Mapping of Human Brain With ICT

8/2/2019 Mapping of Human Brain With ICT

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Mapping of human brain with computers

Parag K. Rabade.

Group consultant: education delivery

e-mail:[email protected]

Abstract. 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. The brain interprets any experience of man with the natural world 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.

1 Introduction

Human beings are part of the natural world. They have evolved from lower organisms over a relativelylong 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, breathe, 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.

Simulated intelligenceThe idea that artificial intelligence exists has now become the dominant theme shaping our understanding

of psychology and neuroscience. By contrast, the reality of artificial intelligence is dismal. To date, no

computer has made the most simple self-initiated decision and has manifested no hint of intelligence.

Everything that computers do is programmed by humans. The seemingly minor difference between

artificial intelligence and simulation of human behaviors is of no consequence to most people. The true

difference is huge. Simulation only produces the appearance of intelligent responses that seem to be

produced by a machine. Artificial intelligence means that intelligence itself is generated by machines.

Unfortunately, artificial intelligence is only a fictional concept, just as time travel is.

Human brain and machinesThe 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 (range). 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
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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. Brains are often compared to computers,

but apart from the trivial fact that both process information using a complex pattern of connections in a

physical space, it has been unclear whether this is more than just a metaphor, said Danielle Bassett, firstauthor and a postdoctoral research associate in the Department of Physics at UC Santa Barbara. The

scientists have uncovered novel quantitative organizational principles that underlie the network

organizations of the human brain, high performance computer circuits, and the nervous system of the

worm, known as nematode C. elegans. Using data that is largely in the public domain, including magnetic

resonance imaging data from human brains, a map of the nematodes nervous system, and a standard

computer chip, they examined how the elements in each system are networked together.They found that all

three shared two basic properties. First, the human brain, the nematodes nervous system, and the computer

chip all have a Russian doll-like architecture, with the same patterns repeating over and over again at

different scales. Second, all three showed what is known as Rents scaling, a rule used to describe the

relationship between the number of elements in a given area and the number of links between them. Worm

brains may seem to have very little in common with human brains and even less in common with computer

circuits, Bassett said.In fact, each of these systems contains a pattern of connections that are locked solidly

in a physical space, similar to how the tracks in a railway system are locked solidly to the ground, formingtraffic paths that have fixed GPS coordinates.Our brain is home to around 100 billion neurons, all of which

are perpetually establishing and breaking connections, known as synapses, with other neurons. There are

trillions of these connections throughout your brain helping orchestrate everything from movement, to

learning, to establishing and recalling memories. But we still don't understand how all the connections

between those neurons work. Our basic synapse is a connection between two neurons: a presynaptic

neuron, and a postsynaptic neuron. Presynaptic neurons release neurotransmitters, which dock with

receptors on the postsynaptic neuron and activate what are known as ion channels in the postsynaptic cell

membrane. All this is to say that when neurons talk to one another, there's more regulating their

communication than a simple on/off switch; and yet, most of the computer chips that we use to model brain

activity operate in this binary fashion.

ResemblanceThe vast majority of computers are digital, which means that they perform their operations using a binarysystem that has only two possible, discrete states: on and off , or if you prefer, 0 and 1. So does

the human brain operate as an analog system, or a digital one? The answer is both. On the one hand, a

neuron either does or does not transmit an action potential. This is an all-or-nothing process, and in this

sense, the brain operates digitally. But the frequency at which a neuron transmits action potentials can vary

continuously, thus giving it this property of an analog system as well. Neurons operate analogically inanother sense as well. Every neuron is constantly receiving numerous nerve impulses (action potentials)

from other neurons across their synapses with its dendrites. Depending on the receptors at which these

potentials are received on the complex surface of the dendrite membrane, they will have either an

excitatory or an inhibitory effect. The neuron constantly sums these two types of potentials, so that the

overall state of polarization of its membrane varies continuously, in analog fashion, under the effect of its

numerous synapses. And it is only at the neurons axon cone that this analog signal is converted into a

digital action potential. Most computers process information very rapidly, but they do so in serial fashion:all of the information is processed by a single central processing unit (CPU) that performs one operation

after another. But the CPU can also simulate parallel processing by subdividing its various tasks into

subtasks and alternating rapidly among them. The brains neurons are much slower than a computers

integrated circuits. But the brains power comes from its being a machine that performs massively parallel

processing. The brain does not have a CPU. Instead, it has millions of neurons that combine signals

simultaneously. At any given time, many large, specialized areas of the brain are operating in parallel to

perform a variety of tasks, such as processing visual or auditory information or planning an action. And

even within each of these areas, information flows through neural networks that have no significant serial


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structures. However, just as computers are serial systems that can simulate parallel processing, the brain is

a parallel system that can simulate serial operations (in handling spoken and written language, for

example). Computers are deterministic machines in the sense that with a given input, they will always

produce the same output. This does not mean that this output is always predictable. For example,

computers can simulate non-deterministic systems by introducing pseudo-random variables. Computers can

also apply equations from chaos physics, in which the results of deterministic processes can be greatly

influenced by tiny variations in the initial conditions. The brain as a whole is considered a non-deterministic system, for the very simple reason that it is never completely the same from one moment to

the next. It is constantly forming new synapses and strengthening or weakening existing ones according to

how they are being used. Consequently, a given input will never produce exactly the same output twice.

However, the physiochemical processes underlying brain activity are considered to be deterministic. 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]. The ideas in these models do have merit because apparent

synchronization of neural structures has been observed. Some researchers have associated synchronizationof 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.

Microanatomical ModelsThese 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. The brain is capable offunctioning 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. Neither computer software nor computer

hardware has the necessary properties to mimic the biological processes and responses of living neurons.

Incidentally, brain functions cannot be simulated in software. 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


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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 (deviation). 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 expressionwithin 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 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. 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 toimpossible. 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. 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 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 neurons in the brain 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 simulate the functions

of the human brain.

ConclusionDespite 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 becreated 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


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characteristics of life indicate that the barrier between an inert machine and a smart life form can never be

overcome. Machines are destined to remain dumb. Since a computer has no feelings or needs, it has no

necessity or ability to negotiate internal and external environmental effects. In turn, the lack of cognitive

drives prevents a computer from seeking sensible measures to counteract the environmental effects. As a

result, a computer has no need to employ intelligence. In fact, the inert nature of computers makes them

unfit to produce any intelligence whatsoever. Many people have a problem imagining how the human mind

could produce thought. The giant step from non-living chemical elements to intelligence seemsinconceivable. In reality, the challenge is not that big. The people are forgetting that the human mind does

not emerge from passive and largely inert chemicals. The giant leap has already been achieved in neurons.

They respond to and interact with the environment. So, the human brain just employs large amounts of

appropriately organized neurons to produce higher level of understanding of and interaction with the

environment. The unfounded belief that the human brain (relative to simple life forms) employs some

additional "mental magic" confuses many explorers of the brain and bothers them with artificially

generated mysteries. 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.

References

http://www.lucidpages.com/branco.html

http://io9.com/5860537/scientists-have-created-a-computer-chip-that-could-one-day-artificially-

boost-your-brain-power

http://thebrain.mcgill.ca/flash/capsules/outil_bleu25.html

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Computer-Chips
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Mapping of Human Brain With ICT

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