Is brain a Computer? Yaser ghaolami Z3291637

10/24/2011

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Context Page Number

Introduction ………………………………………………………………………………………………… 2

Brief history of brain ………………………………………………………………………………………. 2

Analog and Digital computer ……………………………………………………………………………. 4

Thought experiment ……………………………………………………………………………………. 5

Human Brain and Its Artificial neuron ……………………………………………………………… 7

How can a silicon brain be engineered? .………………………………………………………… 8

Conclusion.……………………………………………………………………………………………………….. 10

List of References ………………………………………………………………………………………………. 11

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Introduction:

There have always been many arguments and discussions regarding intelligence and its nature. This

question has always been asked that how can brain (i.e. a body organ made of fat, nerves and etc.)

enable human beings to breathe, eat, move, think and feel and so on. What parts of the brain are

responsible for these actions? Only recently scientists have been able to find answers to these

important questions as they have been recently enabled by the new advancements and progressions

in science and technology. Most recently scientists are investigating the possibility of building a

computer (silicon-based or preferably quantum) brain. The aim of this paper is to evaluate this idea.

Humans began a new social life by choosing permanent settlements mainly next to rivers and lakes

and quitting their nomad lives. This change of life style led to other changes such as creating new

trades (e.g. farming) and social relationships (e.g. exchanging goods). Moreover as time passed they

learnt how to best use their natural resources to improve their lives and make it more efficient,

effective and productive. For example they learnt how to create tools such as hammer, sewing

needle, and farming tools and wheel and so on. As time passed more complex and sophisticated

tools were developed which led to the creation of even more advanced tools. This process continued

to the point that machines and technologies were developed that were able to operate without

human supervision (e.g. fishing nets and irrigation canals). Thus although human’s physical force and

hands were still needed and essential, this kind of tools increased the flexibility, efficiency and

productivity of a lot of activities. Finally these advancements led to the introduction of computers

and robots. Currently computers and robots have replaced many human activities such as industrial

production lines, controlling and guiding space ships, mathematical calculations and planning and

allowing self-service in retail, financial and government agencies and so on. This great progress has

led to the speculation that computers can one day think and decide independently (i.e. artificial

intelligence). Some also think that computers will be more efficient and even smarter than human

beings. Indeed some scientists predict that the creation of artificial intelligence is actually viable and

it will happen soon. However the question is raised whether these claims are correct? Is artificial

intelligence really viable?

Brief history of brain

“The first mammals on Earth (200 million years ago) already had a small neo-cortex which is extra

layers of neural tissue on the surface of the brain responsible for the complexity and flexibility of

mammalian behaviour” (Robson 2011, p.40). The reference further suggests that it remains a

mystery how and when this crucial region was evolved, however what is clear is that the brain size of

mammals increased relative to their bodies as they struggled to contend with the dinosaurs (page

number). In addition they explain that “Timothy Rowe at the University of Texas at Austin recently

used CT scans to look at the brain cavities of fossils of two early mammal-like animals,

Morganucodon and Hadrocodium, both tiny, shrew-like creatures that fed on insects. Rowe's scans

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revealed that the first big increases in size were in the olfactory bulb, suggesting mammals came to

rely heavily on their noses to sniff out food. There were also big increases in the regions of the neo-

cortex that map tactile sensations - probably the ruffling of hair in particular - which suggests the

sense of touch was vital too” (Robson 2011, p.42). There have been suggestions that these findings

support the claim that the early mammals were mostly hiding during the day and more active during

night to protect themselves from the predators of the time, dinosaurs.

Thus after the extinction of dinosaurs, mammals were able to come out and explore the nature

more freely. The ancestors of the primates took to the tress where they would spot and hunt insects

on trees which led to “an expansion of the visual part of the neo-cortex” (Robson, 011, p. 42).

Furthermore it is speculated that these primates were living in groups. In fact Robin Dunbar at the

University of Oxford suggests that this life style has caused the great expansion of the frontal regions

of the primate neocortex, particularly in the apes. As also quoted by the reference (P.42) he states

that “You need more computing power to handle those relationships”. Additionally Dunbar has also

revealed the strong relationship between size of these groups, the frequency of their interactions

with one another and the size of the frontal neo-cortex in various species1 .Robson (P.42) further

proposes that “increasing in size, these frontal regions also became better connected, both within

themselves, and to other parts of the brain that deal with sensory input and motor control (such

changes can even be seen in the individual neurons within these regions, which have evolved more

input and output points)”.

Hence these evolving developments enabled the future primates to gather and process information

that helped them to evaluate and compare different options and use reasoning to choose one

course of action. This intelligent process further increased their intelligence as well as the level of

their abstract thought. In other words the more the primates dealt with the process of thoughts

regarding concrete and physical objects in the nature, the more their abstract thinking ability was

developed. Robson (2011, P.44) suggests that this type of thought process belongs to an ape that

lived about 14 million years ago in Africa. He (P.44) further explains that “it was a very smart ape but

the brains of most of its descendants - orang-utans, gorillas and chimpanzees - do not appear to

have changed greatly compared with the branch of its family that led to us”. However the question is

raised that what made us different? Is it true that this difference is due to the reason that we moved

out of the forest and walked on our two legs? However scientists have discovered that millions of

years after hominids stood up on their two legs their brains were still very small.

Moreover Robson (2011, P.44) proposes that “it is possible that serendipity played a part” in the

expansion of our brain. He (P. 44) furthermore states that “in other primates, the "bite" muscle

exerts a strong force across the whole of the skull, constraining its growth whereas in our

forebears, this muscle was weakened by a single mutation, perhaps opening the way for the skull to

expand”. This mutation occurred around the same time as the first hominids with weaker jaws and

bigger skulls and brains appeared (Nature, vol 428, p 415). Indeed intelligence growth was enhanced

by positive outcomes and consequences resulted from intelligent life styles. Also further practice

and use of brain contributed to the process because more use of brain led to its further growth. For

example Todd Preuss of Emory University in Atlanta, Georgia suggests that the development of

1David Robson, New Scientist, 26 September 2011, issue 2831.

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hunting and butchering tools were actually very influential for humans’ brain growth as meat is a

rich source of nutrients and therefore positively affect their brain expansion. Also the primatologist

Richard Wrangham at Harvard University suggests that fire had a similar role in the expansion of our

brain by enabling us receive nutrients from different foods.

A different example is given by Luke Rendell and colleagues at the University of St Andrews in the UK

whose mathematical models support the idea that cultural and genetic evolution can feed off each

other and this in turn can produce extremely strong selection pressures that lead to runaway

evolution of certain traits. They propose that this kind of feedback may have affected the language

development. Thus during the early developments of language there had been different mutations

that improved the language skills and ability. An example is given by the Robson (2011, P.44) is the

“FOXP2 gene, which enables the basal ganglia and the cerebellum to lay down the complex motor

memories necessary for complex speech.”

However there is a need for a holistic picture which encompasses all the aspects of brain

development such as culture, social relationships, biology, technology, geographical environment. It

should also be noted that evolution never stops and this growth will continue. For example a recent

research has revealed that “the visual cortex has grown larger in people who migrated from Africa to

northern latitudes, perhaps to help make up for the dimmer light up there” (Biology Letters 2011,

p.0570).

Analog and Digital computer

Analog and digital computers are different in how they operate. Analog computers process

information in a continuous fashion and can handle a wide range of naturally occurring processes

and they receive one or more variables and produce a result that represents the relationships

between the input variables .The literatures use an oscilloscope as an example of an analog

computer. An oscilloscope produces visual traces on the screen by putting vertical and horizontal

input signals in the desired relationship. This relationship is commonly known as function. In fact

electronic analog computers can produce different mathematical and logical functions such as

differentiations, integration and logarithms. Moreover the functions that are too complex to be

solved by digital computers can be solved by analog computers. However analog computers are

limited in that they are inflexible and are designed to process a limited number of functions through

certain electronic devices. On the other hand digital computers are freed from this limitation.

Digital computers symbolize information in binary states of 0's (zeros) or 1's (ones). A "0" typically

represent low voltage (near zero volts), and a "1" implies that a voltage (which generally is about 5V

to 3V) exists. Each wire connection is symbolized by one bit of information. So therefore the value of

the bit could be "1" or "0” and two bits stand for two wires. Any bit could have values of "0" or "1" at

various times, which permits to symbolize four single states or events with values of 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.

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Thus by a series of wire connections a long strings of 0’s and 1’s will be produced. Moreover the

unique combination of 0’s and 1’s is translated into a unique number or information.2 In the

following parts of this paper I will try to show how brain could work like a digital computer.

Thought experiment

After exploring a brief history of brain development and the way computers work, it would

be beneficial to investigate a thought experiment made by By Anythony Jonathan

Smith and Richard Hayes.

Brain is the most complex organ of the body which is made of fat and billions of neurons. In

fact the large numbers of neurons and their interactions and connectivity have caused the

complexity of the brain.

As it can be seen from the picture above neurons are made of a cell body, dendrites, axon and

terminal. The dendrites receive signals from other neutrons. The signals travel along the neuron and

to the terminals where they communicate the signals to other neurons. The dendrites become

thinner as they extend to the terminal which improves the surface area with which the neurons

receive information. Moreover there are junctions on the surface of the dendrites which improves

the reception of information. Furthermore the length of dendrites can be varied influenced by

different factors. For example dendrite growth is associated with learning while senility is known to

be linked with shortened dendrites and a reduction in the number of dendrite branches3.

2 Brain Versus Computer by Martin Dak

3 NEURAL NETWORKS,by Christos Stergiou and Dimitrios Siganos

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In the middle of the neurons there are axons that are considered to act as conductors as they

conduct the signals to the transmitters at the terminals. Indeed different neurons have different

axons as well. For example “Bipolar neurons have one Soma, and the axon will then split into two

terminals to transmit information (these kinds of neurons can be found in the retina)”4.

Additionally the terminals are located to the dendrites of a nearby neuron. Upon the arrival of a

signal to the terminal, a chemical (i.e. neurotransmitter) is released which travels across the gap

between the terminal and dendrite of the other neuron. This process is called the synapse and is

repeated through the next neuron and so on. Thus the brain operates through these incredibly fast

and accurate communications between billions of neurons.

In fact this thought experiment involves the thought of replacing the axon with a silicon computer

chip, which will then do the exact same thing.

Although the neuron will have a computer chip, the signals will not be changed at all and it will still

function as before. This demonstrated that a computer chip can replace a natural part of the brain

and still work the same. This raises the question that if we can successfully replace an axon with a

computer chip, can we also replace other parts of the neuron such as dendrites or the terminals with

computer chips which could function the same? If this is possible, this claim can be made that we

can have completely silicon neurons which can basically build a functioning silicon version of the

human brain. However there needs to be a greater and deeper understanding about the brain

system.

4 NEURAL NETWORKS,by Christos Stergiou and Dimitrios Siganos

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Human Brain and Its Artificial neuron

Both the biological and artificial neurons receive the signals from their dendrites and sends out

electrical signals from their terminals to other neuron’s dendrites. A signal could be sent when the

neuron is Fired. In fact the following diagram demonstrates both the biological makeup, and

theoretical digital makeup of a neuron.

The above diagram represents Human biological neurons and the below

diagram shows the digital neuron.5

How can a silicon brain be engineered?

An artificial neuron can only have one output and many inputs (the dendrites). Stergiou and Siganos6

(2011), states that “the neuron is either in two modes, Training Mode TM (where it's learning), or

Using Mode UM (where it's in normal use)”. As the name suggests, the TM involves training the

neuron to fire or not for certain input patterns. They further explains that “In UM, the neuron will

fire when it is fed a pattern that it already knows but if it's presented with a pattern it's not already

learnt, it will use its Firing Rule to decide whether to fire or not”. 5 NEURAL NETWORKS,by Christos Stergiou and Dimitrios Siganos

6 NEURAL NETWORKS,by Christos Stergiou and Dimitrios Siganos

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To better understand it is beneficial to look at this diagram7:

In fact input pattern refers to whether the neuron receives an input or not. Indeed some inputs

might be receiving signals while others not. Thus the neuron’s task is to either fire or not depending

on its input. It is important to understand the concept of the “Firing Rule” as based on the input

pattern this rule will determine whether a neutron should fire or not. Also it is important that this

rule applies to all input pattern including the patterns that are unfamiliar to the neurons.

To better understand these concepts we can examine a neuron that has 3 inputs. The below

diagrams shows the possible output of the neuron (based on the states of the three inputs):

1, 1, 1 1

1, 0, 1 1

0, 0, 0 0

0, 0, 1 0

This table demonstrates that the neuron has learnt four input patterns. In other words the neuron

will fire if the input patterns are 1,1,1 and 1,0,1 and not to fire if the input patterns are 0,0,0 or 0,0,1.

However there remain many other input patterns which are unknown to the neutron.

For example the following demonstration reveals some possible input patterns that are unknown to

the neuron. There are no outputs for these input patterns yet.

0 , 1, 0 ?

0, 1, 1 ?

7 NEURAL NETWORKS , by Christos Stergiou and Dimitrios Siganos

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1, 0, 1 ?

1, 0, 0 ?

Comparing the first pattern (0,1,0) to the learnt input patterns by the neuron, it is revealed that the

pattern is different from 1,1,1 in 2 elements, 1,0,1 in 3 elements, 0,0,0 in 1 element and 0,0,1 in 2

elements. Thus the nearest pattern to the unknown pattern is 0,0,0 because it has the least number

of different elements and therefore needs less changes to match the unknown input pattern.

Therefore when the neuron receives the input pattern of 0,0,0 it will not fire. Hence:

0, 1, 0 0

0, 1, 1 0,1

1, 0, 1 1

1, 0, 0 0,1

But apparently we have a problem as there are 'similar' identified patterns for inputs “0,1,1 and

1,0,0” which results in firing or not firing. These states are undefined.

This example depicts a ‘neutron’ that is currently present in the computers. However a more

complex and sophisticated neuron is the one introduced by McCulloch and Pitts model (MCP). In this

neutron the inputs are weighted, the weight of an input is a number which, when multiplied with

the input, gives the weighted input”. The model further explains that these weighted inputs are next

combined and if they go above a value of pre-set threshold, the neuron would fire. In any other

situation the neuron would not fire. The picture below captures this concept well8:

Thus mathematically, the neutron will fire if:

8 NEURAL NETWORKS,by Christos Stergiou and Dimitrios Siganos

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Where is input of neuron 1 is, is the weight of neuron 1 and similarly and are input

of neurons 2 and 3, also and are their weights.

The neurons are able to randomly alter the weights on their inputs, which will permit them for

greater pattern matching and learning. The weights could be modified in order to produce the

correct (desired) output.

Conclusion

In conclusion considering all the aspects that I discussed about brain and computer it can be claimed

that brain could be indeed a computer or at least it is highly possible for a brain to be like a

computer. This claim can be supported by the fact that current computer science and technology

has allowed for the creation of the basic artificial and digital structure of brain neuron. Therefore it

can be anticipated that more features of the brain can be artificially engineered in the future.

However scientists need deeper and more accurate information about the structure and system of

the brain. As technology becomes more advanced more information is being revealed in this

regards. The revealed information and the possibility of creating an artificial brain (computer-based)

suggest that brain and computer are synonymous which means that brain is indeed a computer!

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List of References:

David Robson, New Scientist, 26 September 2011, issue 2831.

http://www.newscientist.com/article/mg21128311.800-a-brief-history-of-the-brain.html?full=true

Brain Versus Computer By Martin Dak

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

Neural Networks by Christos stergiou and Dimitros Siganos

http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html

Is brain like a computer by Professor Mark Dubin

http://mcdb.colorado.edu/courses/3650/computer/index.html

International Journal of Information Technology and Knowledge Management July-December 2010,

Volume 2, No. 2, pp. 529-532.