Part 6 (1/2)

CHAPTER FOUR.

Achieving the Software of Human Intelligence: How to Reverse Engineer the Human Brain

There are good reasons to believe that we are at a turning point, and that it will be possible within the next two decades to formulate a meaningful understanding of brain function. This optimistic view is based on several measurable trends, and a simple observation which has been proven repeatedly in the history of science: Scientific advances are enabled by a technology advance that allows us to see what we have not been able to see before. Scientific advances are enabled by a technology advance that allows us to see what we have not been able to see before. At about the turn of the twenty-first century, we pa.s.sed a detectable turning point in both neuroscience knowledge and computing power. For the first time in history, we collectively know enough about our own brains, and have developed such advanced computing technology, that we can now seriously undertake the construction of a verifiable, real-time, high-resolution model of significant parts of our intelligence. At about the turn of the twenty-first century, we pa.s.sed a detectable turning point in both neuroscience knowledge and computing power. For the first time in history, we collectively know enough about our own brains, and have developed such advanced computing technology, that we can now seriously undertake the construction of a verifiable, real-time, high-resolution model of significant parts of our intelligence.-LLOYD WATTS, NEUROSCIENTIST1 Now, for the first time, we are observing the brain at work in a global manner with such clarity that we should be able to discover the overall programs behind its magnificent powers.-J. G. TAYLOR, B. HORWITZ, K. J. FRISTON, NEUROSCIENTISTS2 The brain is good: it is an existence proof that a certain arrangement of matter can produce mind, perform intelligent reasoning, pattern recognition, learning and a lot of other important tasks of engineering interest. Hence we can learn to build new systems by borrowing ideas from the brain....The brain is bad: it is an evolved, messy system where a lot of interactions happen because of evolutionary contingencies. ... On the other hand, it must also be robust (since we can survive with it) and be able to stand fairly major variations and environmental insults, so the truly valuable insight from the brain might be how to create resilient complex systems that self-organize well....The interactions within a neuron are complex, but on the next level neurons seem to be somewhat simple objects that can be put together flexibly into networks. The cortical networks are a real mess locally, but again on the next level the connectivity isn't that complex. It would be likely that evolution has produced a number of modules or repeating themes that are being re-used, and when we understand them and their interactions we can do something similar.-ANDERS SANDBERG, COMPUTATIONAL NEUROSCIENTIST, ROYAL INSt.i.tUTE OF TECHNOLOGY, SWEDEN

Reverse Engineering the Brain: An Overview of the Task

The combination of human-level intelligence with a computer's inherent superiority in speed, accuracy, and memory-sharing ability will be formidable. To date, however, most AI research and development has utilized engineering methods that are not necessarily based on how the human brain functions, for the simple reason that we have not had the precise tools needed to develop detailed models of human cognition.

Our ability to reverse engineer the brain-to see inside, model it, and simulate its regions-is growing exponentially. We will ultimately understand the principles of operation underlying the full range of our own thinking, knowledge that will provide us with powerful procedures for developing the software of intelligent machines. We will modify, refine, and extend these techniques as we apply them to computational technologies that are far more powerful than the electrochemical processing that takes place in biological neurons. A key benefit of this grand project will be the precise insights it offers into ourselves. We will also gain powerful new ways to treat neurological problems such as Alzheimer's, stroke, Parkinson's disease, and sensory disabilities, and ultimately will be able to vastly extend our intelligence.

New Brain-Imaging and Modeling Tools. The first step in reverse engineering the brain is to peer into the brain to determine how it works. So far, our tools for doing this have been crude, but that is now changing, as a significant number of new scanning technologies feature greatly improved spatial and temporal resolution, price-performance, and bandwidth. Simultaneously we are rapidly acc.u.mulating data on the precise characteristics and dynamics of the const.i.tuent parts and systems of the brain, ranging from individual synapses to large regions such as the cerebellum, which comprises more than half of the brain's neurons. Extensive databases are methodically cataloging our exponentially growing knowledge of the brain. The first step in reverse engineering the brain is to peer into the brain to determine how it works. So far, our tools for doing this have been crude, but that is now changing, as a significant number of new scanning technologies feature greatly improved spatial and temporal resolution, price-performance, and bandwidth. Simultaneously we are rapidly acc.u.mulating data on the precise characteristics and dynamics of the const.i.tuent parts and systems of the brain, ranging from individual synapses to large regions such as the cerebellum, which comprises more than half of the brain's neurons. Extensive databases are methodically cataloging our exponentially growing knowledge of the brain.3 Researchers have also shown they can rapidly understand and apply this information by building models and working simulations. These simulations of brain regions are based on the mathematical principles of complexity theory and chaotic computing and are already providing results that closely match experiments performed on actual human and animal brains.

As noted in chapter 2, the power of the scanning and computational tools needed for the task of reverse engineering the brain is accelerating, similar to the acceleration in technology that made the genome project feasible. When we get to the nan.o.bot era (see ”Scanning Using Nan.o.bots” on p. 163), we will be able to scan from inside inside the brain with exquisitely high spatial and temporal resolution. the brain with exquisitely high spatial and temporal resolution.4 There are no inherent barriers to our being able to reverse engineer the operating principles of human intelligence and replicate these capabilities in the more powerful computational substrates that will become available in the decades ahead. The human brain is a complex hierarchy of complex systems, but it does not represent a level of complexity beyond what we are already capable of handling. There are no inherent barriers to our being able to reverse engineer the operating principles of human intelligence and replicate these capabilities in the more powerful computational substrates that will become available in the decades ahead. The human brain is a complex hierarchy of complex systems, but it does not represent a level of complexity beyond what we are already capable of handling.

The Software of the Brain. The price-performance of computation and communication is doubling every year. As we saw earlier, the computational capacity needed to emulate human intelligence will be available in less than two decades. The price-performance of computation and communication is doubling every year. As we saw earlier, the computational capacity needed to emulate human intelligence will be available in less than two decades.5 A princ.i.p.al a.s.sumption underlying the expectation of the Singularity is that nonbiological mediums will be able to emulate the richness, subtlety, and depth of human thinking. But achieving the hardware computational capacity of a single human brain-or even of the collective intelligence of villages and nations-will not automatically produce human levels of capability. (By ”human levels” I include all the diverse and subtle ways humans are intelligent, including musical and artistic apt.i.tude, creativity, physical motion through the world, and understanding and responding appropriately to emotions.) The hardware computational capacity is necessary but not sufficient. Understanding the organization and content of these resources-the software of intelligence-is even more critical and is the objective of the brain reverse-engineering undertaking. A princ.i.p.al a.s.sumption underlying the expectation of the Singularity is that nonbiological mediums will be able to emulate the richness, subtlety, and depth of human thinking. But achieving the hardware computational capacity of a single human brain-or even of the collective intelligence of villages and nations-will not automatically produce human levels of capability. (By ”human levels” I include all the diverse and subtle ways humans are intelligent, including musical and artistic apt.i.tude, creativity, physical motion through the world, and understanding and responding appropriately to emotions.) The hardware computational capacity is necessary but not sufficient. Understanding the organization and content of these resources-the software of intelligence-is even more critical and is the objective of the brain reverse-engineering undertaking.

Once a computer achieves a human level of intelligence, it will necessarily soar past it. A key advantage of nonbiological intelligence is that machines can easily share their knowledge. If you learn French or read War and Peace War and Peace, you can't readily download that learning to me, as I have to acquire that scholars.h.i.+p the same painstaking way that you did. I can't (yet) quickly access or transmit your knowledge, which is embedded in a vast pattern of neurotransmitter concentrations (levels of chemicals in the synapses that allow one neuron to influence another) and interneuronal connections (portions of the neurons called axons and dendrites that connect neurons).

But consider the case of a machine's intelligence. At one of my companies, we spent years teaching one research computer how to recognize continuous human speech, using pattern-recognition software.6 We exposed it to thousands of hours of recorded speech, corrected its errors, and patiently improved its performance by training its ”chaotic” self-organizing algorithms (methods that modify their own rules, based on processes that use semirandom initial information, and with results that are not fully predictable). Finally, the computer became quite adept at recognizing speech. Now, if you want your own personal computer to recognize speech, you don't have to put it through the same painstaking learning process (as we do with each human child); you can simply download the already established patterns in seconds. We exposed it to thousands of hours of recorded speech, corrected its errors, and patiently improved its performance by training its ”chaotic” self-organizing algorithms (methods that modify their own rules, based on processes that use semirandom initial information, and with results that are not fully predictable). Finally, the computer became quite adept at recognizing speech. Now, if you want your own personal computer to recognize speech, you don't have to put it through the same painstaking learning process (as we do with each human child); you can simply download the already established patterns in seconds.

a.n.a.lytic Versus Neuromorphic Modeling of the Brain. A good example of the divergence between human intelligence and contemporary AI is how each undertakes the solution of a chess problem. Humans do so by recognizing patterns, while machines build huge logical ”trees” of possible moves and countermoves. Most technology (of all kinds) to date has used this latter type of ”top-down,” a.n.a.lytic, engineering approach. Our flying machines, for example, do not attempt to re-create the physiology and mechanics of birds. But as our tools for reverse engineering the ways of nature are growing rapidly in sophistication, technology is moving toward emulating nature while implementing these techniques in far more capable substrates. A good example of the divergence between human intelligence and contemporary AI is how each undertakes the solution of a chess problem. Humans do so by recognizing patterns, while machines build huge logical ”trees” of possible moves and countermoves. Most technology (of all kinds) to date has used this latter type of ”top-down,” a.n.a.lytic, engineering approach. Our flying machines, for example, do not attempt to re-create the physiology and mechanics of birds. But as our tools for reverse engineering the ways of nature are growing rapidly in sophistication, technology is moving toward emulating nature while implementing these techniques in far more capable substrates.

The most compelling scenario for mastering the software of intelligence is to tap directly into the blueprint of the best example we can get our hands on of an intelligent process: the human brain. Although it took its original ”designer” (evolution) several billion years to develop the brain, it's readily available to us, protected by a skull but with the right tools not hidden from our view. Its contents are not yet copyrighted or patented. (We can, however, expect that to change; patent applications have already been filed based on brain reverse engineering.)7 We will apply the thousands of trillions of bytes of information derived from brain scans and neural models at many levels to design more intelligent parallel algorithms for our machines, particularly those based on self-organizing paradigms. We will apply the thousands of trillions of bytes of information derived from brain scans and neural models at many levels to design more intelligent parallel algorithms for our machines, particularly those based on self-organizing paradigms.

With this self-organizing approach, we don't have to attempt to replicate every single neural connection. There is a great deal of repet.i.tion and redundancy within any particular brain region. We are discovering that higher-level models of brain regions are often simpler than the detailed models of their neuronal components.

How Complex Is the Brain? Although the information contained in a human brain would require on the order of one billion billion bits (see chapter 3), the initial design of the brain is based on the rather compact human genome. The entire genome consists of eight hundred million bytes, but most of it is redundant, leaving only about thirty to one hundred million bytes (less than 10 Although the information contained in a human brain would require on the order of one billion billion bits (see chapter 3), the initial design of the brain is based on the rather compact human genome. The entire genome consists of eight hundred million bytes, but most of it is redundant, leaving only about thirty to one hundred million bytes (less than 109 bits) of unique information (after compression), which is smaller than the program for Microsoft Word. bits) of unique information (after compression), which is smaller than the program for Microsoft Word.8 To be fair, we should also take into account ”epigenetic” data, which is information stored in proteins that control gene expression (that is, that determine which genes are allowed to create proteins in each cell), as well as the entire protein-replication machinery, such as the ribosomes and a host of enzymes. However, such additional information does not significantly change the order of magnitude of this calculation. To be fair, we should also take into account ”epigenetic” data, which is information stored in proteins that control gene expression (that is, that determine which genes are allowed to create proteins in each cell), as well as the entire protein-replication machinery, such as the ribosomes and a host of enzymes. However, such additional information does not significantly change the order of magnitude of this calculation.9 Slightly more than half of the genetic and epigenetic information characterizes the initial state of the human brain. Slightly more than half of the genetic and epigenetic information characterizes the initial state of the human brain.

Of course, the complexity of our brains greatly increases as we interact with the world (by a factor of about one billion over the genome).10 But highly repet.i.tive patterns are found in each specific brain region, so it is not necessary to capture each particular detail to successfully reverse engineer the relevant algorithms, which combine digital and a.n.a.log methods (for example, the firing of a neuron can be considered a digital event whereas neurotransmitter levels in the synapse can be considered a.n.a.log values). The basic wiring pattern of the cerebellum, for example, is described in the genome only once but repeated billions of times. With the information from brain scanning and modeling studies, we can design simulated ”neuromorphic” equivalent software (that is, algorithms functionally equivalent to the overall performance of a brain region). But highly repet.i.tive patterns are found in each specific brain region, so it is not necessary to capture each particular detail to successfully reverse engineer the relevant algorithms, which combine digital and a.n.a.log methods (for example, the firing of a neuron can be considered a digital event whereas neurotransmitter levels in the synapse can be considered a.n.a.log values). The basic wiring pattern of the cerebellum, for example, is described in the genome only once but repeated billions of times. With the information from brain scanning and modeling studies, we can design simulated ”neuromorphic” equivalent software (that is, algorithms functionally equivalent to the overall performance of a brain region).

The pace of building working models and simulations is only slightly behind the availability of brain-scanning and neuron-structure information. There are more than fifty thousand neuroscientists in the world, writing articles for more than three hundred journals.11 The field is broad and diverse, with scientists and engineers creating new scanning and sensing technologies and developing models and theories at many levels. So even people in the field are often not completely aware of the full dimensions of contemporary research. The field is broad and diverse, with scientists and engineers creating new scanning and sensing technologies and developing models and theories at many levels. So even people in the field are often not completely aware of the full dimensions of contemporary research.

Modeling the Brain. In contemporary neuroscience, models and simulations are being developed from diverse sources, including brain scans, interneuronal connection models, neuronal models, and psychophysical testing. As mentioned earlier, auditory-system researcher Lloyd Watts has developed a comprehensive model of a significant portion of the human auditory-processing system from neurobiology studies of specific neuron types and interneuronal-connection information. Watts's model includes five parallel paths and the actual representations of auditory information at each stage of neural processing. Watts has implemented his model in a computer as real-time software that can locate and identify sounds and functions, similar to the way human hearing operates. Although a work in progress, the model ill.u.s.trates the feasibility of converting neurobiological models and brain-connection data into working simulations. In contemporary neuroscience, models and simulations are being developed from diverse sources, including brain scans, interneuronal connection models, neuronal models, and psychophysical testing. As mentioned earlier, auditory-system researcher Lloyd Watts has developed a comprehensive model of a significant portion of the human auditory-processing system from neurobiology studies of specific neuron types and interneuronal-connection information. Watts's model includes five parallel paths and the actual representations of auditory information at each stage of neural processing. Watts has implemented his model in a computer as real-time software that can locate and identify sounds and functions, similar to the way human hearing operates. Although a work in progress, the model ill.u.s.trates the feasibility of converting neurobiological models and brain-connection data into working simulations.

As Hans Moravec and others have speculated, these efficient functional simulations require about one thousand times less computation than would be required if we simulated the nonlinearities in each dendrite, synapse, and other subneural structure in the region being simulated. (As I discussed in chapter 3, we can estimate the computation required for functional simulation of the brain at 1016 calculations per second [cps], versus 10 calculations per second [cps], versus 1019 cps to simulate the subneural nonlinearities.) cps to simulate the subneural nonlinearities.)12 The actual speed ratio between contemporary electronics and the electrochemical signaling in biological interneuronal connections is at least one million to one. We find this same inefficiency in all aspects of our biology, because biological evolution built all of its mechanisms and systems with a severely constrained set of materials: namely, cells, which are themselves made from a limited set of proteins. Although biological proteins are three-dimensional, they are restricted to complex molecules that can be folded from a linear (one-dimensional) sequence of amino acids.

Peeling the Onion. The brain is not a single information-processing organ but rather an intricate and intertwined collection of hundreds of specialized regions. The process of ”peeling the onion” to understand the functions of these interleaved regions is well under way. As the requisite neuron descriptions and brain-interconnection data become available, detailed and implementable replicas such as the simulation of the auditory regions described below (see ”Another Example: Watts's Model of the Auditory Regions” on p. 183) will be developed for all brain regions. The brain is not a single information-processing organ but rather an intricate and intertwined collection of hundreds of specialized regions. The process of ”peeling the onion” to understand the functions of these interleaved regions is well under way. As the requisite neuron descriptions and brain-interconnection data become available, detailed and implementable replicas such as the simulation of the auditory regions described below (see ”Another Example: Watts's Model of the Auditory Regions” on p. 183) will be developed for all brain regions.

Most brain-modeling algorithms are not the sequential, logical methods that are commonly used in digital computing today. The brain tends to use self-organizing, chaotic, holographic processes (that is, information not located in one place but distributed throughout a region). It is also ma.s.sively parallel and utilizes hybrid digital-controlled a.n.a.log techniques. However, a wide range of projects has demonstrated our ability to understand these techniques and to extract them from our rapidly escalating knowledge of the brain and its organization.

After the algorithms of a particular region are understood, they can be refined and extended before being implemented in synthetic neural equivalents. They can be run on a computational substrate that is already far faster than neural circuitry. (Current computers perform computations in billionths of a second, compared to thousandths of a second for interneuronal transactions.) And we can also make use of the methods for building intelligent machines that we already understand.

Is the Human Brain Different from a Computer?

The answer to this question depends on what we mean by the word ”computer.” Most computers today are all digital and perform one (or perhaps a few) computations at a time at extremely high speed. In contrast, the human brain combines digital and a.n.a.log methods but performs most computations in the a.n.a.log (continuous) domain, using neurotransmitters and related mechanisms. Although these neurons execute calculations at extremely slow speeds (typically two hundred transactions per second), the brain as a whole is ma.s.sively parallel: most of its neurons work at the same time, resulting in up to one hundred trillion computations being carried out simultaneously.

The ma.s.sive parallelism of the human brain is the key to its pattern-recognition ability, which is one of the pillars of our species' thinking. Mammalian neurons engage in a chaotic dance (that is, with many apparently random interactions), and if the neural network has learned its lessons well, a stable pattern will emerge, reflecting the network's decision. At the present, parallel designs for computers are somewhat limited. But there is no reason why functionally equivalent nonbiological re-creations of biological neural networks cannot be built using these principles. Indeed, dozens of efforts around the world have already succeeded in doing so. My own technical field is pattern recognition, and the projects that I have been involved in for about forty years use this form of trainable and nondeterministic computing.

Many of the brain's characteristic methods of organization can also be effectively simulated using conventional computing of sufficient power. Duplicating the design paradigms of nature will, I believe, be a key trend in future computing. We should keep in mind, as well, that digital computing can be functionally equivalent to a.n.a.log computing-that is, we can perform all of the functions of a hybrid digital-a.n.a.log network with an all-digital computer. The reverse is not true: we can't simulate all of the functions of a digital computer with an a.n.a.log one.

However, a.n.a.log computing does have an engineering advantage: it is potentially thousands of times more efficient. An a.n.a.log computation can be performed by a few transistors or, in the case of mammalian neurons, specific electrochemical processes. A digital computation, in contrast, requires thousands or tens of thousands of transistors. On the other hand, this advantage can be offset by the ease of programming (and modifying) digital computer-based simulations.

There are a number of other key ways in which the brain differs from a conventional computer:

The brain's circuits are very slow. Synaptic-reset and neuron-stabilization times (the amount of time required for a neuron and its synapses to reset themselves after the neuron fires) are so slow that there are very few neuron-firing cycles available to make pattern-recognition decisions. Functional magnetic-resonance imaging (fMRI) and magnetoencephalography (MEG) scans show that judgments that do not require resolving ambiguities appear to be made in a single neuron-firing cycle (less than twenty milliseconds), involving essentially no iterative (repeated) processes. Recognition of objects occurs in about 150 milliseconds, so that even if we ”think something over,” the number of cycles of operation is measured in hundreds or thousands at most, not billions, as with a typical computer. Synaptic-reset and neuron-stabilization times (the amount of time required for a neuron and its synapses to reset themselves after the neuron fires) are so slow that there are very few neuron-firing cycles available to make pattern-recognition decisions. Functional magnetic-resonance imaging (fMRI) and magnetoencephalography (MEG) scans show that judgments that do not require resolving ambiguities appear to be made in a single neuron-firing cycle (less than twenty milliseconds), involving essentially no iterative (repeated) processes. Recognition of objects occurs in about 150 milliseconds, so that even if we ”think something over,” the number of cycles of operation is measured in hundreds or thousands at most, not billions, as with a typical computer.But it's ma.s.sively parallel. The brain has on the order of one hundred trillion interneuronal connections, each potentially processing information simultaneously. These two factors (slow cycle time and ma.s.sive parallelism) result in a certain level of computational capacity for the brain, as we discussed earlier. The brain has on the order of one hundred trillion interneuronal connections, each potentially processing information simultaneously. These two factors (slow cycle time and ma.s.sive parallelism) result in a certain level of computational capacity for the brain, as we discussed earlier.Today our largest supercomputers are approaching this range. The leading supercomputers (including those used by the most popular search engines) measure over 1014 cps, which matches the lower range of the estimates I discussed in chapter 3 for functional simulation. It is not necessary, however, to use the same granularity of parallel processing as the brain itself so long as we match the overall computational speed and memory capacity needed and otherwise simulate the brain's ma.s.sively parallel architecture. cps, which matches the lower range of the estimates I discussed in chapter 3 for functional simulation. It is not necessary, however, to use the same granularity of parallel processing as the brain itself so long as we match the overall computational speed and memory capacity needed and otherwise simulate the brain's ma.s.sively parallel architecture.The brain combines a.n.a.log and digital phenomena. The topology of connections in the brain is essentially digital-a connection exists, or it doesn't. An axon firing is not entirely digital but closely approximates a digital process. Most every function in the brain is a.n.a.log and is filled with nonlinearities (sudden s.h.i.+fts in output, rather than levels changing smoothly) that are substantially more complex than the cla.s.sical model that we have been using for neurons. However, the detailed, nonlinear dynamics of a neuron and all of its const.i.tuents (dendrites, spines, channels, and axons) can be modeled through the mathematics of nonlinear systems. These mathematical models can then be simulated on a digital computer to any desired degree of accuracy. As I mentioned, if we simulate the neural regions using transistors in their native a.n.a.log mode rather than through digital computation, this approach can provide improved capacity by three or four orders of magnitude, as Carver Mead has demonstrated. The topology of connections in the brain is essentially digital-a connection exists, or it doesn't. An axon firing is not entirely digital but closely approximates a digital process. Most every function in the brain is a.n.a.log and is filled with nonlinearities (sudden s.h.i.+fts in output, rather than levels changing smoothly) that are substantially more complex than the cla.s.sical model that we have been using for neurons. However, the detailed, nonlinear dynamics of a neuron and all of its const.i.tuents (dendrites, spines, channels, and axons) can be modeled through the mathematics of nonlinear systems. These mathematical models can then be simulated on a digital computer to any desired degree of accuracy. As I mentioned, if we simulate the neural regions using transistors in their native a.n.a.log mode rather than through digital computation, this approach can provide improved capacity by three or four orders of magnitude, as Carver Mead has demonstrated.13The brain rewires itself. Dendrites are continually exploring new spines and synapses. The topology and conductance of dendrites and synapses are also continually adapting. The nervous system is self-organizing at all levels of its organization. While the mathematical techniques used in computerized pattern-recognition systems such as neural nets and Markov models are much simpler than those used in the brain, we do have substantial engineering experience with self-organizing models. Dendrites are continually exploring new spines and synapses. The topology and conductance of dendrites and synapses are also continually adapting. The nervous system is self-organizing at all levels of its organization. While the mathematical techniques used in computerized pattern-recognition systems such as neural nets and Markov models are much simpler than those used in the brain, we do have substantial engineering experience with self-organizing models.14 Contemporary computers don't literally rewire themselves (although emerging ”self-healing systems” are starting to do this), but we can effectively simulate this process in software. Contemporary computers don't literally rewire themselves (although emerging ”self-healing systems” are starting to do this), but we can effectively simulate this process in software.15 In the future, we can implement this in hardware, as well, although there may be advantages to implementing most self-organization in software, which provides more flexibility for programmers. In the future, we can implement this in hardware, as well, although there may be advantages to implementing most self-organization in software, which provides more flexibility for programmers.Most of the details in the brain are random. While there is a great deal of stochastic (random within carefully controlled constraints) process in every aspect of the brain, it is not necessary to model every ”dimple” on the surface of every dendrite, any more than it is necessary to model every tiny variation in the surface of every transistor in understanding the principles of operation of a computer. But certain details are critical in decoding the principles of operation of the brain, which compels us to distinguish between them and those that comprise stochastic ”noise” or chaos. The chaotic (random and unpredictable) aspects of neural function can be modeled using the mathematical techniques of complexity theory and chaos theory. While there is a great deal of stochastic (random within carefully controlled constraints) process in every aspect of the brain, it is not necessary to model every ”dimple” on the surface of every dendrite, any more than it is necessary to model every tiny variation in the surface of every transistor in understanding the principles of operation of a computer. But certain details are critical in decoding the principles of operation of the brain, which compels us to distinguish between them and those that comprise stochastic ”noise” or chaos. The chaotic (random and unpredictable) aspects of neural function can be modeled using the mathematical techniques of complexity theory and chaos theory.16The brain uses emergent properties. Intelligent behavior is an emergent property of the brain's chaotic and complex activity. Consider the a.n.a.logy to the apparently intelligent design of termite and ant colonies, with their delicately constructed interconnecting tunnels and ventilation systems. Despite their clever and intricate design, ant and termite hills have no master architects; the architecture emerges from the unpredictable interactions of all the colony members, each following relatively simple rules. Intelligent behavior is an emergent property of the brain's chaotic and complex activity. Consider the a.n.a.logy to the apparently intelligent design of termite and ant colonies, with their delicately constructed interconnecting tunnels and ventilation systems. Despite their clever and intricate design, ant and termite hills have no master architects; the architecture emerges from the unpredictable interactions of all the colony members, each following relatively simple rules.The brain is imperfect. It is the nature of complex adaptive systems that the emergent intelligence of its decisions is suboptimal. (That is, it reflects a lower level of intelligence than would be represented by an optimal arrangement of its elements.} It needs only to be good enough, which in the case of our species meant a level of intelligence sufficient to enable us to outwit the compet.i.tors in our ecological niche (for example, primates who also combine a cognitive function with an opposable appendage but whose brains are not as developed as humans and whose hands do not work as well). It is the nature of complex adaptive systems that the emergent intelligence of its decisions is suboptimal. (That is, it reflects a lower level of intelligence than would be represented by an optimal arrangement of its elements.} It needs only to be good enough, which in the case of our species meant a level of intelligence sufficient to enable us to outwit the compet.i.tors in our ecological niche (for example, primates who also combine a cognitive function with an opposable appendage but whose brains are not as developed as humans and whose hands do not work as well).We contradict ourselves. A variety of ideas and approaches, including conflicting ones, leads to superior outcomes. Our brains are quite capable of holding contradictory views. In fact, we thrive on this internal diversity. Consider the a.n.a.logy to a human society, particularly a democratic one, with its constructive ways of resolving multiple viewpoints. A variety of ideas and approaches, including conflicting ones, leads to superior outcomes. Our brains are quite capable of holding contradictory views. In fact, we thrive on this internal diversity. Consider the a.n.a.logy to a human society, particularly a democratic one, with its constructive ways of resolving multiple viewpoints.The brain uses evolution. The basic learning paradigm used by the brain is an evolutionary one: the patterns of connections that are most successful in making sense of the world and contributing to recognitions and decisions survive. A newborn's brain contains mostly randomly linked interneuronal connections, and only a portion of those survive in the two-year-old brain. The basic learning paradigm used by the brain is an evolutionary one: the patterns of connections that are most successful in making sense of the world and contributing to recognitions and decisions survive. A newborn's brain contains mostly randomly linked interneuronal connections, and only a portion of those survive in the two-year-old brain.17The patterns are important. Certain details of these chaotic self-organizing methods, expressed as model constraints (rules defining the initial conditions and the means for self-organization), are crucial, whereas many details within the constraints are initially set randomly. The system then self-organizes and gradually represents the invariant features of the information that has been presented to the system. The resulting information is not found in specific nodes or connections but rather is a distributed pattern. Certain details of these chaotic self-organizing methods, expressed as model constraints (rules defining the initial conditions and the means for self-organization), are crucial, whereas many details within the constraints are initially set randomly. The system then self-organizes and gradually represents the invariant features of the information that has been presented to the system. The resulting information is not found in specific nodes or connections but rather is a distributed pattern.The brain is holographic. There is an a.n.a.logy between distributed information in a hologram and the method of information representation in brain networks. We find this also in the self-organizing methods used in computerized pattern recognition, such as neural nets, Markov models, and genetic algorithms. There is an a.n.a.logy between distributed information in a hologram and the method of information representation in brain networks. We find this also in the self-organizing methods used in computerized pattern recognition, such as neural nets, Markov models, and genetic algorithms.18The brain is deeply connected. The brain gets its resilience from being a deeply connected network in which information has many ways of navigating from one point to another. Consider the a.n.a.logy to the Internet, which has become increasingly stable as the number of its const.i.tuent nodes has increased. Nodes, even entire hubs of the Internet, can become inoperative without ever bringing down the entire network. Similarly, we continually lose neurons without affecting the integrity of the entire brain. The brain gets its resilience from being a deeply connected network in which information has many ways of navigating from one point to another. Consider the a.n.a.logy to the Internet, which has become increasingly stable as the number of its const.i.tuent nodes has increased. Nodes, even entire hubs of the Internet, can become inoperative without ever bringing down the entire network. Similarly, we continually lose neurons without affecting the integrity of the entire brain.The brain does have an architecture of regions. Although the details of connections within a region are initially random within constraints and self-organizing, there is an architecture of several hundred regions that perform specific functions, with specific patterns of connections between regions. Although the details of connections within a region are initially random within constraints and self-organizing, there is an architecture of several hundred regions that perform specific functions, with specific patterns of connections between regions.The design of a brain region is simpler than the design of a neuron. Models often get simpler at a higher level, not more complex. Consider an a.n.a.logy with a computer. We do need to understand the detailed physics of semiconductors to model a transistor, and the equations underlying a single real transistor are complex. However, a digital circuit that multiplies two numbers, although involving hundreds of transistors, can be modeled far more simply, with only a few formulas. An entire computer with billions of transistors can be modeled through its instruction set and register description, which can be described on a handful of written pages of text and mathematical transformations. Models often get simpler at a higher level, not more complex. Consider an a.n.a.logy with a computer. We do need to understand the detailed physics of semiconductors to model a transistor, and the equations underlying a single real transistor are complex. However, a digital circuit that multiplies two numbers, although involving hundreds of transistors, can be modeled far more simply, with only a few formulas. An entire computer with billions of transistors can be modeled through its instruction set and register description, which can be described on a handful of written pages of text and mathematical transformations.