Is brain a Computer? Yaser ghaolami Z3291637
10/24/2011
Context Introduction
Page Number ………………………………………………………………………………………………… 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
Page 1 of 11
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 Page 2 of 11
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 neocortex 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 1
David Robson, New Scientist, 26 September 2011, issue 2831.
Page 3 of 11
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.
Page 4 of 11
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
Page 5 of 11
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
Page 6 of 11
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
Page 7 of 11
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.
7
0 , 1, 0
?
0, 1, 1
?
NEURAL NETWORKS , by Christos Stergiou and Dimitrios Siganos
Page 8 of 11
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
Page 9 of 11
Where
is input of neuron 1 is,
of neurons 2 and 3, also
and
is the weight of neuron 1 and similarly
and
are input
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!
Page 10 of 11
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.
Page 11 of 11