Neural Computer Unlimited Memory

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NEURAL COMPUTER AND UNLIMITED MEMORY

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NEURAL COMPUTER AND UNLIMITED MEMORY

INTRODUCTION

In this 21 st century, it is an era of new inventions and

discoveries. And in this era main experiment conducted on

neural network, IT and robotic technology. Both IT and

robotic technology is widely used in different areas and in

neural networks more invention are going. And in these days

we develop so many super computers as like param1,2 etc..this super computer invented for storing a large volume of

data and for getting a high processing speed, but it have

limitation that we seen in the experiment of LARGE HADRON

COLLIDE ,it is an experiment conducting in Europe and this

experiment is also known as the largest and dangerous

experiment conducted in this century. This experiment is

used for studying the condition of earth and universe before

big bang. The out coming result is important we cannot

avoid each information and this will happen in fraction of

seconds because these type experiment conducting on light

travelling speed so there should be highly efficient

computer for saving that data and in hadron collider if the

out coming results are stored in compact disc and it make it


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aligned it should have a length of 20 km in a day. Here we

need a high memory having high processing speed. For this

experiment here they used nodes of super computer and

also we know that secondary memory having less execution

than a processor. There we can propose a idea a unlimitedmemory, high processing speed as compared as our

imagination. That is my topic neural computer, innovative

thoughts and dreams an unlimited memory.

NEURAL COMPUTER

ABSTRACT

It is completely a new idea, our topic is to

make our selves as a computer and if there is resources

we can make unlimited storage memory through biological

and electronic concepts. This concept is work with the

basic principle of neural networks. In neural networks it

can not produce miracles, but sensibly they can produce

some amazing results.

Here we trying to make an artificial neural

network, An Artificial Neural Network (ANN) is an

information processing paradigm that is inspired by the


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way biological nervous systems, such as the brain, process

information. The key element of this paradigm is the novel

structure of the information processing system. It is

composed of a large number of highly interconnected

processing elements (neurones) working in unison to solvespecific problems. ANNs, like people, learn by example. An

ANN is configured for a specific application, such as

pattern recognition or data classification, through a

learning process. Learning in biological systems involves

adjustments to the synaptic connections that exist

between the neurones. This is true of ANNs as well.

In this topic our aim is to make storing a data to

brain or genomes that help us to remember through an

electronic device. we don't think that it is a one year

project. For this project we first consider how the neuron

activated while we learning something and which type of

pattern that it generated while we learning then we

converted our neuron through an electronic system

because we know that neuron produces a electric signal

corresponding to its input. For this we have to know how

human brain is learned some thing, Much is still unknown

about how the brain trains itself to process information,

so theories abound. In the human brain, a typical neuron

collects signals from others through a host of fine

structures called dendrites. The neuron sends out spikes

of electrical activity through a long, thin stand known as

an axon, which splits into thousands of branches. At the

end of each branch, a structure called a synapse converts

the activity from the axon into electrical effects thatinhibit or excite activity from the axon into electrical

effects that inhibit or excite activity in the connected

neurones. When a neuron receives excitatory input that is

sufficiently large compared with its inhibitory input, it

sends a spike of electrical activity down its axon. Learning


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occurs by changing the effectiveness of the synapses so

that the influence of one neuron on another changes.

next step is human neuron to artificial neurons for this

some scientists make a model programming it follows as

like this

engineering approach to this project

As above said we first learned how the neuron works and

what type of pulses that produce while we learning. We make a

electronic system that convert messages that are to be learned is


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converted to a form that it can stimulate the neuron and brain.

And we consider each rule in electronics in this field, for

describing these we need the help of FIRING RULE.The firing rule

is an important concept in neural networks and accounts for their

high flexibility. A firing rule determines how one calculateswhether a neuron should fire for any input pattern. It relates to all

the input patterns, not only the ones on which the node was

trained.

COURSE OF THOUGHT

This is a new concept, now it is just proposed this

idea and further researches are going very rapidly and the

research results were kept secretly because it have some


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ethical problems these will discuss latterly. Actually it a

idea come out for making a unlimited memory and it

should have high processing speed and we can avoid the

use of cache memory.

And for this concept the basics of this concept is neural

network.

In these days there is a innovative invention on artificial

neural networks. In this idea it mainly used biological

neuron accompanying with some basic principle used in

the artificial neural networks.

Concept behind this idea

Concept behind this idea is that choosing an

element for making a memory so here chose an element

that is neuron. The specialty for choosing neuron as an

element is that

1. Adaptive learning

Adaptive learning is the property of neural

network that is a neural net work can perform a work withconsidering its input. For a computer network there

should be programmed interface or its corresponding

software for identifying this or by manually we should

point that particular that input to a software in neuron

that is not needed that property is known as adaptive

learning.

2. Self organization

Self organization means a neural network can its own

organization or representation for given information.

3. Real time operation


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It is a special property of a neuron that it can performed a

multitasking job and in this modern world we performed

with the help of PLC and SCADA

4. FAULT TOLERANCEFault tolerance means a neural network have high

capability to avoid error or fault.

In modern devices such like processors these are the

main challenges. They improve to get these but for a

neuron it is a common property. Because of these

properties it can considered as an element to making an

un limited memory. In this memory system we used here

biological neuron as processors and data buses, we know

the processing speed of neuron for knowing processing

speed of neuron just analyze its reflex action and we can

see even light its travelling 3*10^8 m/s.


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BUILDING CONCEPTS OF A NEURAL COMPUTER

1.How a neural network performs an operation

i) FIRING RULE

The performance of neural network mainly

based on FIRING RULE.

A simple firing rule can be implemented by using Hamming distance

technique. The rule goes as follows:

Take a collection of training patterns for a node, some of

which cause it to fire (the 1-taught set of patterns) and

others which prevent it from doing so (the 0-taught set).

Then the patterns not in the collection cause the node to

fire if, on comparison , they have more input elements in

common with the 'nearest' pattern in the 1-taught set than

with the 'nearest' pattern in the 0-taught set. If there is a

tie, then the pattern remains in the undefined state.

For example, a 3-input neuron is taught to output 1 when the input

(X1,X2 and X3) is 111 or 101 and to output 0 when the input is 000

or 001. Then, before applying the firing rule, the truth table is;

X1: 0 0 0 0 1 1 1 1

X2: 0 0 1 1 0 0 1 1

X3: 0 1 0 1 0 1 0 1

OUT: 0 0 0/1 0/1 0/1 1 0/1 1

As an example of the way the firing rule is applied, take the pattern

010. It differs from 000 in 1 element, from 001 in 2 elements, from

101 in 3 elements and from 111 in 2 elements. Therefore, the


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'nearest' pattern is 000 which belongs in the 0-taught set. Thus the

firing rule requires that the neuron should not fire when the input is

001. On the other hand, 011 is equally distant from two taught

patterns that have different outputs and thus the output stays

undefined (0/1).

By applying the firing in every column the following truth table is

obtained;

X1: 0 0 0 0 1 1 1 1

X2: 0 0 1 1 0 0 1 1

X3: 0 1 0 1 0 1 0 1

OUT: 0 0 0 0/1 0/1 1 1 1

The difference between the two truth tables is called the

generalization of the neuron. Therefore the firing rule gives the

neuron a sense of similarity and enables it to respond 'sensibly' topatterns not seen during training.


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ii) DATA TRANSMISSION IN A BIOLOGICAL NEURON

A neuron is simply a transducer which converts physical energy to

electrical signals and it has following procedures:


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2. THRESHOLD NEURON

When a neuron is not sending a signal, it is "at rest." When a neuron is at rest, the inside of theneuron is negative relative to the outside. Although the concentrations of the different ions at-

tempt to balance out on both sides of the membrane, they cannot because the cell membrane al-

lows only some ions to pass through channels (ion channels). At rest, potassium ions (K+) cancross through the membrane easily. Also at rest, chloride ions (Cl-)and sodium ions (Na+) have a

more difficult time crossing. The negatively charged protein molecules (A-) inside the neuron

cannot cross the membrane. In addition to these selective ion


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channels, there is a pump that uses energy to move three sodium ions out of the neuron for every

two potassium ions it puts in. Finally, when all these forces balance out, and the difference in the

voltage between the inside and outside of the neuron is measured, you have the resting poten-

tial. The resting membrane potential of a neuron is about -70 mV (mV=millivolt) - this means

that the inside of the neuron is 70 mV less than the outside. At rest, there are relatively more so-

dium ions outside the neuron and more potassium ions inside that neuron.

Action Potential

The resting potential tells about

what happens when a neuron is at rest. An action potential occurs when a neuron sends informa-

tion down an axon, away from the cell body. Neuroscientists use other words, such as a "spike"

or an "impulse" for the action potential. The action potential is an explosion of electrical activitythat is created by a depolarizing current. This means that some event (a stimulus) causes the

resting potential to move toward 0 mV. When the depolarization reaches about -55 mV a neuron

will fire an action potential. This is the threshold. If the neuron does not reach this criticalthreshold level, then no action potential will fire. Also, when the threshold level is reached, an

action potential of a fixed sized will always fire...for any given neuron, the size of the action po-

tential is always the same. There are no big or small action potentials in one nerve cell - all ac-

tion potentials are the same size. Therefore, the neuron either does not reach the threshold or afull action potential is fired - this is the "ALL OR NONE" principle.

Action potentials

are caused by an exchange of ions across the neuron membrane. A stimulus first causes sodium


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channels to open. Because there are many more sodium ions on the outside, and the inside of the

neuron is negative relative to the outside, sodium ions rush into the neuron. Remember, sodium

has a positive charge, so the neuron becomes more positive and becomes depolarized. It takeslonger for potassium channels to open. When they do open, potassium rushes out of the cell, re-

versing the depolarization. Also at about this time, sodium channels start to close. This causes the

action potential to go back toward -70 mV (a repolarization). The action potential actually goespast -70 mV (a hyperpolarization) because the potassium channels stay open a bit too long.

Gradually, the ion concentrations go back to resting levels and the cell returns to -70 mV.

i)LOGICAL STATUS OF A THRESHOLD NEURON

1 2 (0 1 {0 1){1 1}}1 1 1 y; ; , x = - y- ,-y , x


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ii)PERCEPTRON(THRESHOLD) ACTIVATION FUNCTIONS

iii)THRESHOLD BOOLEAN FUNCTIONS


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Iv)THRESHOLD BOOLEAN FUNCTIONS AND THRESHOLD NEURONS

f x xn( ,..., )1 W w w wn= ( , ,..., )0 1

)...(),...( 1101 nnn xwxwwsignxxf +++=


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V)GEOMETRICAL INTERPRETATION OF THRESHOLD BOOLEAN FUNCTIONS

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ESSENCE OF THIS IDEA


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The objective and aim of this topic is that making a un limited memory by

using a neuron as a element. Neuron is not only an element by technically

it is a data bus. And for an un limited memory we need a storing or

memory for this we use our brain itself means human brain itself. Because

human brain have two parts

1 . short term memory

2. long term memory

In short term memory it act as like as a ram it can success only when we

are in conscious state and longterm memory is act like as a ROM in

computer memory. The entire data is stored in our long term memory that

is the cap[acity of oyr long term memory is unpredictable before the deep

study scientists thought that make long term memory as a memory

element.

And by using this long term memory as according to this idea all data

sends to brain to its long term memory without reading a lesson , without

seeing any visuals and without hearing any audio signals we can send a

data directly to brain. And this can be possible by stimulating neuron by

externally.

Stimulation of neuron can done externally by using a electric field.


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How we can stimulate a neuron externally


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External stimulation done in a bird

This is the set up done for the external stimulation of a bird is Zebra finc

and by stimulating its neuron they produce its sound and it decoded

directly from its brain by brain mapping technique.


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Transmission of data to the brain

A data can transfer to the brain with external stimulation and making a

signal corresponding to the data that have to store in the brain. For

making this we have to study each patern of the brain wave patern for

reading, visuals and this can taken by scanning such as MRI eeG ETC..

And studying each paterns we can produce externally that type of a

signal and by using a transmitter a computer interface we can send this

data directly to the brain.


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APPLICATION

1. By using this we can make unlimited memory

2. it can be used in hadron collider systems

3. if this is a success we can cure the disease parkinsons and paralysys

4. And another thing is that we can forget blindness, dumph and dump

etc..

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Neural Computer Unlimited Memory

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