? 6675 "tons" of air conditioning to keep the hardware cool? All to run a very poor simulation of a human visual cortex at 1 / 600 th the speed of a human brain?

You have to admire Modha's optimism when he claims that it is inevitable that the human level artificial cortex will be operating in real time by the year 2019. As I said a couple of days ago, the estimation is preposterous.

First of all, the current simulation is of the most basic cortical neural architecture. It is the easiest, least complex type of neural simulation problem to solve. In fact, an insect brain is more complex -- with far fewer neurons, at much faster speeds, in a far smaller space, with a far lower energy budget -- than the monstrosity that IBM has strung together at Livermore.

It looks as if Modha's team is in a race with Henry Markram's Swiss team, and other teams around the world to simulate the human cortex the soonest. Perhaps there is an X prize for that achievement. But it seems to Al Fin engineers that this brute force approach is best adapted for burning up research funds, rather than actually coming close to simulating a human brain.

Yes, you must crawl before you can walk, walk before you can run.  But it seems as if what the IBM team is doing is moving two fingers in the air in a crawling motion, and calling it crawling.  Time will tell.

As I said before, the achievement of simulating some basic quasi-physiologic responses to simulated input stimuli -- on this scale -- is remarkable.  This platform will provide for some fascinating lab experiments in artificial neuronal networks.  There is a lot to be learned.

But please go easy on the claims.  The field of artificial intelligence has left a junkyard of exaggerated claims and absurd unfulfilled predictions across the landscape of the past 50 years.  Stick to the facts.

Actually, No, IBM Did NOT Simulate a Cat's Brain

Cognitive computing seeks to engineer the mind by reverse engineering the brain. The mind arises from the brain, which is made up of billions of neurons that are liked by an internet like network. An emerging discipline, cognitive computing is about building the mind, by understanding the brain. It synthesizes neuroscience, computer science, psychology, philosophy, and mathematics to understand and mechanize the mental processes. Cognitive computing will lead to a universal computing platform that can handle a wide variety of spatio-temporally varying sensor streams. _Source

Image via Brian WangAt a supercomputing conference in Portland, Oregon, IBM announced a successful simulation of a "cat brain scale" silicon neuro-computing platform. You can read an engrossing account of this achievement from Dharmendra S. Modha (via Brian Wang).

The achievement is a fascinating one, and worth celebrating. But it would be inaccurate to describe the accomplishment as the simulation of a cat brain. IBM's simulation is nowhere close to a simulation of a cat's brain -- and we should have no illusions regarding the comparison. Consider this:

...using Dawn Blue Gene / P supercomputer at Lawrence Livermore National Lab with 147,456 processors and 144 TB of main memory, we achieved a simulation with 1 billion spiking neurons and 10 trillion individual learning synapses. This is equivalent to 1,000 cognitive computing chips each with 1 million neurons and 10 billion synapses, and exceeds the scale of cat cerebral cortex. The simulation ran 100 to 1,000 times slower than real-time. _ Modha

Some of the neuronal level phenomena observed in the simulation (in response to stimuli) vaguely resembled actual neurocortical electrophysiological activity observed in a mammalian brain. But the parallels are quite loose. And considering the time scale of the simulation was between 100 and 1,000 times slower than real-time, tweaking the stimuli and response to better match mammalian equivalents may take some while.

IBM's simulation of 1 billion spiking neurons was interesting, but a very pale shadow of a genuine cat brain, with all its specialised structural cortical and sub-cortical components -- and accessory (but very influential) glial and vascular infrastructure.

IBM is estimating that around the year 2018, it will be able to simulate a human-scale "brain" in real time. That is preposterous. Only if you accept that IBM has simulated a cat's brain in 2009 would you accept that IBM may simulate a human brain by 2018. But as IBM readily admits, it has not even simulated a cat's multi-specialised cortex -- much less a cat's entire brain.

In terms of details in our simulations, we are currently working on differentiating our cortical region into specific areas (such as primary visual cortex or motor cortex) and providing the long-range connections that form the circuitry between these areas in the mammalian brain. For this work, we are drawing from many studies describing the structure and input/output patterns of these areas as well as a study recently performed within IBM that collates a very large number of individual measurements of white matter, the substrate of long-range connectivity within the brain. _Modha

Give them time. I expect some amazing results from this approach to brain simulation, eventually. Unfortunately, neither the hardware nor the software tools are anywhere close to providing scientists with a semi-realistic mammalian brain at this time.

Even a realistic insect brain would be a marvelous achievement. Particularly one that operated in real time, and was sized on roughly the same scale as an actual insect.

So while Al Fin cognitive neuroscientists, engineers, and cognitive computing specialists congratulate IBM on its latest achievement, they also caution IBM not to overstate what they have actually accomplished.

25 Nov 2009 Update: Be sure to read Henry Markram's response to the IBM claims at Brian Wang's site (and in comments here).

Bottom Up Brain Building: The Missing Links

There is a way to build thinking machines that might work. Use as your model the only proof of concept of higher intelligence known to the universe: the human brain / mind. Start by developing a machine nano-architecture that can do almost as many things that the human brain can do. To do this, you must understand the brain and you must invent new machine tools for imitating what the brain does.

Berkeley professor of electrical engineering Leon Chua, has made a good start. His "memristor" devices have gotten a lot of people excited about the possiblity of designing a working machine brain from the bottom up.

When two metallic wires are separated with a few nanometers of memristive material (such as certain transition metal oxides), an electronic device is formed that acts much like a nonlinear resistor, but with a twist. The resistance varies over time as a function of the currents flowing through it. In other words, it is a resistor with memory.
The rate at which their resistance changes is extremely nonlinear in the voltage applied. Small voltages hardly perturb the resistance at all, while somewhat larger voltages can induce fast changes. _Memristor Cortical Computing

Because memristor devices are able to change their electronic characteristics based upon their electronic history, they can be seen as analogous to neuronal synapses, which change their characteristics based upon their synaptic firing history. This dynamic adaptation on the nano scale presents the possibility of building very dense self-organising electronic networking chips far beyond anything previously possible.

Intelligence is not an algorithm. It cannot be "programmed." But given the proper "neural architecture" and appropriate experience, intelligence can be evolved over time. The memristor needs to be joined by other advanced memory-electronics in order to provide for machines the extremely subtle learning that the human brain can do.

The electronic brain will be a time coming. "We're still getting to grips with this chip," says Williams. Part of the problem is that the chip is just too intelligent - rather than a standard digital pulse it produces an analogue output that flummoxes the standard software used to test chips. So Williams and his colleagues have had to develop their own test software. "All that takes time," he says.

Chua, meanwhile, is not resting on his laurels. He has been busy extending his theory of fundamental circuit elements, asking what happens if you combine the properties of memristors with those of capacitors and inductors to produce compound devices called memcapacitors and meminductors, and then what happens if you combine those devices, and so on.

"Memcapacitors may be even more useful than memristors," says Chua, "because they don't have any resistance." In theory at least, a memcapacitor could store data without dissipating any energy at all. Mighty handy - whatever you want to do with them. Williams agrees. In fact, his team is already on the case, producing a first prototype memcapacitor earlier this year, a result that he aims to publish soon. "We haven't characterised it yet," he says. With so many fundamental breakthroughs to work on, he says, it's hard to decide what to do next. Maybe a memristor could help. _NS

It should have been obvious that the tools for creating machine intelligence were inadequate to the task. Just as it should have been obvious that the models used to predict climate over multi-decadal scales are completely inadequate to the task. Many things become obvious once one throws off the blinders of conventional groupthink. If one allows oneself to think outside the box, the possibilities suddenly multiply wildly.

More: The image at top is of a slime mold, which is sometimes a single-cell organism and sometimes a multiple cell organism, depending on environmental conditions. Each biological cell is in essence an incredibly complex computing machine. Multicellular organisms possess unbelievable biological computing power, compared to non-biological computers. Imagine the computing power of a human being, complete with brain and nervous systems. It is literally beyond the power of human made hardware to emulate. But as humans develop more subtle technologies of computation, they will be able to build -- from the bottom up -- machines that have the intelligence first of insects, then of higher and higher animals.

Will humans eventually be able to evolve machines with the intelligence of humans and higher? Of course. It is also true that if humans do not choose to become more intelligent themselves, that they are finished on this planet, long term. How humans go about the enhancement of their own intelligence will make for some interesting work in the not so distant future.

Brian Wang provides more quotes and images on this topic

Silicon Brain Can Simulate Day in One Second

Since the neurons are so small, the system runs 100,000 times faster than the biological equivalent and 10 million times faster than a software simulation. “We can simulate a day in one second,” Meier notes. _NW

Image Source
The EU is sponsoring an ambitious silicon brain project called FACETS.

The FACETS project aims to address the unsolved question of how the brain computes with a concerted action of neuroscientists, computer scientists, engineers and physicists. It combines a substantial fraction of the European groups working in the field into a consortium of 13 groups from Austria, France, Germany, Hungary, Sweden, Switzerland and the UK. Since September 2005 more than 80 scientists join their efforts over a period of 4 years._FACETS.

The groups have completed a "stage 1" silicon brain with 300 neurons and a half million synapses on a single chip. Stage 2 development is underway for a brain chip containing 200,000 neurons and 50 million synapses. The nearer term goals are not to replace the human brain with a chip, but rather to use the unique strengths of brain-type reasoning -- at ultra high silicon speeds -- to supplement the data processing, analysis, and computational abilities of more conventional super computers.

To build it, the team is creating its network on a single 20cm silicon disk, a ‘wafer’, of the type normally used to mass-produce chips before they are cut out of the wafer and packaged. This approach will make for a more compact device.
So called ‘wafer-scale integration’ has not been used much before for this, as such a large circuit will certainly have manufacturing flaws. “Our chips will have faults but they are each likely to affect only a single synapse or a single connection in the network,” Meier points out. “We can easily live with that. So we exploit the fault tolerance and use the entire wafer as a neural network.”

...Practical neural computers could be only five years away. “The first step could be a little add-on to your computer at home, a device to handle very complex input data and to provide a simple decision,” Meier says. “A typical thing could be an internet search.”

In the longer term, he sees applications for neural computers wherever there are complex and difficult decisions to be made. Companies could use them, for example, to explore the consequences of critical business decisions before they are taken. In today’s gloomy economic climate, many companies will wish they already had one!
The FACETS project, which is supported by the EU’s Sixth Framework Programme for research, is due to end in August 2009 but the partners have agreed to continue working together for another year. They eventually hope to secure a follow-on project with support from both the European Commission and national agencies. _Nanowerk

As impressive as this effort appears, the end result of the project will come nowhere near the complexity or specific competencies of the human brain. But they will add enormous variety and capacity to our current computational skills. One more step toward androids that can emulate humans convincingly.

Can Smart Machines Bail Us Out?

Markram estimates that in order to accurately simulate the trillion synapses in the human brain, you'd need to be able to process about 500 petabytes of data (peta being a million billion, or 10 to the fifteenth power). That's about 200 times more information than is stored on all of Google's servers. (Given current technology, a machine capable of such power would be the size of several football fields.) Energy consumption is another huge problem. The human brain requires about 25 watts of electricity to operate. Markram estimates that simulating the brain on a supercomputer with existing microchips would generate an annual electrical bill of about $3 billion . _Henry Markram

Singularity devotees such as Ray Kurzweil place great hopes upon the advent of a super human level thinking machine that can show us the way out of the labyrinth. According to that meme, the appearance of superhuman intelligence and the onset of the singularity will coincide.

Smart machines do not have to work like the human brain, but since the human brain is the only "smart machine" we currently know of, the first ones probably will. Because we already have a "proof of concept" of the biological thinking machine, the smart money for smart machines is on the people who are trying to reverse-engineer the brain.

A recent Wired article looked at the IBM Almaden project to reverse-engineer the human brain. Several such projects are ongoing around the world, including the Blue Brain project in Lausanne, which was launched in 2005 as a joint venture with IBM. Henry Markram predicts that if progress in new computing machines continues at the current rate, he should be able to simulate a human brain in silico within 10 years. A shrewd observer of technological predictions might observe that a ten year prediction is cheap, allowing plenty of time for one to explain away the prediction.

Still, Markram is definitely one of the people to watch, if one wants to see the state of the art. The video below is two years old, from the 2006 IBM Almaden conference on cognitive computing. For anyone truly interested in the topic, the videos from that conference are worth watching.

Can smart machines bail us out? It depends on where they go to school, on what they learn and from whom. Smart machines, like smart brains, can develop along twisted, dysfunctional lines. Wisdom, intelligence, character, experience, perspective, savvy, competence, vision, are all important for developing truly smart brains and machines. Is it fair to expect from machines what we are unwilling to develop in ourselves? If we were serious about all this, would our educational systems, entertainment methods, and news/informational means take their present forms? Not likely.

Intelligent machines are possible -- we are proof of concept. Whether we are foresighted enough to evolve machines that are both intelligent and wise (from a human perspective) is another question.