Part 8 (1/2)

In chapter 3, I mentioned the work of robotics pioneer Hans Moravec, who has been reverse engineering the image processing done by the retina and early visual-processing regions in the brain. For more than thirty years Moravec has been constructing systems to emulate the ability of our visual system to build representations of the world. It has only been recently that sufficient processing power has been available in microprocessors to replicate this human-level feature detection, and Moravec is applying his computer simulations to a new generation of robots that can navigate unplanned, complex environments with human-level vision.105 Carver Mead has been pioneering the use of special neural chips that utilize transistors in their native a.n.a.log mode, which can provide very efficient emulation of the a.n.a.log nature of neural processing. Mead has demonstrated a chip that performs the functions of the retina and early transformations in the optic nerve using this approach.106 A special type of visual recognition is detecting motion, one of the focus areas of the Max Planck Inst.i.tute of Biology in Tubingen, Germany. The basic research model is simple: compare the signal at one receptor with a time-delayed signal at the adjacent receptor.107 This model works for certain speeds but leads to the surprising result that above a certain speed, increases in the I velocity of an observed object will decrease the response of this motion detector. Experimental results on animals (based on behavior and a.n.a.lysis of I, neuronal outputs) and humans (based on reported perceptions) have closely matched the model. This model works for certain speeds but leads to the surprising result that above a certain speed, increases in the I velocity of an observed object will decrease the response of this motion detector. Experimental results on animals (based on behavior and a.n.a.lysis of I, neuronal outputs) and humans (based on reported perceptions) have closely matched the model.

Other Works in Progress: An Artificial Hippocampus and an Artificial Olivocerebellar Region

The hippocampus is vital for learning new information and long-term storage of memories. Ted Berger and his colleagues at the University of Southern California mapped the signal patterns of this region by stimulating slices of rat hippocampus with electrical signals millions of times to determine which input produced a corresponding output.108 They then developed a real-time mathematical model of the transformations performed by layers of the hippocampus and programmed the model onto a chip. They then developed a real-time mathematical model of the transformations performed by layers of the hippocampus and programmed the model onto a chip.109 Their plan is to test the chip in animals by first disabling the corresponding hippocampus region, noting the resulting memory failure, and then determining whether that mental function can be restored by installing their hippocampal chip in place of the disabled region. Their plan is to test the chip in animals by first disabling the corresponding hippocampus region, noting the resulting memory failure, and then determining whether that mental function can be restored by installing their hippocampal chip in place of the disabled region.

Ultimately, this approach could be used to replace the hippocampus in patients affected by strokes, epilepsy, or Alzheimer's disease. The chip would be located on a patient's skull, rather than inside the brain, and would communicate with the brain via two arrays of electrodes, placed on either side of the damaged hippocampal section. One would record the electrical activity coming from the rest of the brain, while the other would send the necessary instructions back to the brain.

Another brain region being modeled and simulated is the olivocerebellar region, which is responsible for balance and coordinating the movement of limbs. The goal of the international research group involved in this effort is to apply their artificial olivocerebellar circuit to military robots as well as to robots that could a.s.sist the disabled.110 One of their reasons for selecting this particular brain region was that ”it's present in all vertebrates-it's very much the same from the most simple to the most complex brains,” explains Rodolfo Llinas, one of the researchers and a neuroscientist at New York University Medical School. ”The a.s.sumption is that it is conserved [in evolution] because it embodies a very intelligent solution. As the system is involved in motor coordination-and we want to have a machine that has sophisticated motor control-then the choice [of the circuit to mimic] was easy.” One of their reasons for selecting this particular brain region was that ”it's present in all vertebrates-it's very much the same from the most simple to the most complex brains,” explains Rodolfo Llinas, one of the researchers and a neuroscientist at New York University Medical School. ”The a.s.sumption is that it is conserved [in evolution] because it embodies a very intelligent solution. As the system is involved in motor coordination-and we want to have a machine that has sophisticated motor control-then the choice [of the circuit to mimic] was easy.”

One of the unique aspects of their simulator is that it uses a.n.a.log circuits. Similar to Mead's pioneering work on a.n.a.log emulation of brain regions, the researchers found substantially greater performance with far fewer components by using transistors in their native a.n.a.log mode.

One of the team's researchers, Ferdinando Mussa-Ivaldi, a neuroscientist at Northwestern University, commented on the applications of an artificial olivocerebellar circuit for the disabled: ”Think of a paralyzed patient. It is possible to imagine that many ordinary tasks-such as getting a gla.s.s of water, dressing, undressing, transferring to a wheelchair-could be carried out by robotic a.s.sistants, thus providing the patient with more independence.”

Understanding Higher-Level Functions: Imitation, Prediction, and Emotion

Operations of thought are like cavalry charges in a battle-they are strictly limited in number, they require fresh horses, and must only be made at decisive moments.-ALFRED NORTH WHITEHEAD But the big feature of human-level intelligence is not what it does when it works but what it does when it's stuck.-MARVIN MINSKY If love is the answer, could you please rephrase the question?-LILY TOMLIN

Because it sits at the top of the neural hierarchy, the part of the brain least well understood is the cerebral cortex. This region, which consists of six thin layers in the outermost areas of the cerebral hemispheres, contains billions of neurons. According to Thomas M. Bartol Jr. of the Computational Neurobiology Laboratory of the Salk Inst.i.tute of Biological Studies, ”A single cubic millimeter of cerebral cortex may contain on the order of 5 billion ... synapses of different shapes and sizes.” The cortex is responsible for perception, planning, decision making and most of what we regard as conscious thinking.

Our ability to use language, another unique attribute of our species, appears to be located in this region. An intriguing hint about the origin of language and a key evolutionary change that enabled the formation of this distinguis.h.i.+ng skill is the observation that only a few primates, including humans and monkeys, are able to use an (actual) mirror to master skills. Theorists Giacomo Rizzolatti and Michael Arbib hypothesized that language emerged from manual gestures (which monkeys-and, of course, humans-are capable of). Performing manual gestures requires the ability to mentally correlate the performance and observation of one's own hand movements.111 Their ”mirror system hypothesis” is that the key to the evolution of language is a property called ”parity,” which is the understanding that the gesture (or utterance) has the same meaning for the party making the gesture as for the party receiving it; that is, the understanding that what you see in a mirror is the same (although reversed left-to-right) as what is seen by someone else watching you. Other animals are unable to understand the image in a mirror in this fas.h.i.+on, and it is believed that they are missing this key ability to deploy parity. Their ”mirror system hypothesis” is that the key to the evolution of language is a property called ”parity,” which is the understanding that the gesture (or utterance) has the same meaning for the party making the gesture as for the party receiving it; that is, the understanding that what you see in a mirror is the same (although reversed left-to-right) as what is seen by someone else watching you. Other animals are unable to understand the image in a mirror in this fas.h.i.+on, and it is believed that they are missing this key ability to deploy parity.

A closely related concept is that the ability to imitate the movements (or, in the case of human babies, vocal sounds) of others is critical to developing language.112 Imitation requires the ability to break down an observed presentation into parts, each of which can then be mastered through recursive and iterative refinement. Imitation requires the ability to break down an observed presentation into parts, each of which can then be mastered through recursive and iterative refinement.

Recursion is the key capability identified in a new theory of linguistic competence. In Noam Chomsky's early theories of language in humans, he cited many common attributes that account for the similarities in human languages. In a 2002 paper by Marc Hauser, Noam Chomsky, and Tec.u.mseh Fitch, the authors cite the single attribution of ”recursion” as accounting for the unique language faculty of the human species.113 Recursion is the ability to put together small parts into a larger chunk, and then use that chunk as a part in yet another structure and to continue this process iteratively. In this way, we are able to build the elaborate structures of sentences and paragraphs from a limited set of words. Recursion is the ability to put together small parts into a larger chunk, and then use that chunk as a part in yet another structure and to continue this process iteratively. In this way, we are able to build the elaborate structures of sentences and paragraphs from a limited set of words.

Another key feature of the human brain is the ability to make predictions, including predictions about the results of its own decisions and actions. Some scientists believe that prediction is the primary function of the cerebral cortex, although the cerebellum also plays a major role in the prediction of movement.

Interestingly, we are able to predict or antic.i.p.ate our own decisions. Work by physiology professor Benjamin Libet at the University of California at Davis shows that neural activity to initiate an action actually occurs about a third of a second before the brain has made the decision to take the action. The implication, according to Libet, is that the decision is really an illusion, that ”consciousness is out of the loop.” The cognitive scientist and philosopher Daniel Dennett describes the phenomenon as follows: ”The action is originally precipitated in some part of the brain, and off fly the signals to muscles, pausing en route to tell you, the conscious agent, what is going on (but like all good officials letting you, the b.u.mbling president, maintain the illusion that you started it all).”114 A related experiment was conducted recently in which neurophysiologists electronically stimulated points in the brain to induce particular emotional feelings. The subjects immediately came up with a rationale for experiencing those emotions. It has been known for many years that in patients whose left and right brains are no longer connected, one side of the brain (usually the more verbal left side) will create elaborate explanations (”confabulations”) for actions initiated by the other side, as if the left side were the public-relations agent for the right side.

The most complex capability of the human brain-what I would regard as its cutting edge-is our emotional intelligence. Sitting uneasily at the top of our brain's complex and interconnected hierarchy is our ability to perceive and respond appropriately to emotion, to interact in social situations, to have a moral sense, to get the joke, and to respond emotionally to art and music, among other high-level functions. Obviously, lower-level functions of perception and a.n.a.lysis feed into our brain's emotional processing, but we are beginning to understand the regions of the brain and even to model the specific types of neurons that handle such issues.

These recent insights have been the result of our attempts to understand how human brains differ from those of other mammals. The answer is that the differences are slight but critical, and they help us discern how the brain processes emotion and related feelings. One difference is that humans have a larger cortex, reflecting our stronger capability for planning, decision making, and other forms of a.n.a.lytic thinking. Another key distinguis.h.i.+ng feature is that emotionally charged situations appear to be handled by special cells called spindle cells, which are found only in humans and some great apes. These neural cells are large, with long neural filaments called apical dendrites that connect extensive signals from many other brain regions. This type of ”deep” interconnectedness, in which certain neurons provide connections across numerous regions, is a feature that occurs increasingly as we go up the evolutionary ladder. It is not surprising that the spindle cells, involved as they are in handling emotion and moral judgment, would have this form of deep interconnectedness, given the complexity of our emotional reactions.

What is startling, however, is how few spindle cells there are in this tiny region: only about 80,000 in the human brain (about 45,000 in the right hemisphere and 35,000 in the left hemisphere). This disparity appears to account for the perception that emotional intelligence is the province of the right brain, although the disproportion is modest. Gorillas have about 16,000 of these cells, bon.o.bos about 2,100, and chimpanzees about 1,800. Other mammals lack them completely.

Dr. Arthur Craig of the Barrow Neurological Inst.i.tute in Phoenix has recently provided a description of the architecture of the spindle cells.115 Inputs from the body (estimated at hundreds of megabits per second), including nerves from the skin, muscles, organs, and other areas, stream into the upper spinal cord. These carry messages about touch, temperature, acid levels (for example, lactic acid in muscles), the movement of food through the gastrointestinal tract, and many other types of information. This data is processed through the brain stem and midbrain. Key cells called Lamina 1 neurons create a map of the body representing its current state, not unlike the displays used by flight controllers to track airplanes. Inputs from the body (estimated at hundreds of megabits per second), including nerves from the skin, muscles, organs, and other areas, stream into the upper spinal cord. These carry messages about touch, temperature, acid levels (for example, lactic acid in muscles), the movement of food through the gastrointestinal tract, and many other types of information. This data is processed through the brain stem and midbrain. Key cells called Lamina 1 neurons create a map of the body representing its current state, not unlike the displays used by flight controllers to track airplanes.

The information then flows through a nut-size region called the posterior ventromedial nucleus (VMpo), which apparently computes complex reactions to bodily states such as ”this tastes terrible,” ”what a stench,” or ”that light touch is stimulating.” The increasingly sophisticated information ends up at two regions of the cortex called the insula. These structures, the size of small fingers, are located on the left and right sides of the cortex. Craig describes the VMpo and the two insula regions as ”a system that represents the material me.”

Although the mechanisms are not yet understood, these regions are critical to self-awareness and complicated emotions. They are also much smaller in other animals. For example, the VMpo is about the size of a grain of sand in macaque monkeys and even smaller in lower-level animals. These findings are consistent with a growing consensus that our emotions are closely linked to areas of the brain that contain maps of the body, a view promoted by Dr. Antonio Damasio at the University of Iowa.116 They are also consistent with the view that a great deal of our thinking is directed toward our bodies: protecting and enhancing them, as well as attending to their myriad needs and desires. They are also consistent with the view that a great deal of our thinking is directed toward our bodies: protecting and enhancing them, as well as attending to their myriad needs and desires.

Very recently yet another level of processing of what started out as sensory information from the body has been discovered. Data from the two insula regions goes on to a tiny area at the front of the right insula called the frontoinsular cortex. This is the region containing the spindle cells, and tMRI scans have revealed that it is particularly active when a person is dealing with high-level emotions such as love, anger, sadness, and s.e.xual desire. Situations that strongly activate the spindle cells include when a subject looks at her romantic partner or hears her child crying.

Anthropologists believe that spindle cells made their first appearance ten to fifteen million years ago in the as-yet-undiscovered common ancestor to apes and early hominids (the family of humans) and rapidly increased in numbers around one hundred thousand years ago. Interestingly, spindle cells do not exist in newborn humans but begin to appear only at around the age of four months and increase significantly from ages one to three. Children's ability to deal with moral issues and perceive such higher-level emotions as love develop during this same time period.

The spindle cells gain their power from the deep interconnectedness of their long apical dendrites with many other brain regions. The high-level emotions that the spindle cells process are affected, thereby, by all of our perceptual and cognitive regions. It will be difficult, therefore, to reverse engineer the exact methods of the spindle cells until we have better models of the many other regions to which they connect. However, it is remarkable how few neurons appear to be exclusively involved with these emotions. We have fifty billion neurons in the cerebellum that deal with skill formation, billions in the cortex that perform the transformations for perception and rational planning, but only about eighty thousand spindle cells dealing with high-level emotions. It is important to point out that the spindle cells are not doing rational problem solving, which is why we don't have rational control over our responses to music or over falling in love. The rest of the brain is heavily engaged, however, in trying to make sense of our mysterious high-level emotions.

Interfacing the Brain and Machines

I want to do something with my life; I want to be a cyborg.-KEVIN WARWICK

Understanding the methods of the human brain will help us to design similar biologically inspired machines. Another important application will be to actually interface our brains with computers, which I believe will become an increasingly intimate merger in the decades ahead.

Already the Defense Advanced Research Projects Agency is spending $24 million per year on investigating direct interfaces between brain and computer. As described above (see the section ”The Visual System” on p. 185), Tomaso Poggio and James DiCarlo at MIT, along with Christof Koch at the California Inst.i.tute of Technology (Caltech), are attempting to develop models of the recognition of visual objects and how this information is encoded. These could eventually be used to transmit images directly into our brains.

Miguel Nicolelis and his colleagues at Duke University implanted sensors in the brains of monkeys, enabling the animals to control a robot through thought alone. The first step in the experiment involved teaching the monkeys to control a cursor on a screen with a joystick. The scientists collected a pattern of signals from EEGs (brain sensors) and subsequently caused the cursor to respond to the appropriate patterns rather than physical movements of the joystick. The monkeys quickly learned that the joystick was no longer operative and that they could control the cursor just by thinking. This ”thought detection” system was then hooked up to a robot, and the monkeys were able to learn how to control the robot's movements with their thoughts alone. By getting visual feedback on the robot's performance, the monkeys were able to perfect their thought control over the robot. The goal of this research is to provide a similar system for paralyzed humans that will enable them to control their limbs and environment.

A key challenge in connecting neural implants to biological neurons is that the neurons generate glial cells, which surround a ”foreign” object in an attempt to protect the brain. Ted Berger and his colleagues are developing special coatings that will appear to be biological and therefore attract rather than repel nearby neurons.

Another approach being pursued by the Max Planck Inst.i.tute for Human Cognitive and Brain Sciences in Munich is directly interfacing nerves and electronic devices. A chip created by Infineon allows neurons to grow on a special substrate that provides direct contact between nerves and electronic sensors and stimulators. Similar work on a ”neurochip” at Caltech has demonstrated two-way, noninvasive communication between neurons and electronics.117 We have already learned how to interface surgically installed neural implants. In cochlear (inner-ear) implants it has been found that the auditory nerve reorganizes itself to correctly interpret the multichannel signal from the implant. A similar process appears to take place with the deep-brain stimulation implant used for Parkinson's patients. The biological neurons in the vicinity of this FDA-approved brain implant receive signals from the electronic device and respond just as if they had received signals from the biological neurons that were once functional. Recent versions of the Parkinson's-disease implant provide the ability to download upgraded software directly to the implant from outside the patient.

The Accelerating Pace of Reverse Engineering the Brain

h.o.m.o sapiens, the first truly free species, is about to decommission natural selection, the force that made us....[S]oon we must look deep within ourselves and decide what we wish to become.-E. O. WILSON, CONSILIENCE: THE UNITY OF KNOWLEDGE, 1998 We know what we are, but know not what we may be.-WILLIAM SHAKESPEARE The most important thing is this: Tobe able at any moment to sacrifice what we are for what we could become.-CHARLES DUBOIS

Some observers have expressed concern that as we develop models, simulations, and extensions to the human brain we risk not really understanding what we are tinkering with and the delicate balances involved. Author W. French Anderson writes:

We may be like the young boy who loves to take things apart. He is bright enough to disa.s.semble a watch, and maybe even bright enough to get it back together so that it works. But what if he tries to ”improve” it? ... The boy can understand what is visible, but he cannot understand the precise engineering calculations that determine exactly how strong each spring should be....Attempts on his part to improve the watch will probably only harm it....I fear ... we, too, do not really understand what makes the [lives] we are tinkering with tick.118

Anderson's concern, however, does not reflect the scope of the broad and painstaking effort by tens of thousands of brain and computer scientists to methodically test out the limits and capabilities of models and simulations before taking them to the next step. We are not attempting to disa.s.semble and reconfigure the brain's trillions of parts without a detailed a.n.a.lysis at each stage. The process of understanding the principles of operation of the brain is proceeding through a series of increasingly sophisticated models derived from increasingly accurate and high-resolution data.

As the computational power to emulate the human brain approaches-we're almost there with supercomputers-the efforts to scan and sense the human brain and to build working models and simulations of it are accelerating. As with every other projection in this book, it is critical to understand the exponential nature of progress in this field. I frequently encounter colleagues who argue that it will be a century or longer before we can understand in detail the methods of the brain. As with so many long-term scientific projections, this one is based on a linear view of the future and ignores the inherent acceleration of progress, as well as the exponential growth of each underlying technology. Such overly conservative views are also frequently based on an underestimation of the breadth of contemporary accomplishments, even by pract.i.tioners in the field.

Scanning and sensing tools are doubling their overall spatial and temporal resolution each year. Scanning-bandwidth, price-performance, and image-reconstruction times are also seeing comparable exponential growth. These trends hold true for all of the forms of scanning: fully noninvasive scanning, in vivo scanning with an exposed skull, and destructive scanning. Databases of brain-scanning information and model building are also doubling in size about once per year.