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.

Playthings of the Gods

Technology Review
Leon Chua -- father of Amy Chua -- conceived the memristor in a paper back in 1971. The memristor is a resistor with a memory of an earlier state. It behaves differently, depending upon its history. Because inter-neuronal synapses typically also behave differently, depending upon their histories, the memristor is often seen as a building-block for creating more brain-like computers. Researchers are already simulating what a memristor-based computing system might look like:

Memristors are resistors that "remember" the state they were in, which changes according to the current passing through them. They are expected to revolutionise the design and capabilities of electronic circuits and may even make possible brain-like architectures in silicon, since neurons behave like memristors.

Today, we see one of the first revolutionary circuits thanks to Yuriy Pershin at the University of South Carolina and Massimiliano Di Ventra at the University of California, San Diego, two pioneers in this field. Their design is a memristor processor that solves mazes and it is remarkably simple.

...Pershin and Di Ventra begin by creating a kind of a universal maze in the form of a grid of memristors, in other words an array in which each node is connected to another by a memristor and a switch. This can be made to represent any regular maze by switching off certain connections within the array.

Solving this maze is then simple. Simply connect a voltage across the start and finish of the maze and wait. "The current flows only along those memristors that connect the entrance and exit points," say Pershin and Di Ventra. This changes the state of those memristors allowing them to be easily identified. The chain of these memristors is then the solution.

That's potentially much quicker than other maze solving strategies which effectively work in series. "The maze is solved in a massively parallel way, since all memristors in the network participate simultaneously in the calculation," they say. _TechnologyReview_via_NextBigFuture

One of the problems with asking a physicist, engineer, or computer scientist to devise a brain-like computer, is that persons trained strictly within these disciplines are not likely to know which elements of brain functioning should be "simplified" or "abstracted", and which elements should be closely copied.

The pursuit of artificial intelligence is rife with failed promises and predictions, over the past 60+ years. If we are not to go at least another 60 years without meaningful success, we will need researchers who are cross-trained in multiple disciplines relating to the problem.

The research described in the Technology Review article above was based upon the simulation of an array of memristors -- not on an actual memristor circuit. But even with real memristors, the circuit is simplistic in the extreme. The idea that one could assemble large numbers of simplified "synapses" into something that might behave like a biological brain -- in any meaningful way -- appears silly to anyone with even a basic understanding of how the brain works. And yet such silliness represents one of many parallel hopes for a so-far failed endeavour: artificial intelligence.

The synapse is not the basic unit of human intelligence or consciousness. The basic unit of human consciousness is something far less substantial and more ephemeral. It exists at multiple logical levels above the synaptic level. It is dependent upon the simultaneous function of trillions of synapses of distinctly multiple types, involving efferent, afferent, and re-entrant activity at multiple logical levels.

What the researchers describe in the Technology Review article is the simulation of a toy. Not the toy itself -- a simulation of the toy. The human brain is not a toy. Unless, of course, you are a god.

Memristor Brains? No, But Likely a Step in the Right Direction

IEEE
Brian Wang presents a fascinating glimpse at the next stage of attempted machine intelligence -- driven by DARPA grants. The approach will likely involve the use of the Chua memristor -- or similar nano-scaled electronic devices. DARPA has specified its requirements for its new family of scalable and adaptive electronic thinking systems, and it appears that the memristor family of devices may be the best approach for government contractors wishing to collect their fees.

Researchers have suspected for decades that real artificial intelligence can't be done on traditional hardware, with its rigid adherence to Boolean logic and vast separation between memory and processing. But that knowledge was of little use until about two years ago, when HP built a new class of electronic device called a memristor. Before the memristor, it would have been impossible to create something with the form factor of a brain, the low power requirements, and the instantaneous internal communications. Turns out that those three things are key to making anything that resembles the brain and thus can be trained and coaxed to behave like a brain. In this case, form is function, or more accurately, function is hopeless without form.

Basically, memristors are small enough, cheap enough, and efficient enough to fill the bill. Perhaps most important, they have key characteristics that resemble those of synapses. That's why they will be a crucial enabler of an artificial intelligence worthy of the term.

The entity bankrolling the research that will yield this new artificial intelligence is the U.S. Defense Advanced Research Projects Agency (DARPA). When work on the brain-inspired microprocessor is complete, MoNETA's first starring role will likely be in the U.S. military, standing in for irreplaceable humans in scout vehicles searching for roadside bombs or navigating hostile terrain. But we don't expect it to spend much time confined to a niche. Within five years, powerful, brainlike systems will run on cheap and widely available hardware. _IEEE

A step in the right direction? Yes. The memristor family of devices will allow for a nanoscale fabrication of devices which function very much like a inter-neuronal synapse. Creating massively parallel circuits with such devices will allow designers to produce some fascinating -- and possibly quite functional -- computing devices.

But will these devices work anything like the human (or animal) brain? Not anytime soon. Because the designers seem focused on one small, rudimentary aspect of the human brain -- the neuronal synapse -- it is unlikely that they will achieve the "bigger picture" view of how human brains actually work for a long, long time, and after many failures.

But the development of electronic devices which imitate the synapse more accurately will place the pursuit of the machine brain on an entirely different level, above and away from the diminutive local optima which previous AI researchers have been struggling to achieve.

What will it take for memristor family devices to approach human brain level of function? First, it will require the knowledge that the brain has many distinct types of neurons, which form many distinct types of synapses. Next, it will require the awareness that synapses are just the meager beginning of the spark of intelligence. It is actually a vast ensemble of synaptic actions occurring in precise ways at precise times, and affecting precise modular systems of processors, which makes animal-style consciousness and intelligence possible.

Then, it will require the insight that intelligence is "embodied," to start the research down a long, difficult, but final road toward the creation of a rudimentary working machine intelligence.

If you are thinking that there are other approaches to intelligence than the animal or human approach, Al Fin cognitive scientists respond, "of course." But where are these alternative approaches? Where are their proofs of concept, their working prototypes? No closer today, than in the late 1940s and 1950s when absolutely brilliant computer scientists first believed they were within easy reach.

Human level machine intelligence would create a radical revolution of human existence at many levels, in many ways. But such a development does not appear to be very close. Certainly, humans are not ready for it. But a lot of things happen which humans are not prepared to experience. Better start getting ready now.

More: Brain Inspired Computing by Versace (via Brian Wang)

Moneta Neuromorphics Laboratory (via Brian Wang)