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HOME HUMAN MIND Intelligence Consciousness		Notice: This page is based on the eBook THE HUMAN MIND by Martin Dak and compares human brains with computers. Since the content may upset you, you agree that you shall defend, indemnify, and hold harmless the author and the publisher against any and all claims, losses, expenses, and lawsuits that may arise from this visit. Leave now or read at your risk.       INTRODUCTION   Ever more often, we encounter ideas that computers are becoming smarter and will outdo the human brain one day. Projections like these are enthusiastically welcome by the computerized public, and looked at skeptically by most psychologists and philosophers. The issue is not just a clash between the brain and a machine, but whether or not human ingenuity is as good or better than nature is. The competition ultimately translates into a struggle between nature and science. There is a strong belief that by studying natural sciences we can understand the laws of the universe and apply them more consistently and in better ways than nature can. Nature has produced life, the brain, and intelligence, and science is believed to have the same potential. However, science and nature are vastly different forces. Nature produces intelligence of the brain in a natural way. Science is a product of the brain. Any experience of man with the natural world is interpreted by the brain before the ideas are assembled into scientific knowledge. Conclusions drawn from scientific studies are only as good as human reasoning is. Highly intelligent brains can understand the world correctly and can benefit from learning. By contrast, cognitive deficits may skew interpretation of the natural world and may lead to odd beliefs and pursuit of unrealistic goals. Which of these two possibilities is gaining momentum can be deduced from human history. Human beings are part of the natural world. They have evolved from lower organisms over a relatively long time. The evolutionary process only happened because living things interact with the environment and are shaped by it physically and mentally. High degree of interaction between an organism and the surroundings leads to acquisition of the ability to negotiate less-than-perfect environmental conditions. The ability of some living things to interact with the environment has become so high that the word intelligence has been coined. Humans have the best ability to interact with their world and are most intelligent of all known life forms. Intelligence and life itself have fascinated people ever since the dawn of humanity. How is it possible that an organism can move, breath, and think? What mysterious force is behind such abilities? The prehistoric man could not figure out the answers to these questions. The level of intelligence was not sufficiently high, and a serious exploration of the puzzling issues was not possible, because of nonexistent science. About 10,000 years ago, humans began creating permanent settlements. This social change allowed rapid expansion of social knowledge, trades, engineering, and sciences. The following millennia brought countless discoveries about the natural world. Little by little, humans learned to interact with the environment efficiently. At first, people created simple tools to be used directly by humans, such as spear, hammer, sewing needle, wheel, or potter's wheel. This significant step clearly signaled predominance of reason over brute muscle force. Gradually, people learned to develop machines that worked even without human supervision. There were rabbit snares, fishing nets, or irrigation canals. Human muscles still remained indispensable in most activities, but the simple devices tremendously expanded human abilities. Within a few thousand years, humans constructed water mills and animal-drawn carriages. Later, steam power gave rise to machines in industry and transportation. Combustion engines allowed humans to mass produce cars. The birth of computers and the robotic industry has allowed humans to let machines take over traditional human labor, from making coffee to flying a space ship. The rapid deployment of sophisticated machines and science in our lives has been nothing short of spectacular. The great successes of the past have made many people believe that machines will one day not only do things for us, but will also think. Some scientists even predict that machines will be much smarter than human are. The increasing pace of scientific discoveries and the speed of transition from theoretical concepts to their implementation give many optimists the feeling that artificial intelligence is not only possible, but is likely to be here within a few decades. But are these assumptions correct? Or do they stand for delusion and wishful thinking?       THE NATURE OF MACHINES   The word machine implies mindless activity. A car, lathe, or computer only does what humans program it to do. Employment of sensors and guidance systems has allowed some machines to exhibit seemingly intelligent behaviors. An airplane can be programmed to take off in Los Angeles and land in New York without human involvement. The man-made program is executed correctly and gives the impression that the airplane thinks. Some people even express their beliefs along these lines: "The computer thinks that …" The computer does not think. The computer is just another dumb machine, although it is more complex than other machines are and has a rich repertoire. Ever since people learned to build machines, they have been attempting to simulate life. The greater expertise scientists and engineers gained, the more they felt qualified to replace living creatures with machines. Various sensing devices have been constructed to detect harmful chemicals, but dogs are still the best solution to detect explosives, drugs, or the odor of a specific person. Boeing tried to use ultrasound detectors to find cracks in airplane wings, but concluded that the best detector is the human eye. The space program has been making trips beyond the edge of the solar system, but no rocket scientist would dare to tell a sparrow how to fly. Some stunning progress has been made, however. Humans can build high performance telescopes to boost human vision beyond the visual abilities of any bird of prey. Night vision goggles allow people to see in the dark better than most animals can. But these achievements are minuscule. No one can construct an artificial eye that would have visual abilities similar to those of humans. A machine with superb optical functions may be constructed one day. A more difficult problem is interpretation of the image and its separation into representations of individual optical objects. But the most challenging issue is cognitive interpretation of the imagery. Optical aspects of vision are not enough to grasp the meaning of a seen object. The interpreter of the image needs to employ the logical faculties of the brain. This is where a difference between machines and living things is clearly apparent.     ANALOG AND DIGITAL COMPUTERS   The main difference between analog and digital computers is not in what they do, but how they do it. Analog computers process information in a continuous fashion and can handle a wide range of naturally occurring processes. An analog computer receives one or more variables and produces a result that represents the relationships between the input variables. Perhaps the simplest example of an analog computer is an oscilloscope. It receives vertical and horizontal signals, and produces a visual trace on the oscilloscope screen. The oscilloscope does not truly compute, but puts the input signals in the desired relationship. More generally, the relationship is called a function. Electronic analog devices are capable of producing various mathematical and logical functions, including logarithms, integration, and differentiation. Some complex functions may not be solvable with digital computers, but analog computers can usually handle them well. The main disadvantage of analog computers is that they are hardwired and designed to process only a limited number of functions by means of dedicated electronic devices. This deficit is eliminated in digital computers. Digital computers represent information in binary states of 0's (zeros) or 1's (ones). A "0" usually stands for low voltage (close to zero volts), and a "1" means that a voltage (usually 5 V or 3.3 V) is present. One wire connection is represented by one bit of information. The value of the bit is "0" or "1." Two bits can represent two wires. Each bit can have the values of "0" or "1" at different times, which allows to represent four unique states or events with the values 00, 01, 10, and 11. The state 00 means that both wires have no voltage applied at a given time, and 11 means that both wires have the nominal voltages present at the same time. By increasing the number of wire connections, long strings of 0's and 1's (words) can be produced. Each unique combination of 0's and 1's is decoded and represents a unique number, or information in general. A set of related wires is referred to as a bus. A bus can have 64 or more wire connections arranged in parallel and is controlled by a microprocessor. The microprocessor determines what kind of information is put on the bus at a specific time. It could be memory address, content of the memory address, or operating code (instruction to perform an action). The transfer of information over the bus is controlled by a software program. The arrangement allows the use of the same hardware (the same physical devices) to process very different information at different times. Since the computing is done one variable at a time and is controlled by a timing protocol, a digital computer does serial processing of information. This statement is not totally correct, because all bits of the same word are processed concurrently. But in the analog computer, all input variables can be processed at the same time, which allows parallel processing. Overall, the analog computer better reflects the natural world because specific functions are associated with dedicated wires and circuitry. Also human senses have dedicated sensors with direct neural connections to the brain. Each human eye has about 120 high-quality megapixels. A really good digital camera has about 16 megapixels. The numbers of megapixels between the eye and the camera are not that dramatically different, but the digital camera has no permanent wire connections between the physical sensors and the optical, computational, and memory functions of the camera. The microprocessor input and output need to be multiplexed to properly channel the flow of the arriving and exiting information. Similarly, the functional heart of a digital computer only time-shares its faculties with the attached devices: memory, camera, speaker, or printer. If such an arrangement existed in the human brain, you could do only one function at a time. You could look, then think, and then stretch out your hand to pick up an object. But you could not speak, see, hear, think, move, and feel at the same time. These problems could be solved by operating numerous microprocessors concurrently, but the hardware would be too difficult to design, too bulky to package, and too expensive to implement. By contrast, parallel processing poses no problem in the human brain. Neurons are tiny, come to life in huge numbers, and form connections spontaneously. Just as important is energy efficiency. Human brains require negligible amounts of energy, and power dissipation does not overheat the brain. A computer as complex as the human brain would need its own power plant with megawatts of power, and a heat sink the size of a city.     DEFICITS OF COMPUTERS   Computers are man-made machines. As such, they have been designed to perform repetitive functions. A lot of effort has been put in durability and reliability. Ability of computers to withstand temperature extremes, mechanical shocks, humidity, chemical spills, magnetic fields, and other environmental effects is of utmost importance. To meet these goals, computers must be made to strict electrical and mechanical tolerances. The computer enclosure serves as a protective shield for the sensitive electronics inside. All these qualities distinguish a good product from a shabby one. Both science and technology need to cooperate to produce quality computers, but living things are different. People and animals come in various shapes and sizes, and with many imperfections. Living things are not designed to last unchanged a lifetime. Living bodies interact with the environment and adjust to it. Furry animals shed their coats in the summer and grow more hair in the winter. A crab sheds its protective shell when it becomes small and grows a new one. A shark loses its teeth and replaces them with a new set. A lizard loses its tail and grows a new one. A polar bear has developed a special fur to negotiate the cold environment of the Arctic ocean. A seal insulates itself with extra blubber. A tree in a hot climate moves its leaves vertically to reduce evaporation. All living things respond to their surroundings, change their "mental strategies", and also modify their bodies. The reason for these abilities is that living organisms are not only biological machines, but also manufacturing plants that support reproduction, maintenance, and remodeling of the organisms in response to environmental effects. No man-made machine can do this. The differences between living things and machines are most apparent in real life. A young kitten goes around the block, falls in a swamp, and gets wet and muddy before coming home. Mother cat sees the stinking creature, licks it, and keeps it warm. She accepts her child. Machines respond differently. A 20-dollar bill goes around the block and becomes dirty before you insert it into a machine in the supermarket. And the machine complains, "Please insert a valid bill," and refuses to recognize its baby. These examples accentuate the nature of life. It is flexible. The mind and the body act in unison and influence each other. Mental processes and somatic functions are inseparable, and full consciousness requires awareness of one's body. No computer is built to account for its physical makeup and to perform the necessary structural changes to function optimally in a harsh environment. No computer walks into the shade to cool down. A software self-check only verifies that the electronic circuits work, but the machine knows nothing about itself. There is no self-awareness, and the computer is designed to work the same way in rain or shine. In a computer, there is a clear separation between the physical machine and the function it produces. To some people, the "body" and the "mind" of a computer might appear separate, but this notion is incorrect. The electronics and its enclosure are erroneously seen as the body, and the software is falsely considered to be the mind. By changing the software or the whole operating system, the computer is believed to acquire a new mind. In reality, software and hardware are two different aspects of the computer architecture. A computer does not need software. The program could be implemented with pegs located on a rotating drum or in some other mechanical way. Computer software is just part of the machine implementation, not the mind. A computer has no soul or mind. A computer has no equivalent of self-awareness or consciousness, qualities that define living organisms. The only similarity between living things and computers shows in computer responses and human behaviors. A computer can do something, and a living thing can do something. But here the similarities end. A computer has nothing that would be equivalent to the "state of the mind." A computer does not get happy, tired, angry, curious, motivated, frustrated, or embarrassed. All these deficits are given by the architecture of computers. They use solid state devices that perform the required function, but are incapable of perceiving their physical and chemical conditions. This lack of internal responsivenesss of electronic devices to arriving signals is the main difference between machines and living organisms.       BRAIN MODELS Comparing the human brain and the computer might be doable in theory. The function of computers is known. They have been designed by scientists and engineers, and all aspects of computer architecture and functions have been rigorously recorded by the designers. Anyone who wishes to spend the time can acquire the necessary information. By contrast, little is known about the functions, connectivity, cognitive architecture, and internal mechanisms of human brains. Scientific knowledge in this area is inadequate, and most experts tacitly assume that the brain and the computer have similar processes and functions. Humans have designed and implemented computers in logical ways that make sense, and there is no reason for the brain to be organized any other way. Thus, our expectation and understanding of the world steer our cognition toward the familiar. Pursuing the familiar, engineers and neuroscientists worldwide have been trying to explain the cognitive functions of the human brain for decades. Various models have been proposed. So far, no model (with the exception of Dak's) has been able to offer a comprehensive theory and explain how the brain truly works. All other models seem to heavily lean toward the brain or the mind. Some models strictly focus on one aspect, and the brain and the mind exclude each other. Furthermore, even the best models are limited in their scope. They only consider one or two functions. The most popular focus is on movement, language, vision, or memory. When these models are compared with the true physiology and functional organization of the human brain, the models appear inaccurate and inadequate. A few exceptional authors have attempted to produce comprehensive brain models by covering the majority of brain functions and faculties, but also these models fail to uncover the true functional and architectural relationships in the brain. In addition, the diverse brain functions are treated in isolated manner, and not as components of a whole cognitive system. In such models, one faculty can operate on different principles than another faculty does, and the physiology of every brain function is unique and unrelated to other functions. Because of compartmentalization and lack of relatedness, none of the all-inclusive brain models has offered sensible explanation of the physiology of the brain. As a rule, the models ignore all aspects of being human. The focus of the authors is on functional manifestations and on preconceived false ideas about the mind, when the brain is treated as if it were a computing machine. Incidentally, the models heavily focus on strict logic and scholastic intelligence, but exclude all aspects of emotional intelligence. According to Dak, the brain cannot be understood without our understanding of mental functions and their manifestations. Dak's breakthrough came thanks to his expertise in clinical psychology and his skill in applying emotional insight. Inability to notice, classify, and explain behavioral phenomena will continually misguide scientists. They will be unable to understand the functions of the brain, until they learn what the brain does. Any effort in this field should start with the study of the mind and behavior. First scientists need to identify all essential mental functions and learn about the interactions between them. Then researchers need to create a model of the cognitive architecture of the mind. And only then will neuroscientists be able to associate the model of the mind with brain anatomy and neural responses. In the end, there must be agreement between psychology, neurology, and neurobiology. Dak has succeeded in this difficult endeavor, but no research team has followed his approach. Interestingly, Rafael Yuste recently outlined similar approaches in an article "Circuit Neuroscience: the road ahead" [10]. Reading his work is sheer pleasure. Yes, there are still a few sensible scientists, but they are few. Paradoxically, had scientists followed Dak's or Yuste's philosophy, they might still be struggling with the functions of the mind and with their interactions. The fact is that there are many mental phenomena that have not been explained by research teams, not even recognized to exist! Nevertheless, despite these obvious deficits, it is interesting to review some of the better known "functional models" of the human brain. Religious and Philosophical Models It is now well established that the level of a person's intelligence reflects the type and sophistication of a brain model the person accepts and believes in. Incidentally, a believer in the supernatural readily embraces a brain model that is divorced from reality. Similarly, a philosopher who is only interested in theoretical issues of the brain will attempt to understand general concepts of the mind without considering neurobiology. Also this man's brain model exists separately from the physical world. Consequently, most religious and philosophical models of the brain consider that the brain and mind are separate. The mind is believed to be able to do whatever it wants to, including leaving the body and traveling through the outer space to the purgatory, heaven, and beyond. The leading concept of these models is that the mind is independent of the brain and exists before the brain is constructed by an intelligent designer. The brain only serves as a temporary shelter for the mind, and as an interface to control the otherwise mindless body. Most brain models of this type are naive and manifest lack of understanding of the natural world, but some models attempt to employ the most advanced scientific theories. For example, William Witherspoon proposes that the string theory and its multidimensional interpretation of the universe will solve the mysteries of the human brain [5]. Interestingly, similar separation of mind and brain is tacitly professed by psychologists and psychiatrists who believe that they can heal mental illnesses while never acknowledging that most mental patients have suffered biological brain damage. Unlike religious researchers, who rely on the supernatural, most philosophers attempt to bring science and logic into their work. The trouble is that philosophers commonly lack emotional intelligence and often focus all their effort on the comprehension of a single phenomenon. A topic of special importance to philosophers is qualia, that is personal quality of cognition. A philosopher may consider why salt is salty and not bitter, or he may wonder why red color is red and not yellow, and spends a lifetime trying to fathom these rudimentary facts. A related issue of this kind is the philosophical question: "What is the purpose of man?" There is no purpose. The question reflects reduced emotional intelligence of the inquirer. Approaches like these are not helpful and distract attention from meaningful neuropsychological work. Computational Models Computational models of the brain are perhaps the most widespread. They reflect the mainstream effort in cognitive neuroscience. There are countless models in this category, but all assume that the brain is essentially a biological machine that functions like a computer and performs computations at the molecular or neuronal level. Some fancier models combine individual neurons into functional modules that agree with neuroanatomy. A frequent theme in these models is parallel processing of information. Every neuron or neuronal cluster is believed to perform a small part of the overall processing, and the combined interactions of all neurons produce the desired functions. Using this philosophy, Swiss and IBM researchers are attempting to produce a machine that simulates function of the human brain based on their mapping of the neocortical column [2]. The detail that the brain also consists of the limbic system, brain stem, and cerebellum has somehow escaped the attention of the scientists. And other computational models are even more divorced from biological reality. An unusual computational model of the brain has been introduced by Karl Pribram. He uses holography to explain neuronal interactions [1]. Holographic models of the brain assume that information can be transferred from one brain area to another and that information is stored in multiple copies [9]. Although holographic models are not religious, they share the same concept of free flow of information and faculties in the brain. Almost any brain area seems suitable to house the soul or brain function in these models. Another high-tech model of the human brain is based on synchronization of neuronal oscillations. Using this approach, Bernhard Mitterauer and Kristen Kopp propose a brain model consisting of synchronized compartments organized in time and space [6]. Some of the ideas in these models do have merit because apparent synchronization of neural structures has been observed. Some researchers have associated synchronization of neuronal populations with conscious awareness, but the true nature of this phenomenon has been misinterpreted, and the concept of synchronization has been applied too broadly and indiscriminately. However, all the computational models suffer from a serious flaw. The models are essentially representing machines. Very complex and highly sophisticated machines, but nevertheless machines. The cognitive flaw is not inherent to the machines, but to the developers of the models. Some scientists believe that by making a machine more complex and providing it with bells and whistles the machine will somehow develop intelligence. Other researchers just hope that their effort and diligent work will lead to some useful result. They do not know what result they may obtain, but the hopes are certainly high. Many computational neuroscientists believe that the brain is a machine that implements an algorithm that can be expressed by means of mathematical equations. Although no one dares to say it aloud, the impression one gets when dealing with some brain researchers is that merely writing down the correct formulas can give rise to artificial intelligence. This belief is akin to religious beliefs. Just saying the right magical word can make things happen. However, neuroscientists of this type are a tiny minority. The overwhelming majority believes that the appropriate formulas need to be expressed through a suitable medium. But even this mentality is usually unproductive, because few researchers recognize the correct medium that produces intelligence. The mentioned brain models may lead to the impression that scientists are, similarly as religious thinkers, mentally divorced from the material world. But such a conclusion would not be entirely correct. The majority of researchers are materialists and intellectually know that the mind cannot exist without the brain. Sadly, because of deficits in the department of emotional intelligence, most brain researchers unwittingly separate the mind from the brain. The mistake is difficult to recognize because a computer, which does software processing, is perceived as an analog of the brain. In reality, the computer is just a machine. It does not perform neurobiological and neurocognitive functions as the brain does. The misguided effort of computational neuroscientists stems from a rudimentary failure to understand the connection between life and intelligence. In this regard, they are not much different from medieval alchemists who searched for the fountain of youth. Neither alchemists nor computational neuroscientists have been able to comprehend the nature of living things. Microanatomical Models These models also assume that the brain is a machine, but unlike most technical models, they stress the importance of molecular structure, DNA structure, and microbiology of neurons and their connections. The scientists hope that by focusing on the fundamental building blocks of the brain, a universal relationship will be uncovered. The organization of the whole brain will simply be obtained by repeating the established patterns. This is an interesting approach that is valid in theory, but is equivalent to the study of the solar system by focusing on the molecular structure, rather than on the functional relationships of the sun and the planets. It seems that researchers pursuing this course put too much stress on details and miss the whole picture. The flaw of this approach is apparent in the study of neurotransmitters. Many neuroscientists believe that neurotransmitters produce brain functions. This is not so. Neurotransmitters largely enable communication between brain structures. An analogy is offered by an automobile. It uses electricity, cooling fluid, windshield wiper fluid, engine oil, air, and gasoline. But these ingredients do not produce the function of the automobile. Similarly, neurotransmitters only support the functions of the brain. Inability to comprehend this basic relationship makes doctors believe that adding a neurotransmitter to a Parkinson's patient will restore the function of the patient's brain. It will not. Similarly, adding more oil to an automobile with a broken transmission will not fix the automobile. This example exposes the problem associated with narrow focus. The researchers are unable to see the forest for the trees. Neuroanatomical Models These models are based on the neural organization of the human brain and have the highest likelihood of explaining how the brain works. The problem with these models is that they do not provide enough detail and do not explain fundamental issues in neuropsychology, such as emotion, dreams, consciousness, anosognosia, or memory consolidation. The most influential model of this kind is the Triune Brain Model proposed by Paul MacLean. The model essentially assumes that the brain consists of three independent brains: the neocortex, limbic system, and brain stem. The brain stem supplies basic biological functions; the limbic system produces emotion, and the neocortex generates thought [3]. The model does not provide any more structural detail, and the rest is pure psychology that has no association with specific brain structures, timing, or processes. The model appears to be based on biological realities of the brain, but is so vague with regard to specific functions and neural structures that instinct, emotion, mind, and reason seem like unrelated entities existing independently of the brain. Most importantly, neither McLean nor his followers have successfully explained what emotion and consciousness are, and how they interact with instinct and reason. Some followers plainly state "We all know what emotion is" and refuse to analyze it any deeper. The concept of the Triune Brain has been "improved" by Ned Herrmann. He added the two-brain theory of Roger Sperry to the model [4], not realizing that Sperry's explanation of the split-brain physiology [7] is fundamentally flawed, and thus compounding the flaws of the Triune Brain Model. The discoveries of Sperry are particularly troubling because thousands of other brain researchers have confirmed his incorrect conclusions. Only a few scientists raised some objections, claiming that the brain functions are not fully explained by the proposed mechanisms. But the voices of these dissenters have been muted and have not gained acceptance in the academic, medical, and scientific communities. The brain is currently believed to have unique neuropsychological functions in each hemisphere. The division is perceived as black and white with no shades in between. Another interesting aspect is that the left (talking) hemisphere is believed to be conscious, while the right hemisphere is tacitly or explicitly associated with a less prominent cognitive role. Regarding this issue, split-brain studies have done a big disservice to science. By splitting the brain, its normal functions are altered and produce phenomena that obscure the relationships between the hemispheres. Lately, some researchers proposed that part of the brain can process information and produce action without conscious awareness [8]. It is unclear how this purported ability could relate to the Triune Brain Model. In theory, the mindless brain stem or the emotional limbic system might produce some reflexive activity. If so, one would expect that this activity would be co-activated with the biological functions of the brain stem or with the emotional states of the limbic system. It is also possible that some as yet unknown cortical mechanisms are behind such unconscious automatic responses. Whatever the purported mechanisms are, they are not explained by the proponents of the Triune Brain Model. As is apparent, the diverse categories of brain models reveal that humanity lacks a basic understanding of what the brain is. Is it a machine? Or God's supernatural device to control mindless humans? Or a biological subsystem of the human organism? Laymen, doctors, and scientists have been unable to come up with a consensus.     WHY IS THE BRAIN SO DIFFICULT TO UNDERSTAND?   Brain researchers face several big obstacles. The obvious one is that the human brain is very complex, with countless microscopic neurons and connections. Another obstacle is our lack of knowledge of the most basic brain mechanisms. We do not know how to approach the brain. Senses apparently deliver input to the brain, but beyond this assumption, we have poor understanding of how senses are processed. The discovery of ventral and dorsal visual streams has only made our work more difficult because it is unclear how these streams fit into the cognitive architecture of the brain and which parts of the brain receive the resulting signals. Similar problems exist with memory processing. So far, no research team has been able to pinpoint where in the brain memory is and how it functions. It is known that memory depends on the hippocampus to some degree. But everything else is pure speculation, beliefs, and hunches. Not considering Dak's work, there is no comprehensive theory of memory operation. Emotion is another troubling aspect of the human brain. No one seems able to explain what emotion is. Similar confusion exists about other mental functions, such as hypnosis, lucidity, or sleep. These functions exist, but their place and purpose in the neuropsychological processes of the mind are unknown to science. Since the brain is a crucial element of advanced living organisms, it is important to recognize that life does not have a universal expression. Different biochemical makeup of organisms leads to diverse perceptual qualities (qualia). Cats have black and white vision, humans can see color, and some birds can process infrared or ultraviolet frequencies. Because of the different neurobiological properties, every creature has a different experience of the world. A dolphin can use ultrasound echolocation to find a fish buried in sand; a dog can use the scent of a footprint to identify a specific animal, but man cannot notice or interpret such signals. The ability to perceive the invisible gives some animals an advantage in acquisition of information. And knowledge and the ability to reason are the key ingredients for making intelligent choices. While some animals have superb abilities to gather information in unique environments, humans are exquisitely fit to reason with the information they have. Humans are good at placing information in a context, comparing ideas, and projecting a likely outcome. Similarly as the ability to acquire information, also the ability to reason varies dramatically between species. Only big apes and humans are capable of recognizing themselves in the mirror. They understand that the reflections are their images. No other animals can figure out that they see themselves. Most appear to believe that they see another individual. Surprisingly, notable reasoning differences exist even between humans. What makes sense to one person can be perceived as utter nonsense by another. A religious astronomer may believe in the divine origin of the world and may see evidence of it throughout the universe. But an astronomer who believes in evolution draws entirely different conclusions. The surprising thing is that both persons have intelligence. They can acquire the same information and can process it. But they have different interpretation of sensory perceptions and can draw different conclusions about what they experience. These phenomena reveal that there is no such thing as "universal intelligence." Intelligence has different levels and idiosyncrasies, and not all intelligence is intelligent. All the different interpretations of reality are valid at their levels of intelligence, but the individual qualities of intelligence complicate the quest for an intelligent machine. Should it be a self-indulged womanizer like Einstein, or should it be a fanatical conqueror like Hitler, or should it be a self-sacrificial person like Mother Teresa? In addition to the obvious technical difficulties in brain research, there are subjective obstacles. The human brain (the brain of a researcher) is unlike any other machine. The researcher explores himself, his neuropsychological makeup, his cognitive processes, and his mental and emotional world. Every researcher unconsciously assumes that he is smart and that he is fully capable of comprehending how the brain works if someone explains it to him. But this belief is usually false, and unconscious mental obstacles interfere with the researcher's cognition. Common sense suggests that functions of the brain depend on anatomy, and a successful brain model should incorporate the neural responses of individual brain structures, as Dak's brain model does. Unfortunately, most brain researchers do not want to learn how the brain truly works; they want the brain to behave in agreement with their expectations. Unpopular thoughts about the brain are rejected even before they reach conscious awareness, and the conscious mind only pursues predetermined ideas. For example, no brain scientist wants to discover that he is stupid, crooked, or psychopathic. Any finding pointing toward such an outcome is either dismissed or rationalized to preserve one's self-worth. That is the inherent problem of studying the brain. If a researcher wants to fully understand how the brain functions, he has to consider not just neuroanatomy and simple responses, but he also has to explore mentation, judgment, morality, psychology, and has to do a good deal of introspection. He must have the courage to become cognitively naked, unprotected, and exposed to potentially unfavorable discoveries about himself. Most brain scholars have no inclination to deal with such sensitive issues and only focus on sensory or behavioral responses. The researchers almost entirely ignore cognitive drives or mental processes that modulate behaviors. Another problem hindering brain research is human ego. Discovering how the brain works is considered one of the most prestigious human achievements, and no scholar wants to admit that he has been beaten by somebody else. Too much prestige is at stake. This is why brain scholars emphatically proclaim that no one has explained how the brain works. The discovery must still be left open to exploration so that the researchers can function and can have a purpose in life. Discovering how the brain and mind function is equivalent to killing someone's God. Life loses all its meaning. This mentality became obvious in the fall of 2008. We invited 200 nationally recognized brain scholars to order free copies of Dak's work THE HUMAN MIND. Practically all invitees declined the offer. They did not want to believe that stunning discoveries in brain research have been made, that a comprehensive theory of the whole human brain has been developed, and that the entire field of cognitive neuroscience has changed. The issues of disbelief and refusal cannot be resolved by calling upon science. Science has failed to persuade believers in the supernatural that evolution has created the earth and life, and also neuroscientists who believe that the brain is a biological computer will be unable to accept ideas that counter their belief-based reasoning. To accept new interpretations of reality, the scientists would have to invalidate their beliefs, their understanding of the world, and their ways of thinking. Doing so is next to impossible because a person's mind is a reference of cognitive reality. Whatever the mind thinks is perceived as logical, and even false conclusions are believed to be correct. This is why many a brain researcher is stuck in an endless pursuit of scientific discoveries that lead to nowhere. Information about the existence of valid explanation of the brain physiology is rejected, and unproductive paths are followed. Because of academic frustration and no ability to see the light at the end of the tunnel, most leading brain scientists flatly refuse to hear alternative explanations to their beliefs. The scholars are sure that they are the best of the best, and if they do not understand the brain, they do not accept that anyone else could understand it. Any statement that is contrary to their beliefs is rejected. Also believers in the supernatural suffer from this mental allergy, and refuse to learn anything that supports evolution. These are symptoms of neuropsychological corruption that typically arises from defective emotional intelligence. The deficit has a slim chance of being corrected by the subject's desire to be objective. A believer in God is not searching for the truth; he has already found it. All he now wants is to find supernatural explanations to the things he still does not understand. Of course, there can be no factually correct explanation that is both supernatural and logical. The supernatural and the logical are mutually exclusive. Similarly, the belief that the human brain is a biological computer is incompatible with the notion that the brain is part of a living organism, and with the idea that the biological makeup and function are inseparable. But there are additional reasons why scholars and scientists are unwilling to learn about the true physiology and functional organization of the human brain. One reason that prevents brain researchers from learning about the brain architecture and function is purely economical. Brain researchers who have received grants to do a specific study from the position of computational neuroanatomy cannot afford to deviate from their preconceived ideas. They cannot say, "Sorry, our assumption was totally bogus. There is no point in pursuing computational techniques, because they cannot produce intelligence, or uncover the cognitive architecture of the brain, or explain the most basic human qualities." Had the team members done so, too many jobs would be lost; too many donors would withdraw their support, and the prestige of the research organization would suffer. Incidentally, every brain researcher unconsciously believes that he is on the right track and that he is an essential worker. A similar handicap is produced by the combination of morality, politics, beliefs, and emotions, and negatively affects all aspects of cognitive neuroscience. Scholars and scientists belong to research teams and the academia. Conformance with the views of the leadership and sponsors is of utmost importance if one wants to advance his or her misguided career. Not science, facts, or discoveries, but compliance with the prevailing views and practices is what is valued. By discovering how the brain works and how it malfunctions, a researcher could ruin his or her career because the academic, social, political, or legal implications of the discovery might not be acceptable to the superiors. Similarly as Galileo, also today's discoverer has to be careful when his work interferes with social beliefs and policies. And even the researcher might not like the findings. The discoveries might challenge the scientist's personal beliefs about one's mental and moral qualities. All these influences unconsciously force researchers to shy away from learning about the faculties of the human brain. By believing that the brain is a computer (that is a rational machine) and by carefully avoiding the issues of drives, emotion, and pathology, scientists stay on the solid path of political correctness. But this well-tread path has not led to our understanding of the human brain to this day and is guaranteed not to lead there in the future. Many professionals not only refuse to accept that the brain might function in certain ways; they cannot afford to recognize alternative explanations. For a century, doctors have been carving out parts of the brain, believing that they have been helping patients. As a result, numerous flimsy theories about the function of the brain have emerged. Also today, leading brain surgeons carve out or otherwise destroy neural tissue to "help" their patients. If the experts understood how the brain truly works and what they are doing to the patients, the professionals would be shocked and incapable of living with themselves. Doctors like these have no option but to continue practicing medicine the same old way, hoping that no one will ever find out about their psychopathology and malpractice.     WHAT IS THE BRAIN?   The human brain is an organ of the body, and a biological extension of the human organism. The human organism is a living entity, and every part of it, including the brain, is alive, too. Since the brain is a living system, it exhibits key characteristics of living organisms. The brain is not some passive blob of organic mass that sits inside the skull. Even a mature brain responds to environmental influences by changing its form and function. Learning and living of the brain are ongoing lifelong processes. They affect both the brain function and structure. No man-made machine has such properties. To this day, we have developed no machinery or technical mechanisms to compare the brain with. However, once the brain is understood, it will become clear that life and the function of the brain are inseparably connected. Because the brain is alive, all its parts operate in agreement with biological laws. The brain needs the body, and the body needs the brain to allow the organism to function. The brain has specialized neural structures that are dedicated to particular functions. The structures are interconnected with short and long axons (neural fibers) to communicate signals between distant areas of the brain. Neurons and their fibers carry signals by means of electricity and neurotransmitters. Individual brain structures perform dedicated functions, but no structure can work alone. Figuratively speaking, brain functions depend on neural systems; each consists of several neural structures. One type of neural systems produces monolithic functions, such as speech or vision. A different type of systems produces simple hybrid functions. The brain usually engages the monolithic and the hybrid systems concurrently. Some parts of the brain are capable of parallel processing, while other brain structures process information in series. There are also complex supersystems that consist of monolithic and hybrid subsystems. Furthermore, complex supersystems consist of primary and secondary systems, which are further divided into alpha and beta systems, and even these are divided into two-level systems. The diverse systems often interact through polymodal association areas and other structures. More specifically, the brain systems produce the cognitive mind, conscious mind, subconscious mind, unconscious mind, volitional functions, housekeeping functions, non-cognitive functions of various controllers, and interfaces between diverse types of brain systems. As the cryptic description hints, the brain has a complex hierarchy of neural systems. Understanding how the various neural structures are grouped into functional systems is one thing. Figuring out how the different systems interact and produce behavior is even more challenging. Event-related potentials and neuroimaging studies help, but are not enough. Also in this case, clinical psychology, neurology, and emotional insight need to be employed to make sense of the functional relationships. One serious misconception about the human brain is its plasticity. While reading scientific literature on this topic, one gets the impression that the brain has endless plasticity during neural development in childhood. Although it is true that a brain injury in childhood can lead to stunning reconfiguration and preservation of essential functions, it needs to be recognized that there are limits. The lesser known fact is that the performance of the spared functions always shows some deficits. They are often mild and are optimistically ignored. From the practical viewpoint, the recovery is almost complete, and the deficits are inconsequential. Almost no thought is given to the original purpose of the damaged neural structures, or to the structures that have been recruited to compensate for the lost function. How much had to be sacrificed to recover the lost function is never an issue. Most scientists conclude that the brain has a great redundancy and the destruction of one neural structure need not be catastrophic, because the brain can employ other mechanisms to compensate for the loss. And this naive notion holds true even in cases of hemispherectomy. The human brain is not a digital computer and does not work in digital ways. Analog processes throughout the natural world strongly suggest that even the brain has analog-like circuits with analog-like signal processing. Yes, of course, synapses fire when they are sufficiently activated, but this fact does not make the brain digital. All the higher cognitive functions are analog. Incidentally, the brain does not compute anything. Brain structures are innervated by environmental stimuli or by signals from other neural structures. Cognitive processes employ very simple mechanisms to retrieve, evaluate, and use information. There is no involvement of complex mathematical formulas and processes, such as the Fast Fourier Transform. The brain runs on simple biological principles and natural processes. Repetitive approaches in brain architecture and parallel processing of information are some of the most powerful techniques supporting brain functions. The exact coding format and the timing protocol used in transmission of information via neural fibers are still unknown, but Dak's research indicates that structurally identical neural fibers can carry sensory information, memories, or current thoughts. The different contents of information suggest that the brain uses the same general principles to support diverse functions. The functions use unique neurotransmitters or separate neural fibers to keep different faculties apart. The brain is hard-wired. A rough analogy of the human brain is an analog computer consisting of operational amplifiers. Each amplifier produces a dedicated function (signal selector, integrator, multiplier, adder, etc.), and the interactions of all the engaged functional modules result in the overall function of the analog computer. The analog computer is hard-wired, and a dedicated line can only carry a unique type of signal. Similarly, the brain is hard-wired and keeps different faculties separate. The pathways of vision, emotion, thought, and motor control almost never mix in the brain. Each faculty uses dedicated pathways to polymodal association areas. The richness of human faculties is accomplished by activating huge numbers of neural connections and structures in a highly organized manner. Unlike in a digital computer, the signals do not need to be coded in a central location, the microprocessor. The characteristics of brain signals largely determine which neuronal synapses are activated. Synapses that are insensitive to a specific signal remain dormant, and only synapses that are responsive to the characteristics of the signal are engaged. The brain is capable of functioning the way it does exactly because of physical neural structures and connections. These biological building elements cannot be simulated in software. They must exist in the real world. Neural chemicals alone are not enough. Any brain-like machine must consist of units that are capable of sensing, reacting to, and interacting with the world. Only neurons, which are the simplest known living elements, have these abilities. The neurobiological behaviors of neurons cannot be modeled by computers. For the same reason, functions of the mind cannot be separated from the biological body and the brain. Interactions between the physical brain, body, and external world make life and intelligence possible. There is no way to compute or mathematically model neurobiological interactions to produce pain, emotion, need, desire, curiosity, anticipation, boredom, determination, or consciousness. Computer circuits do not have the necessary physical properties to mimic the responses of living neurons. Incidentally, brain functions cannot be simulated in software. Signal detection, which is recognition of an incoming stimulus, is perhaps the most common mechanism of information processing in the brain. The activated structures perform simple functions, such as signal filtering, cognitively focused amplification, cognitive comparison, contextually coded address generation, and cross-correlation of contextually related information, along with simple and complex feedback functions. Thanks to simplicity and repetitive nature of neural functions, the brain has become cognitively powerful. In this regard, the brain is reminiscent of RISC computers, which use few operating codes in their instruction set and repeat them in various combinations. Also the DNA is coded in such a simple repetitive way. The cognitive architecture of the brain just mirrors the DNA structure. This relationship can only become apparent when the cognitive architecture and physiology of the whole brain are understood. The basic cognitive structure of the brain involves senses, memories, cognitive core, and executive functions. The cognitive core receives input from senses or from memories. Depending on the source of information, the subject can process sensory stimuli or can work with memorized events and experiences. Such regression into one's soul is important for self-reflection at the cost of ignoring the surrounding environment and the biological needs of the human organism. One of the most confusing issues in cognitive neuroscience and clinical psychology is the concept of subconscious and unconscious minds. These terms can sometimes merge into the expression unconscious when identifying both types of minds is impractical. In essence, all mental processes begin as subconscious. Senses need time to process their input, and the sensory processes occur outside human awareness. At 500 ms post-stimulus, consciousness is achieved. The conscious mind decides what to do and issues instructions to various functional systems. Their internal operations are hidden from consciousness. They represent the unconscious mind. The unconscious may interact (in fact be identical) with the subconscious and may initiate a new set of stimuli in response to the conscious instructions. Since the origin of the feedback signals cannot be always determined by the conscious mind, it is best to say that they come from the unconscious, which could be the subconscious, or the unconscious, or both. Experience has shown that the brain and the mind cannot be understood by studying just one function. This popular approach among faculty members is a waste of time, resources, and effort! Similarly, the brain cannot be understood by taking a global view and analyzing the brain from that perspective alone. Both the micro and macro views need to be applied concurrently and allowed to merge somewhere in the middle, regardless of the outcome. In the end, a unique function, such as vision, language, or dreams, should agree with the overall organization of the brain. In turn, the global organization should agree with other faculties that had not been considered during the development of the model, such as mental body scheme, reasoning, or locomotion. Unfortunately, most researchers start their work by focusing on preconceived ideas about the brain and only later consider minor changes to accommodate discrepancies between behavior and their models. As usual, controversial phenomena and confusing behavioral manifestations, such as lucidity, hypnosis, or multiple personality, are put on the back burner. Neuroscientists cannot afford to be slowed down by such "purported cognitive anomalies." They will be dealt with when the true functional model of the human brain is fully developed. That these "anomalies" hold the key to our understanding of brain architecture and functions is totally ignored. This is exactly the approach taken by the IBM and Swiss scientists. They assume that by modeling neuroanatomical connections they will be able to explain how the brain works. That the brain is a biological computing machine is given, and all effort must conform to this notion. Another detail is that the researchers are modeling and not mapping connections. This is a big difference. A model contains assumed simplifications of believed reality, but the actual synapses may differ from the model. And one more detail is that neural connections are affected by personal experience, and synapses of one brain do not necessarily mirror the microanatomy of other brains. Dak has been able to correctly explain the physiology of all major cognitive functions only because he has had a global model of the brain. Although he has uncovered as yet unknown aspects of brain physiology, most of his work covers phenomena that are well known to neuroscience. But none of them has been explained correctly. Even if one were understood, chances are good that also the cognitive architecture of the whole brain would become known. Incidentally, Dak's work brings sense to neuroscience and reveals the true physiology of vision, split brain, anosognosia, semantic dementia, memory consolidation, Wada test, Capgras delusion, and other phenomena scientists and doctors are familiar with, but do not really understand.   FUNCTIONAL DIFFERENCES   Everything a digital computer does is determined by digital information. The computer functions are expressed in a software program. The program determines what should be done. The other major ingredients of computing include information stored in memory or information obtained from computer sensors. These two sources of information (known as data) supply factual entries to the computer program. A computer activity starts by reading the program and accessing the appropriate data. The machine retrieves one word of the program (operational code) and just one word of data at a time. The script of the program is followed exactly, and variations in computer responses are achieved by testing the values of the data against predetermined limits in the computer program. The running computer seems to interact with the environment. In reality, the computer only reacts to the data it receives from the sensors or from the memory. If the data became scrambled, unrealistic, or unlikely, the computer would just process the data, and would not detect a problem. A computer might read that the outside temperature is between 30ºC and 31ºC over a 24-hour period. The values are within the programmed operating limits of the computer and do not trigger an alert. But any person would immediately sense that something is not right, because daytime and nighttime temperatures cannot be that close. One common theme when comparing the functions of computers and brains is executive speed. Not just the lay public, but also professional scientists are obsessed with speed. They are impressed by huge numbers, but ignore the practical meaning of computational processes within computers. The speed of computers indicates how many operations are performed per second. And operations are instructions written in software. Perhaps a more meaningful representation would be the number of man-years necessary to write the software. But neither the executive speed nor the years needed for software development are comparable with the operating mode of the brain. A computer may run its master clock at the speed of light, but never ever creates anything new; a computer only executes predetermined routines. By contrast, the human brain needs between 500 and 1500 ms to consciously process over 99% of all cognitive demands. Brain processes prior to 500 ms are both subconscious and unconscious. During the conscious interval of decision making, the human brain creates new concepts, approaches, and interprets the meaning of the cognitive input. New skills, synapses, and neurons are being created in an endless process of learning, and the faculties are instantly available to the living organism. With the cognitive assessment concluded, the conscious mind may instruct various executive faculties to do something. The executive faculties have their internal control mechanisms that are transparent to the conscious mind and are unconscious. Unlike a computer, the brain has no program that must be executed. The brain has vast abilities, but only some are engaged at a particular time. Everything the brain does is driven by stimuli. They come as sensory stimuli from the outside world or as internal stimuli occurring within the body and the brain. The internal stimuli can be described as the needs of the organism, and can be either biological, emotional, or mental. The brain associates every stimulus with a priority, and the highest priority is processed first. Even when several priorities arise at the same time, the brain is able to correctly identify the highest priority. Survival of the organism is normally given the top priority, and everything else has to wait. This is why going to work has higher priority in a healthy mind than going fishing. The brain also correctly sequences priorities of seemingly equal importance. Again, the well-being of the organism affects the decisions. The priorities of the tasks to be done are chosen to minimize human effort. For example, a person who has to buy food, send letters, and buy a detergent, will shop for both items at the same time, and then send the letters from the post office. A computer is unable to feel the meaning of priorities. It might buy food, then visit the post office, and then go back to the same store for the detergent. The inability of computers to feel has resulted in compensatory schemes for handling priorities. Signals reaching computers are processed based on predetermined algorithms. This is done by means of checking for the presence of signals at fixed intervals, a method known as poling. Poling treats all signals equally and processes them in the order they are detected. Another common methods of signal processing is the use of interrupts. Interrupts are responses to signals arriving on dedicated signals lines. Interrupts can be unconditional or can only occur if additional conditions are satisfied. Whichever way is chosen to process the arriving signals, the method is only a poor substitute for the ability to set priorities. Priorities of the human brain are determined in real time based on general understanding of the world, or based on specific life experiences. The decisions depend on conceptual and biographical memories. The biographical memory stores all life experiences. They can be reactivated to retrieve specific information about the past, and the information can become a current priority. For example, you walk to work when you meet an old friend. Instead of continuing walking, you change your priority and start talking with the friend. By contrast to this specific information and response, conceptual knowledge about the world interprets all stimuli and helps the organism to negotiate the current happenings. You can interact with a stranger just by relying on your conceptual memory. Everything you experience makes sense and is interpreted by your conceptual memory alone. But when the other person is someone you know from the past, your senses and conceptual memory also receive information from your biographical memory. A match between your biographical memory and the sensory input is signaled to your consciousness and gives you the feeling of recognition. Since you know the person, your interaction is more focused and involves emotional and cognitive aspects of the past. A computer has nothing that would be equivalent to the biographical memory. Not even the conceptual knowledge exists in computers. They only simulate concepts by processing data by the computer program. In fact, not the computer, but the human programmers determine how the computer responds. The possible responses are limited by the functional repertoire of the computer hardware. These differences imply that computer memory and human memory are very different entities. Human memory includes facts, relationships, knowledge, and overall experiences of the past. Computer memory only includes facts that have no connection with a broader context. For example, computers that are specifically designed for artificial intelligence often have language decoders that activate one of several preprogrammed answers. The best linguistic match is selected, and the answer is displayed or converted to sound. The computer response may appear intelligent, but the machine reacts mindlessly. There is no reasoning, just a stimulus and a programmed electronic response. The physical computer has no experience of the stimulus, of the reply, or of the broader context. Unlike the piecewise transfer of information in computers (one word at a time), brains usually deal with whole ideas and schemes. In addition, brains operate within a given context. Context focuses conscious attention in space and time. Only information from the activated context enters your awareness. Thanks to contextual focus, words that have multiple meanings are understood properly. A computer is incapable of this operating mode, because a computer cannot sense contextual associations and operate within their limits. Another major functional difference between a computer and the brain is the level of information visibility. A computer either has information and can declare it, or the computer lacks information and cannot produce it. By contrast, the human brain can have awareness that is not declarative and is not fully conscious. For example, you can catch yourself walking toward the restroom without having conscious knowledge that you need to go. At other times, you unknowingly feed yourself without realizing that you are hungry. Also sleep and wakefulness function this way. You may wake up when you want to, even though you had been asleep and unconscious. Or, you may become emotional when you catch the smell of some forgotten biographical experience. You do not know what the smell reminds you of, but you associate the stimulus with positive or negative emotion. A similar effect is produced by the tip-of-the-tongue feeling. You can almost say a word you are thinking of, but you just cannot get over the last hurdle and recall the word explicitly. Similarly, you know that you can ride a bicycle, but you would be unable to describe what you actually do when you ride. The information is in your brain, but is not accessible to your consciousness. These forces manifest the effects of the unconscious mind. It does activities without your conscious control. The conscious mind only makes a wish, and the unconscious carries it out. No computer has such an ability. The key functional differences between brains and computers are driven by the purpose of these diverse systems. Human brains have developed over a long time to support human life while interacting with the natural environment. Only functions that are necessary for survival are sufficiently developed in the human brain. This is why the brain has practically oriented faculties. The brain does not need to perform countless repetitive operations or to gather billions of statistical data as computers do. Asking the brain to do so for the purpose of comparing the brain with a computer is a senseless exercise. Only a computer is capable of finding that the Milky Way has about 100 billion stars. Identifying and counting them all is beyond human abilities. Humans are rather poor at collecting data. The night sky only has 3000 stars that are visible to the naked eye. This number is not overwhelming when considered as a total, but if someone had to count them one by one, he would undoubtedly say that the stars are countless. But this characterization of cerebral cognitive abilities does not tell the whole story. The true abilities of the brain at counting and collecting data do not show in a healthy mind. The most advanced mathematical and statistical faculties are slowed down and limited in their expression by other brain systems that need to receive, understand, and evaluate the results before acknowledging that the information has been processed. This is why as few as 3000 stars seem as too many. By contrast, brain damage can deactivate the interfacing systems and allow the modules of mathematical abilities to run at their best. Such people are known as savants, and their mathematical abilities often surpass advanced computers. Interestingly, the subjects do not know what they actually do to obtain the stunning results. The drawback of this prominence is that the subjects are unable to correctly engage in social interactions with other humans. Some people have tried to compare computers with the human brain to point out the differences between these two systems. Paradoxically, the authors have failed to grasp the fundamental problems of their effort. In most cases, the researchers have  unknowingly compared the human brain with human logic expressed through the software and hardware of a computer. In a computer, the logic and decisions are human, but the implementation of the processes is driven by a machine. This combination gives the machine high speed when executing man-implemented solutions, but the logic has the classical flaws of scholastic intelligence. An example of these traits is offered by a search engine on the internet. The search engine can find huge amounts of data, but the data are often irrelevant or of very low informative value. The search engine evaluates internet pages numerically, but is inapt to assess their true importance.  Perhaps the most striking difference between a computer and the brain shows after structural damage. We can take almost any computer apart, rebuild it with new parts, and the computer will function again. No such thing has been done with human brains. In the most successful interventions, parts of the brains have been removed, and the patients survived with no or minimal observable deficits. Because of these apparent successes, there is currently a strong push to find cures for structural brain damage, and a lot of hope has been put into stem cell research. In most cases, this is a futile enterprise. Unlike a computer or the human body, the brain often cannot be fixed. Once damage occurs, it usually remains at the same level or leads to further brain damage. This rule arises from the cognitive architecture and physiology of the brain, and applies to most neurodegenerative illnesses, such as Alzheimer's, Parkinson's, Huntington's, autism, epilepsy, and even schizophrenia. For the same reason, it is next to impossible to improve the intelligence of a person with a low IQ. By contrast to structural damage, functional deficits of the brain caused by toxins, poor nutrition, or dysregulation of neurotransmitters are usually treatable when caught early, before any neurobiological damage occurs.    MEDDLESOME SCIENTISTS   Many neuroscientists wish to combine smart machines with the brain and boost human abilities. Naturally, these ideas come from people who have inadequate understanding of intelligence and the cognitive architecture of the brain. A few crude experiments with animals have shown that output signals of the primary motor cortex can be processed by machines, and scientist already believe that this is a workable solution. Before scientists get seriously involved in this adventure, they better have a thorough understanding of the overall function of the brain. If scientists do not figure out the details of brain physiology and force their technical solutions too early, they will introduce serious problems that routinely arise in brains with damaged interfaces between neural structures. Until researchers figure out the physiology of the whole brain, motor systems, stuttering, dyslexia, epilepsy, and phantom pain, they should not consider attaching any machine to a brain. Doing so will undoubtedly cause epileptic seizures, heart failures, or symptoms of Parkinsonism. As so many times in history, also contemporary researchers are forcing technical solutions to problems they do not understand. And the patients pay the price. This state of affairs when neurosurgeons work on the brain without understanding its physiology and cognitive architecture is something really special in the human society. If you were repairing computers, airplanes, or nuclear reactors and did not know how  they function, you could be fired, imprisoned, or shot on the spot. But as a neurosurgeon, you can cut out the cingulate cortex, temporal lobe, hippocampus, pallidum, hypothalamus, amygdala, prefrontal lobe, corpus callosum, or some other part of the brain without the slightest possibility that you will be fired by your employer or held legally accountable by the state. This status of invulnerability allows many a brain researcher to mutilate and torture animals in pursuit of his goals. The attempt to combine the brain with an electronic machine is just a reflection of this socially accepted pathology. Science fiction is nice in movies and literature, but has no place in medicine. Even if it were possible to develop a "thinking machine" (which is not), interfacing it to human cognitive processes would be very tricky, if not impossible. Practically every neural structure has feedback loops to other parts of the brain, and interference with the feedback loops can produce countless unpleasant surprises. This phenomenon is prominent in the functions of the basal ganglia. The interactions are seemingly unfathomable and defy all expectations. In addition, there is no single place in the brain where a smart machine could be attached. Just the motor cortex must properly interface the pyramidal and extrapyramidal motor systems, premotor systems, sensory systems, control systems, cognitive systems, and memory systems. The neural structures of these systems are spread throughout the brain. Interfacing all the essential neural structures would result in a gross invasion into the brain. The likelihood of killing the patient would be practically guaranteed. Interfacing just one or two brain structures to machines would not be enough. The human brain has complex timing characteristics, and many interactions need to be satisfied to ensure normal brain functions. As clinical examples manifest, violation of cerebral processes in patients with brain injuries usually leads to auditory or visual hallucinations, involuntary muscle activations, phantom pain, alien hand, and other erratic experiences. Even a simple combination of a "walking machine" and the primary motor cortex of a paralyzed patient could have disastrous consequences. The intelligent machine might attempt to move in certain ways under the control of the pyramidal motor system, and the extrapyramidal motor system might be engaged in different kinds of movements. The resulting pain might kill the patient.       THE THINKING MACHINE   The goal of many computer engineers and scientists is development of artificial intelligence, that is a thinking machine that would be just as smart and even smarter than the human brain is. Pursuit of the goal is a noble undertaking, but meets a few obstacles. The biggest one is that scientists do not fully understand what intelligence is. Other problems arise from programmed behaviors. Humans exhibit many programmed behaviors in the form of habits. We greet each other, shake hands, and habitually engage in social conversations. We do these "illogical" activities spontaneously. In addition to habits, we can apply creative intelligence and alter our habitual reactions. Computers do not have this luxury; they only respond in programmed ways. A computer is unable to spontaneously become cognitively proactive beyond the triggers and routines designed by programmers. The designers and programmers, and not the computer, determine the abilities and functions of the machine. The computer responses are only as good as the software programs written by humans are. The discrepancy between intelligence and programmed behaviors is apparent when we communicate with computers. We would like a smart machine to be truthful and to provide correct answers to questions. But true intelligence also involves emotion and emotional intelligence. Being truthful may not be intelligent when your life is at stake or when you are trying to fool someone to gain an advantage. Similarly, interpretation of questions without employing emotional intelligence may not produce the desired results. If a question includes irony, the meaning can be the opposite than what the machine decodes. Similarly, when the question is "Did someone ring the door bell?", a machine without emotional intelligence may answer "No," even though someone knocked on the door or called the resident's name. A machine is unable to volunteer a relevant information that is not requested verbatim. The poor responses of the machine are given by the decoding mechanisms of the question. The content of the question is assessed by software routines, and a meaning of the question is "guessed" by the software. Then the software produces a statistically likely response by combining the words of the answer into a grammatically correct reply. The machine has no "idea" whether or not the answer is correct. The computer just processes programs, but is unable to engage emotional intelligence and critically assess the correctness of the answer. This inability arises from the lack of experiencing of the world by the computer. By contrast, a human subject may express hesitation or uncertainty when reporting some poorly remembered information. This never happens to a computer. If an information is available, it is reported with absolute certainty as a known fact. Humans can easily answer almost any question thanks to engaging emotional intelligence and employing experience they acquire in real life. A machine could not properly respond to figurative speech or idiomatic expressions, such as They took him to the cleaners. Also the common expression How are you doing? might be misunderstood by a computer. In all likelihood, the computer would reply, "How am I doing what?" To counter problems like these, common human expressions could be translated into machine operations that would simulate human responses. The computer might be programmed to automatically respond, "Thank you, I am doing fine", but would not understand the purpose of the exchange. The very act of "understanding" means something different in a machine than it means in man. When people understand an expression, they also experience (feel) its meaning based on previous encounters with the expression within a specific past context. This ability makes good lyrics, poetry, and prose appealing to people. A machine cannot "feel" the meaning of a word and cannot associate it with a previous contextual experience through internal electronic excitation. A machine cannot have an experience and cannot learn. The denial of learning by machines sounds like heresy. There are research teams of Ph.D.'s who have built their careers around the concept that machines can learn. But from the human viewpoint, learning is only possible when an experience has a meaning. A machine does not care what happens. Data are just numbers, and a machine feels the same (that is nothing), no matter what data are processed. The words "guy" and "gentleman" appear the same to a computer. A computer has no ability to feel the emotional difference and choose the appropriate expression within the overall context. Furthermore, a computer does not know that it has learned something. A computer has no awareness of what information has been acquired and to what extent. Humans naturally employ their meta memory (awareness of what they know) and other mechanisms when they decide whether or not they know something and how well they know it. This awareness or lack thereof is not associated with conscious retrieval of the topic. Human beings can assess the extent of their knowledge without consciously retrieving the specifics of an information. By contrast, a computer has no ability to determine whether or not some data are available until the data are found. And since most computers only use a one-memory system, computers cannot confirm that the data are correct. The data are considered correct simply because they are found. If that mechanism alone were employed in human minds, then the memories of dreams or movies would be considered reality. The above characterization does not mean that all forms of information acquisition are beyond the abilities of machines. If a machine were equipped with tactile sensors, the machine could move about and "learn" the layout of the surrounding environment by trial and error. The "learning" would be equivalent to spatial exploration in accordance with programmed instructions. The machine would likely be unable to distinguish between minor and gross mistakes, and unexpected obstacles or challenges could not be handled well. A machine lacking emotionally meaningful communication between its sensors and its experience stored in memory would not understand which maneuvers are permissible and which will lead to breakdown and self-destruction. Children learn these issues early in life as they explore their environment, but doing the same with machines is next to impossible. They would have to have operational limits specified in software to avoid doing things that might be dangerous. But real-life possibilities are countless. There would always be issues the designers never thought about. Similarly, the imposed restrictions might be too restrictive, and the machine might not attempt to perform functions that are doable. For example, touching a wall and hitting it at 10 mph might result in the same software reactions. Sure, the machine could be equipped with high-dynamic-range sensors that would sense various levels of impacts, but the machine would still feel nothing. Only the software would respond to the impacts with corrective measures. If the impacts occurred outside a sensor or destroyed the sensor, the machine would not register the impacts. In humans, damage to a sensor is detected by other sensors, and they inform the brain about the injury. These scenarios point out the crucial role of neural networks in the human body. The body and its sensors must be integrated with the mind to ensure the survival of the living organism. Although sensing of the status of the body of a machine might be technologically achievable one day, an insurmountable challenge exists in the "electronic brain", where the sensory information has to be interpreted. It has been widely reported that the human brain has no sensors of pain, but this characterization is too simplistic. Although the brain cannot sense external pain applied directly to its neural structures, the brain is needed to interpret signals of sensors. The human organism has perceptual neurons in the body and also in the brain. The two neuronal populations perceive, understand, and affect each other. Reactivity of the cerebral neurons allows you to experience pain, disgust, emotion, or consciousness. Clusters of brain neurons collect stimuli from the peripheral sensors and obtain a global picture about the state of the human body or the external world. In turn, the brain neurons affect the peripheral sensors, and act through them on other cells that constitute the human body. This activity is known as biofeedback. Both the peripheral sensors and the brain neurons can feel and experience the meaning of the processed information. This communication mode is not possible in machines. Machines can read the sensors, but the "electronic brain" feels nothing. The readings are processed in software (or by hardwired electronics) and have no impact on the physical state or "feeling" of the electronic circuits. The ability to feel and experience is what leads to sensible learning, intelligent choices, and free will in humans. Some human faculties have become so sensitive and specialized that the environment can be experienced through looks, language, sensory images, or mental imagination of various scenarios. Because of this dependency on living neurons and their responses, computational approaches cannot satisfactorily simulate functions of the human brain. Loss of meaningful contact with the external and internal physical worlds affects knowledge, intelligence, and relationship with oneself. Lack of feeling and perceptual knowledge results in the inability to distinguish the surrounding world from one's body. This deficit is known from patients with left-side paralysis that is accompanied by anosognosia. The subjects cannot recognize their left sides as parts of selves and produce confabulated answers about their bodies and physical abilities. A similar problem exists in patients with neglect. They can see in their neglected hemifields, but ignore the sensory messages. The cognitively unattended information has no significance in the brain and is ignored. Similarly, an intelligent machine that has no contact with its body might collect sensory information, but would fail to respond to the data, because they would have no practical meaning. A locomotive might be coming at the smart machine at 80 mph, and the machine would only "pay attention" to the exact position and speed of the locomotive. No idea would enter the "electronic mind" of the seemingly intelligent machine that it is 20 milliseconds from being obliterated. The clever machine would fail to get out of the way because the machine has no understanding of pain and death. Another major problem with "intelligent machines" is how they are created. They are designed and produced by a centralized process. Everything the machine is comes from the design team. The software routines are programmed into the computer, but are not acquired by the machine in a process of learning. There are countless permutations that need to be accounted for to simulate real life, and countless interactive networks within the machine have to be implemented. The centralized design process can never be perfect in scope and quality. By contrast, a living organism develops in a distributed fashion. The conscious mind does not need to think about everything that is going on in the body and brain. Neurons interact locally and respond to problems locally. The brain only acts as a coordinator of strategies and higher functions. The brain does not tell neurons when to create synapses, store memories, or learn from experience. These details are spontaneously determined by the involved neurons by way of interaction with other neurons, sensors, or the social and physical environments. There are too many things going on in a living organism, and trying to account for them all by the programmer would be utterly foolish. Naturally, trying to design all such interactions into a machine is totally hopeless. Even people are consciously unaware of all their skills, knowledge, and experiences. Attempts to program such hidden information into machines are guaranteed to be unsuccessful. Despite the striking imperfections of computers, scientists commonly use the expression "electronic brain" and imply that a thinking machine can be electronic. No thinking entity can ever be made of solid-state devices. The characteristics of living organisms hint that functional artificial intelligence can never be created in machines. A machine could be made to solve very complex numerical problems in the spirit of artificial intelligence, but the machine would have to be supervised by emotional intelligence. Lack of supervision would produce wrong or irrational solutions to problems, and would almost certainly lead to self-destruction of the "smart machine" or might cause harm to people. Solid-state machines that do not interact with the environment and do not feel its impact on the machines can never develop emotional intelligence. The only way to produce emotional intelligence (and also creative scholastic intelligence) is through highly responsive organic compounds. Very high organization and complex interactions of such compounds result in living cells that allow formation of more advanced multicell organisms. The characteristics of life indicate that the barrier between a dumb machine and a smart life form can never be overcome. Machines are destined to remain dumb. In recent years, neuroscientists have recognized the inherent limitations of electronic computers and have been trying to produce organic elements that could be usable in cybernetics. An example is the pursuit of an organic transistor. The transistor would supposedly have richer functional and logical repertoire than its silicon cousins have. The hope is that by applying such organic building blocks, intelligent machines can be produced. But will it work? The fundamental principle of intelligence demand that a biological element interact with the environment. This interaction is internally dependent on the element and is not a function of some externally applied instructions. What we call intelligence is mutual interactions of living cells. The physiology and biological needs of cells are the forces that produce interactions between neurons and lead to intelligence. An input from an external element that has no interaction with other cells and is not part of the whole system cannot induce intelligent, logical, beneficial, or desirable responses. This deficit can be seen in mental patients of all types. Giving such patients a sound advice is of no benefit if the patients are unable to evaluate the advice and recognize it as useful and beneficial. Naturally, organic computing elements are not living entities and are not comparable to neurons. The devices may be using organic compounds, but essentially behave like machines and only respond to programmed functions. Also in this case, scientists attempt to program intelligence through centralized design, but do not allow a living organism to become intelligent by virtue of its internal experiences, needs, and responses. An interesting aspect of neuroscience is that true intelligence represents life and a living being. If we create a biological "machine" with "artificial intelligence", are we able to deny it basic rights that all other intelligent life forms enjoy? Or do we treat the biological unit as a slave who has to serve humans? If we accept this ideology, we can create intelligent machines right now by destroying the neural circuits of emotional intelligence. The remaining brain functions will represent the purest artificial intelligence one can get.  As the discussion hints, the unique properties of the brain cannot be duplicated by any other means but by life itself. A living cell with the ability to sense the environment and react to it is the basic building block of all higher organisms. Some cells build the body; some process information in real time; some store memories, and some facilitate metabolism. Although all cells are living individuals, they specialize and, similarly as social insects, fulfill unique functions within hybrid biological systems. Interactions of many neurons result in higher thought. There is no self-awareness at the level of a single neuron; the responses are "instinctive." Higher cognitive functions (emotion, self-awareness, and consciousness) only emerge as a product of interactions between huge numbers of neurons. These principles are fundamental not only to life, but also to the existence of intelligence. An intelligent machine would have to be functionally structured the same way as the brain is. In reality, machines only mimic the overall behavioral manifestations of life, but lack the essential internal relationships that define life, feeling, emotion, intelligence, and consciousness.       REFERENCES   [1] Jeff Prideaux. Comparison between Karl Pribram's "Holographic Brain Theory" and more conventional models of neuronal computation. Retrieved November 1, 2008 from http://www.acsa2000.net/bcngroup/jponkp/ [2] A Working Brain Model. Source: Complexity Digest 2007.46, 29-Nov-2007. Retrieved November 2, 2008 from http://www.comdig.org/index.php?id_issue=2007.46 [3] The Reptilian Brain and the Triune Brain Model (1/7). Retrieved November 2, 2008 from http://www.eruptingmind.com/reptilian-brain-triune-model/ [4] The Four Quadrant Model of the Brain, Ned Herrmann's Whole Brain Model. Retrieved November 1, 2008 from http://www.kheper.net/topics/intelligence/Herrmann.htm [5] William Witherspoon (1999-2004). String Theory and the Human Mind. Retrieved November 2, 2008 from http://www.wwitherspoon.org/StringTheory.htm [6] Bernhard Mitterauer & Kristen Kopp. The self-composing brain: Towards a glial–neuronal brain theory. Brain and Cognition, Volume 51, Issue 3, April 2003, Pages 357-367 [7] The Split Brain Experiments. Retrieved November 2, 2008 from http://nobelprize.org/educational_games/medicine/split-brain/background.html [8] Francis Crick & Christof Koch. Consciousness and Neuroscience. Cerebral Cortex, 8:97-107, 1998 [9] Ignacio E. Ochoa Pacheco (1996). The Holographic Model of Brain Function and the Orgone Energy Fields Theory. Retrieved February 21, 2009 from http://www.orgone.org/articles/ax7ignc1.htm [10] Yuste Rafael. Circuit Neuroscience: the road ahead. Frontiers in Neuroscience, July 2008, Volume 2, Issue 1, Pages 6-9.
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