CHAPTER 1

INTRODUCTION

The evolution and development of mankind began thousands and thousands of years

before. And today our intelligence, our brain is a resultant of this long developmental phase.

Technology also has been on the path of development since when man appeared. It is man

that gave technology its present form. But today, technology is entering a phase where it will

outwit man in intelligence as well as efficiency.

Man has now to find a way in which he can keep in pace with technology, and one of

the recent developments in this regard, is the brain chip implants.

Brain chips are made with a view to enhance the memory of human beings, to help

paralyzed patients, and are also intended to serve military purposes. It is likely that

implantable computer chips acting as sensors, or actuators, may soon assist not only failing

memory, but even bestow fluency in a new language, or enable "recognition" of previously

unmet individuals. The progress already made in therapeutic devices, in prosthetics and in

computer science indicates that it may well be feasible to develop direct interfaces between

the brain and computers.

This technology is only under developmental phase, although many implants have

already been made on the human brain for experimental purposes. Let’s take a look at this

developing technology.

There is no doubt of the essential role of discrete neuronal networks in brain function.

Nevertheless, models of brain function based on neuronal networks alone fail to answer the

various fundamental questions of how the brain works, such as, “What is the neuronal

substrate of consciousness?“, or “Why do anesthetic effects diminish at higher atmospheric

pressure?”, or “How can purely endogenous processes be initiated?” These are but a few

examples of as yet unsatisfactorily addressed questions. In spite of concerted effort by

preeminent neuroscientists, no single complete theory of brain function explaining these

phenomenology’s has been offered. This void strongly suggests that there is a missing link in

the current fundamental concept of how the brain works.

This apparent impasse in neuroscience has recently been surmounted by the Vortex

Theory, which effectively links all-important phenomenology’s into a single fundamental

concept of the brain’s functional organization. The theory is firmly based on biological and 1

anatomical reality, essential considerations for any biological hypothesis. This manuscript is

an introduction to the fundamental architectural unit of the association cortex in the Vortex.

1.1 Working of human brainIn general, there are three stages in the processing of information by the nervous

system- sensory input, integration, and motor output. Sensory neurons transmit information

from sensors that detect external stimuli (light, sound, heat, smell, taste, touch) and internal

conditions (blood pressure, blood CO2 level, muscle tension). This information travels to the

CNS where interneurons analyze and interpret (integration) the sensory input, incorporating

the current circumstance with relevant situations from the past. The motor output then leaves

the CNS via motor neurons which communicate with muscle or endocrine cells.

Figure 1.1 Human brain

Example:

The knee-jerk reflex provides an example of this process. Here is what happens. First,

tapping the tendon connected to the quadriceps (extensor) muscle initiates the reflex. Sensors

then detect a sudden stretch in the quadriceps. Sensory neurons convey the information to the

spinal cord in addition to communicating with the motor neurons that deliver information to

the quadriceps. In return, the motor neurons convey signals to the quadriceps, causing the

muscle to contract and jerk the lower leg forward. The sensory neurons from the quadriceps

also communicate with interneurons in the spinal cord. In response, the interneurons inhibit

motor neurons that supply the hamstring (flexor) muscle. This inhibition prevents the

hamstring from contracting, which would resist the action of the quadriceps.

2

1.2 Organization of the ReportThis report starts with an overview of the brain and its working. We analyze the

impact of implantable brain chips on the paralyzed persons and its military applications.

Neural networks were developed after the brain which has its applications like pattern

recognition etc. The report is organized as follows:

Chapter 1: Introduction - This chapter briefly explains the overview of the report.

Chapter 2: Evaluation toward implantable brain chips - This chapter describes the

consequences that resulted in the development of brain chips.

Chapter 3: Neural networks - This chapter discusses the basic principle of the neural

networks. This chapter also includes the working and learning process of the neural

networks.

Chapter 4: Brain Chip - This chapter explains about the brain chip and its working and it

also explains the achievements, advantages and drawbacks of brain chips and also describes

the challenges faced by the scientists for the development of brain chips.

Chapter 5: Conclusions - This chapter summarizes the major accomplishments of this report.

3

CHAPTER 2

EVOLUTION TOWARDS IMPLANTABLE BRAIN CHIPS

Worldwide there are at least three million people living with artificial implants. In

particular, research on the cochlear implant and retinal vision has furthered the development

of interfaces between neural tissues and silicon substrate micro probes. There have been

many researches in order to enable the technology of implanting chips in the brain to

develop. Some of them are mentioned below.

2.1 The study of brainThe study of the human brain is, obviously, the most complicated area of

research. When we enter a discussion on this topic, the works of JOSE DELGADO need to

be mentioned. Much of the work taking place at the NIH, Stanford and elsewhere is built on

research done in the 1950s, notably that of Yale physiologist Jose Delgado, who implanted

electrodes in animal brains and attached them to a "stimoceiver" under the skull. This device

transmitted radio signals through the electrodes in a technique called electronic stimulation of

the brain, or ESB, and culminated in a now-legendary photograph, in the early 1960s, of

Delgado controlling a live bull with an electronic monitor.

2.2 Jose Delgado experimentsAccording to Delgado, "One of the possibilities with brain transmitters is to influence

people so that they conform to the political system. Autonomic and somatic functions,

individual and social behavior, emotional and mental reactions may be invoked, maintained,

modified, or inhibited, both in animals and in man, by stimulation of specific cerebral

structures. Physical control of many brain functions is a demonstrated fact. It is even possible

to follow intentions, the development of thought and visual experiences."

Delgado, in a series of experiments terrifying in their human potential, implanted

electrodes in the skull of a bull. Waving a red cape, Delgado provoked the animal to charge.

Then, with a signal emitted from a tiny hand-held radio transmitter, he made the beast turn

aside in mid-lunge and trot docilely away. He has [also] been able to “play” monkeys and

cats like “little electronic toys” that yawn, hide, fight, play, mate and go to sleep on

4

command. The individual is defenseless against direct manipulation of the brain [Delgado,

Physical Control].

Figure 2.1 Jose Delgado controlling a bull with stimoceiver

Such experiments were done even on human beings. Studies in human subjects with

implanted electrodes have demonstrated that electrical stimulation of the depth of the brain

can induce pleasurable manifestations, as evidenced by the spontaneous verbal reports of

patients, their facial expression and general behavior, and their desire to repeat the

experience. With such experiments, he unfolded many of the mysteries of the BRAIN, which

contributed to the developments in brain implant technology. For e.g.: he understood how the

sensation of suffering pain could be reduced by stimulating the frontal lobes of the brain.

Delgado was born in Rondo, Spain, and interestingly enough he is not a medical

doctor or even a vet, but merely a biologist with a Degree from Madrid University. He,

however, became an expert in neurobehavioral research and by the time he had published this

book (Physical Control of the Mind ) in 1969, he had more than 200 publishing credits to his

name. His research was sponsored by Yale University, Foundations Fund for Research in

Psychiatry, United States Public Health Service1, Office of Naval Research2, United States

Air Force 657-1st Aero medical Research Laboratory3, NeuroResearch Foundation, and the

Spanish Council for Scientific Education, among others.

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CHAPTER 3

NEURAL NETWORKS

3.1 What Are Artificial Neural Networks?

Figure 3.1 Neural network interconnections

● An extremely simplified model of the brain

● Essentially a function approximator

► Transforms inputs into outputs to the best of its ability

● Composed of many “neurons” that co-operate to perform the

desired function

3.2 What Are They Used For?● Classification

► Pattern recognition, feature extraction, image

matching

● Noise Reduction

► Recognize patterns in the inputs and produce

noiseless outputs

● Prediction

► Extrapolation based on historical data

3.3 Why Use Neural Networks?● Ability to learn

► NN’s figure out how to perform their function on their own 6

► Determine their function based only upon sample inputs

► Ability to generalize i.e. produce reasonable outputs for inputs it has

not been taught how to deal with.

3.4 Real NeuronsLet's start by taking a look at a biological neuron. Figure 1 shows such a neuron

Figure 3.2 A Biological Neuron

A neuron operates by receiving signals from other neurons through connections,

called synapses. The combination of these signals, in excess of a

certain threshold or activation level, will result in the neuron firing that is sending a signal on

to other neurons connected to it. Some signals act as excitations and others as inhibitions to a

neuron firing. What we call thinking is believed to be the collective effect of the presence or

absence of firings in the pattern of synaptic connections between neurons.

This sounds very simplistic until we recognize that there are approximately one

hundred billion (100,000,000,000) neurons each connected to as many as one thousand

(1,000) others in the human brain. The massive number of neurons and the complexity of

their interconnections results in a "thinking machine", your brain.

Each neuron has a body, called the soma. The soma is much like the body of any

other cell. It contains the cell nucleus, various bio-chemical factories and other components

that support ongoing activity.

Surrounding the soma are dendrites. The dendrites are receptors for signals generated

by other neurons. These signals may be excitatory or inhibitory. All signals present at the

dendrites of a neuron are combined and the result will determine whether or not that neuron

will fire.

If a neuron fires, an electrical impulse is generated. This impulse starts at the base,7

called the hillock, of a long cellular extension, called the axon, and proceeds down the axon

to its ends.

The end of the axon is actually split into multiple ends, called the buttons. The

buttons are connected to the dendrites of other neurons and the resulting interconnections are

the previously discussed synapses. (Actually, the buttons do not touch the dendrites; there is

a small gap between them.) If a neuron has fired, the electrical impulse that has been

generated stimulates the buttons and results in electrochemical activity which transmits the

signal across the synapses to the receiving dendrites.

At rest, the neuron maintains an electrical potential of about 40-60 mill volts. When a

neuron fires, an electrical impulse is created which is the result of a change in potential to

about 90-100 mill volts? This impulse travels between 0.5 to 100 meters per second and lasts

for about 1 millisecond. Once a neuron fires, it must rest for several milliseconds before it

can fire again. In some circumstances, the repetition rate may be as fast as 100 times per

second, equivalent to 10 milliseconds per firing.

Compare this to a very fast electronic computer whose signals travel at about

200,000,000 meters per second (speed of light in a wire is 2/3 of that in free air), whose

impulses last for 10 nanoseconds and may repeat such an impulse immediately in each

succeeding 10 nanoseconds continuously. Electronic computers have at least a 2,000,000

times advantage in signal transmission speed and 1,000,000 times advantage in signal

repetition rate.

It is clear that if signal speed or rate were the sole criteria for processing performance,

electronic computers would win hands down. What the human brain lacks in these, it makes

up in numbers of elements and interconnection complexity between those elements. This

difference in structure manifests itself in at least one important way; the human brain is not

as quick as an electronic computer at arithmetic, but it is many times faster and hugely more

capable at recognition of patterns and perception of relationships.

The human brain differs in another, extremely important, respect beyond speed; it is

capable of "self-programming" or adaptation buttons are connected to the dendrites of other

neurons and the resulting interconnections are the previously discussed synapses. (Actually,

the buttons do not touch the dendrites; there is in response to changing external stimuli. In

other words, it can learn. The brain has developed ways for neurons to change their response

to new stimulus patterns so that similar events may affect future responses. In particular, the

8

sensitivity to new patterns seems more extensive in proportion to their importance to survival

or if they are reinforced by repetition.

3.4.1 Neural network structure

Neural networks are models of biological neural structures. The starting point for

most neural networks is a model neuron, as in Figure 2. This neuron consists of multiple

inputs and a single output. Each input is modified by a weight, which multiplies with the

input value. The neuron will combine these weighted inputs and, with reference to a

threshold value and activation function, use these to determine its output. This behavior

follows closely our understanding of how real neurons work.

Figure 3.3 A Model Neuron

While there is a fair understanding of how an individual neuron works, there is still a

great deal of research and mostly conjecture regarding the way neurons organize themselves

and the mechanisms used by arrays of neurons to adapt their behavior to external stimuli.

There are a large number of experimental neural network structures currently in use

reflecting this state of continuing research.

In our case, we will only describe the structure, mathematics and behavior of that

structure known as the back propagation network. This is the most prevalent and generalized

neural network currently in use. If the reader is interested in finding out more about neural

networks or other networks, please refer to the material listed in the bibliography.

To build a back propagation network, proceed in the following fashion. First, take a

number of neurons and array them to form a layer. A layer has all its inputs connected to

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either a preceding layer or the inputs from the external world, but not both within the same

layer. A layer has all its outputs connected to either a succeeding layer or the outputs to the

external world, but not both within the same layer.

Next, multiple layers are then arrayed one succeeding the other so that there is an into

input layer, multiple intermediate layers and finally an output layer, as in Figure 3.

Intermediate layers, that is those that have no inputs or outputs to the external world, are

called >hidden layers. Back propagation neural networks are usually fully connected. This

means that each neuron is connected to every output from the preceding layer or one input

from the external world if the neuron is in the first layer and, correspondingly, each neuron

has its output connected to every neuron in the succeeding layer.

Figure 3.4 Back propagation Network

Generally, the input layer is considered a distributor of the signals from the external

world. Hidden layers are considered to be categorizers or feature detectors of such signals.

The output layer is considered a collector of the features detected and producer of the

response. While this view of the neural network may be helpful in conceptualizing the

functions of the layers, you should not take this model too literally as the functions described

may not be so specific or localized.

With this picture of how a neural network is constructed, we can now proceed are

considered to be categorizers or feature detectors of such to describe the operation of the

While this view of the neural network may be helpful in conceptualizing the functions of the

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layers, you should not take this model too literally as the functions described network in a

meaningful fashion please refer to the material listed in the bibliography.

3.4.2 Neural Network Operation

The output of each neuron is a function of its inputs. In particular, the output of

the jth neuron in any layer is described by two sets of equations:

(3.1)

(3.2)

For every neuron, j, in a layer, each of the i inputs, Xi, to that layer is multiplied by a

previously established weight, wij. These are all summed together, resulting in the internal

value of this operation, Uj. This value is then biased by a previously established threshold

value, tj, and sent through an activation function, Fth. This activation function is usually the

sigmoid function, which has an input to output mapping as shown in Figure 4. The resulting

output, Yj, is an input to the next layer or it is a response of the neural network if it is the last

layer. Neuralyst allows other threshold functions to be used in place of the sigmoid described

here.

Figure 3.5 Sigmoid Function

In essence, Equation 1 implements the combination operation of the neuron and

Equation 2 implements the firing of the neuron.

From these equations, a predetermined set of weights, a predetermined set of

Threshold values and a description of the network structure (that is the number of layers and

the number of neurons in each layer); it is possible to compute the response of the neural 11

network to any set of inputs. And this is just how Neuralyst goes about producing the

response. But how does it learn?

3.4.3 Neural Network Learning

Learning in a neural network is called training. Like training in athletics, training in a

neural network requires a coach, someone that describes to the neural network what it should

have produced as a response. From the difference between the desired response and the

actual response, the error is determined and a portion of it is propagated backward through

the network. At each neuron in the network the error is used to adjust the weights and

threshold values of the neuron, so that the next time, the error in the network response will be

less for the same inputs.

This corrective procedure is called back propagation (hence the name of the neural

network) and it is applied continuously and repetitively for each set of inputs and

corresponding set of outputs produced in response to the inputs. This procedure continues so

long as the individual or total errors in the responses exceed a specified level or until there

are no measurable errors. At this point, the neural network has learned the training material

and you can stop the training process and use the neural network to produce responses to new

input data.

Figure 3.6 Neuron Weight Adjustment

As you train the network, the total error, that is the sum of the errors over all the

training sets, will become smaller and smaller. Once the network reduces the total error to the

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limit set, training may stop. You may then apply the network, using the weights and

thresholds as trained.

[There is some heavier going in the next few paragraphs. Skip ahead if you don't need to

understand all the details of neural network learning.]

Back propagation starts at the output layer with the following equations:

(3.3)

(3.4)

For the ith input of the jth neuron in the output layer, the weight wij is adjusted by

adding to the previous weight value, w'ij, a term determined by the product of a learning

rate, LR, an error term, ej, and the value of the ith input, Xi. The error term, ej, for the jth

neuron is determined by the product of the actual output, Yj, its complement, 1 - Yj, and the

difference between the desired output, dj, and the actual output.

Once the error terms are computed and weights are adjusted for the output layer, the

values are recorded and the next layer back is adjusted. The same weight adjustment process,

determined by Equation 3, is followed, but the error term is generated by a slightly modified

version of Equation 4. This modification is:

(3.5)

In this version, the difference between the desired output and the actual output is

replaced by the sum of the error terms for each neuron, k, in the layer immediately

succeeding the layer being processed (remember, we are going backwards through the layers

so these terms have already been computed) times the respective pre-adjustment weights.

The learning rate, LR, applies a greater or lesser portion of the respective adjustment

to the old weight. If the factor is set to a large value, then the neural network may learn more

quickly, but if there is a large variability in the input set then the network may not learn very

well or at all. In real terms, setting the learning rate to a large value is analogous to giving a

child a spanking, but that is inappropriate and counter-productive to learning if the offense is

so simple as forgetting to tie their shoelaces. Usually, it is better to set the factor to a small.

This is similar to Equation 3, with a momentum factor, M, the previous weight, w'ij,

and the next two previous weight, w''ij, included in the last term. This extra term allows for

momentum in weight adjustment. Momentum basically allows a change to the weights to

persist for a number of adjustment cycles. The magnitude of the persistence is controlled by

13

the momentum factor. If the momentum factor is set to 0, then the equation reduces to that of

Equation 3. If the momentum factor is increased from 0, then increasingly greater persistence

of previous adjustments is allowed in modifying the current adjustment. This can improve the

learning rate in some situations, by helping to smooth out unusual conditions in the training

set.

As you train the network, the total error, that is the sum of the errors over all the

training sets, will become smaller and smaller. Once the network reduces the total error to the

limit set, training may stop. You may then apply the network, using the weights and

thresholds as trained.

It is a good idea to set aside some subset of all the inputs available and reserve them

for testing the trained network. By comparing the output of a trained network on these test

sets to the outputs you know to be correct, you can gain greater confidence in the validity of

the training. If you are satisfied at this point, then the neural network is ready for running.

Usually, no back propagation takes place in this running mode as was done in the

training mode. This is because there is often no way to be immediately certain of the desired

response. If there were, there would be no need for the processing capabilities of the neural

network! Instead, as the validity of the neural network outputs or predictions are verified or

contradicted over time, you will either be satisfied with the existing performance or

determine a need for new training. In this case, the additional input sets collected since the

last training session may be used to extend and improve the training data.

14

CHAPTER 4

BRAIN CHIP

Matthew Nagle’s brain chip was designed to provide a balance between safety,

durability, and functionality. The chip had to be small enough to not hinder normal brain

function and non-disruptive to neural communication to avoid brain damage. At the same

time, the chip had to be resistant to corrosion caused by brain chemicals. While fulfilling

these safety requirements, the primary function of the chip was to record and transmit the

delicate signals of Nagle’s brain.

Figure 4.1 chip inside the human brain

Brain chips often fail because of pinholes in their insulation coat. These

pinholes allow chemicals and fluid to come in direct contact with the sensitive circuitry of

the chip, which results in immediate failure of the chip. Thus the coating material of Nagle’s

brain chip was of utmost importance. Because of the size constraint, encapsulating the chip in

a thick layer of insulation was not a viable option. Instead, the chip implanted in Nagle was

coated in monolithic silicone. Its electrodes were coated withParalyne C, topped with

platinum tips and insulated with thin glass. The combination of these materials allowed the

chip to be small, durable, and efficient.

. Nagle’s chip recorded brain signals using integrated CMOS Circuitry, which

is an array of recording electrodes. Just like repeating an experiment ensures statistically

significant results, using multiple electrodes chemicals and fluid to come in direct contact

with the sensitive circuitry of the chip, which results in immediate failure of improved the

reliability of the recorded data. The chip was equipped with 96 recording electrodes spaced

0.4mm apart. It received data at the rate of10,000 signals per second per electrode. The end

15

size of the chip was 4mm x 4mm x 1.5mm and was implanted a little over 1mm into Nagle’s

brain.

4.1 Working of brain chipIn developing the BCI, researchers implanted brain chip in subjects with full

neural and motor capacity. The subjects performed elementary actions, such as raising an

arm, and the neuron activity was recorded using the chip. These experiments allowed for the

simultaneous recording of both hand motion and neural activity. Later in the trial, Nagle

developed the ability to open and close a prosthetic hand. All of his accomplishments were

exciting not only because of the physical successes, but also because of the manner in which

he was able to control the BCI.Like a healthy person, Nagle was able to do other things, such

as whistling or talking, while voluntarily “moving” an Matthew Nagle: Plugged

Inwww.wired.comthis exact location. After recovering from surgery, doctors thought that it

would take11 months for Nagle to learn how to control computer cursor using the

BrainGate®system. However, Nagle surprised everyone when he began to have success on

just his second day of training with the implanted BCI.

The Brain Gate Neural Interface created a direct link between Nagle’s brain

and a computer in the following way: when he thought “move cursor down,” his

Figure 4.2 Experiment on a person

Cortical neurons fired in a distinctive pattern. The brain chip sensed these electrical

signals Implementation The researchers were then able to create a relational model using the

two data sets. In addition, researchers discovered that although there are multiple sets of

neurons that determine the force and direction of motor action, the data from a small sample

of neurons can be reconstructed into full three-dimensional arm trajectories using simple

multiple linear regression. Researchers found that the placement of the brain chip did not

16

matter as much as originally thought. The chip was able to pick up neural signals not only

from the neurons that it directly touched, but also from important nearby neural clusters. This

suggests that users of the chip were able to learn how to use the BCI through signals

generated by the BCI itself. With prolonged use, the neurons in contact with the chip became

increasingly compatible and responsive in performing desired tasks.

In Nagle’s inaugural clinical trial, doctors first pinpointed the exact location in

Nagle’s primary motor cortex that once controlled his dominant hand. The chip was then

implanted at this exact location. After recovering from surgery, doctors thought that it would

take11 months for Nagle to learn how to control computer cursor using the

BrainGate®system. However, agle surprised everyone when he began to have success on just

his second day of training with the implanted BCI.

The Brain Gate Neural Interface created a direct link between Nagle’s brain

and a computer in the following way: when he thought “move cursor down,” his cortical

brain chip sensed these electrical signals and transmitted them to a pedestal plug cursor using

the BrainGate®system. However, agle surprised everyone when he began to have success on

just his second day of training with the implanted BCI that was directly attached to his

skull. The signal was then sent through a wire to an amplifier, where it was converted into

optical data and sent to a computer through fiber-optic cables. The Brain Gate® system

decoded the data associated with Nagle’s thoughts into the specified movement of the

computer cursor. Thus Nagle was able to play computer games, check email, and draw using

Brain Gate object, in his case a cursor or a prosthetic hand. In other words, the BCI did not

require single-focus concentration.

Furthermore, using the Brain Gate® system became intuitive for Nagle.

Rather than thinking about the process of moving a cursor by moving his hand, he eventually

started moving the cursor by simply imagining the cursor going from place to place. Brain

experimentation is a risky procedure where minor errors or mechanical malfunctions can lead

to permanent damage or even death The cursor became as much a part of Nag leas his arms

and legs once were. The Brain Gate® system decoded the data associated with Nagle’s

thoughts into the specified movement of the computer cursor

Researchers removed Nagle’s brain chip after one year of observation.

Because of the brevity of his trial, it is unknown whether transmitting signals from an

implanted chip causes brain damage. Brain experimentation is a risky procedure where minor

errors or mechanical malfunctions can lead to permanent damage or even death. Extended 17

research beyond the scope of Nagle’s study is necessary to determine the long-term effects of

the chip

.

4.2 Brain cells and silicon chip linked electronically

Fig-4.3 The Max Planck Institute grew this 'snail' neuron atop an Infineon

Technologies CMOS device that measures the neuron's electrical activity, linking chips and

living cells.

One of the toughest problems in neural prosthetics is how to connect chips and real

neurons. Today, many researchers are working on tiny electrode arrays that link the two.

However, once a device is implanted the body develops so-called glial cells, defenses that

surround the foreign object and prevent neurons and electrodes from making contact.

In Munich, the Max Planck team is taking a revolutionary approach:

interfacing the nerves and silicon directly. "I think we are the only group doing this,"

Fromherz said.

Fromherz is at work on a six-month project to grow three or four neurons on a

180 x 180-transistor array supplied by Infineon, after having successfully grown a single

neuron on the device. In a past experiment, the researcher placed a brain slice from and

transmitted them to a pedestal plug cursor using the BrainGate®system. However, agle

surprised everyone when he began to have success on just his second day of training with the

implanted BCI that was directly attached to his skull. The signal was then sent through a

wire to an amplifier, where it was converted in a Plexiglas container with electrolyte at 37

degrees C. In a few days dead tissue fell away and lives nerve endings made contact with the

chip full three-dimensional arm trajectories using simple multiple linear regression.

Researchers found that the placement of the brain chip did not matter as much as originally

thought.

18

Their plan is to build a system with 15,000 neuron-transistor sites--a first step toward

an eventual computational model of brain activity.

4.3 Achievements in the fieldThe achievements in the field of implantable chips, bio-chips, so far are

significant. Some of them are mentioned below:

4.3.1 Brain “pacemakers”

Figure 4.4 Pacemaker in the brain

Researchers at the crossroads of medicine and electronics are developing

implantable silicon neurons that one day could carry out the functions of a part of the brain

that has been damaged by stroke, epilepsy or Alzheimer's disease.

The U.S. Food and Drug Administration have approved implantable

neurostimulators and drug pumps for the treatment of chronic pain, spasticity and diabetes,

according to a spokesman for Medtronic Inc. (Minneapolis). A sponsor of the Capri

conference, Medtronic says it is already delivering benefits in neural engineering through its

Active therapy, which uses an implantable neurostimulator, commonly called a brain

pacemaker, to treat symptoms of Parkinson's disease.

Surgeons implant a thin, insulated, coiled wire with four electrodes at the tip,

and then thread an extension of that wire under the skin from the head, down the neck and

into the upper chest. That wire is connected to the neurostimulator, a small, sealed patient-

controlled device that produces electrical pulses to stimulate the brain. These implants have

helped patients suffering from Parkinson’s disease to a large extent that was directly attached

to his skull. The signal was then sent through a wire to an amplifier, where it was converted

19

in a Plexiglas container with electrolyte at 37 degrees C. In a few days dead tissue fell away

and lives nerve endings made contact with the

4.3.2 Retinomorphic chips

The famed mathematician Alan Turing predicted in 1950 that computers would match

wits with humans by the end of the century. In the following decades, researchers in the new

field of artificial intelligence worked hard to fulfill his prophecy, mostly following a top-

down strategy: If we can just write enough code, they reasoned, we can simulate all the

functions of the brain. The results have been dismal. Rapid improvements in computer power

have yielded nothing resembling a thinking machine that can write music or run a company,

much less unlock the secrets of consciousness. Kwabena Boahen, a lead researcher at the

University of Pennsylvania's Neuro engineering Research Laboratory, is trying a different

solution. Rather than imposing pseudo-smart software on a conventional silicon chip, he is

studying the way human neurons are interconnected. Then he hopes to build electronic

systems that re-create the results. In short, he is attempting to reverse-engineer the brain from

the bottom up.

Fig 4.5 Computer chip model of neural function for implanted brain prostheses

· Boahen and his fellow neuromorphic engineers are now discovering that the

brain's underlying structure is much simpler than the behaviors, insights, and feelings it

incites. That is because our brains, unlike desktop computers, constantly change their own

connections to revamp the way they process information. "We now have microscopes that

can see individual connections between neurons. They show that the brain can retract

connections and make new ones in minutes. The brain deals with complexity by wiring itself

up on the fly, based on the activity going on around it," Boahen says. That helps explain how

three pounds of neurons, drawing hardly any more power than a night-light, can perform all

the operations associated with human thought.

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The first product from Boahen's lab is a retinomorphic chip, which he is now

putting through a battery of simple vision tests. Containing nearly 6,000 photoreceptors and

4,000 synthetic nerve connections, the chip is about one-eighth the size of a human retina.

Just as impressive, the chip consumes only 0.06 watt of power, making it roughly three times

as efficient as the real thing. A general-purpose digital computer, in contrast, uses a million

times more energy per computation as does the human brain. "Building neural prostheses

requires us to match the efficiency, not just the performance, of the brain," says Boahen. A

retinal chip could be mounted inside an eyeball in a year or two, he says, after engineers

solve the remaining challenges of building an efficient human-chip interface and a compact

power supply.

This artificial eye contains working electronic versions of the four types of

ganglion cells in the retina. The cumbersome array of electronics and optics surrounds an

artificial retina, which is just one-tenth of an inch wide.

Remarkable as an artificial retina might be, it is just a baby step toward the big

objective—reverse-engineering the brain's entire ornate structure down to the last dendrite. A

thorough simulation would require a minutely detailed neural blueprint of the brain, from

brain stem to frontal lobes.

4.3.3 The mental mouse

Figure 4.6 A receiver placed on back of the rat to control its movements by the chip

which is placed inside its brain

Dr. Philip R. Kennedy, an [sic] clinical assistant professor of neurology at

Emory University in Georgia, reported that a paralyzed man was able to control a cursor with

a cone-shaped, glass implant. Each [neurotrophic electrode] consists of a hollow glass cone

about the size of a ball-point pen tip. The implants…contain an electrode that picks up

impulses from the nerve endings. Before they are implanted, the cones are coated with

chemicals — taken from tissue inside the patients’ own knees — to encourage nerve growth.

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The implants are then placed in the brain’s motor cortex — which controls body movement

— and over the course of the next few months the chemicals encourage nerve cells to grow

and attach to the electrodes. A transmitter just inside the skull picks up signals from the

process information. "We now have microscopes that can see individual connections between

neurons. They show that the brain can retract

4.4 Benefits of the technology The future may well involve the reality of science fiction’s cyborg, persons

who have developed some intimate and occasionally necessary relationship with a machine.

It is likely that implantable computer chips acting as sensors, or actuators, may soon assist

not only failing memory, but even bestow fluency in a new language, or enable “recognition

“of previously unmet individuals. The progress already made in therapeutic devices, in

prosthetics and in computer science indicates that it may well be feasible to develop direct

interfaces between the brain and computers.

Computer scientists predict that within the next twenty years neural interfaces

will be designed that will not only increase the dynamic range of senses, but will also

enhance memory and enable “cyber think”-invisible communication with others. This

technology will facilitate consistent and constant access to information when and where it is

needed.

The linkage of smaller, lighter, and more powerful computer systems with radio

technologies will enable users to access information and communicate anywhere or anytime.

Through miniaturization of components, systems have been generated that are wearable and

nearly invisible, so that individuals, supported by a personal information structure, can move

communally based data source.

As intelligence or sensory "amplifiers", the implantable chip will generate at least

four benefits:

1) It will increase the dynamic range of senses, enabling, for example, seeing IR, UV, and

chemical spectra;

2) It will enhance memory;

3) It will enable “cyber think”-invisible communication with others when making decisions.

4) It will enable consistent and constant access to information where and then it is needed

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For many these enhancements will produce major improvements in the quality of life,

or their survivability, or their performance in a job. The first prototype devices for these

improvements in human functioning should be available in five years, with the years of the

Figure 4.7 Some of the working modules of brain chip

Figure 4.8 Person to person communication with the help of brain chip

Military prototypes starting within ten years, and information workers using

prototypes within fifteen years, general adoption will take roughly twenty to thirty years.

Figure 4.9 In a clinical trial; a woman used a brain-chip system to control a robot

arm with her thooughts and reach for a drink of coffee

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Function as a prosthetic cortical implant. The user's visual cortex will receive

stimulation from a computer based either on what a camera sees or based on an artificial

"window" interface.

Giving completely paralyzed patients full mental control of robotic limbs or

Communication devices obvious and basic problems involve safety. Evaluation of the costs

and benefits of these implants requires a consideration of the surgical and long term risks.

From a computer based either on what a camera sees or based on an artificial one question,

has long been a dream of those working to free such individuals from their locked-in state.

Now this dream is on the verge of reality.

4.5 Drawbacks of the technologyEthical appraisal of implantable computer chips should asses at least the following

areas of concern; issues of safety and informed consent, issues of manufacturing and

scientific responsibility ,anxieties about the psychological impacts of enhancing human

nature, worries about possible usage in children, and most troublesome, issues of privacy and

autonomy. As is the case in evaluation of any future technology, it is unlikely that we can

reliably predict all effects. Nevertheless, the potential for harm must be considered. The most

obvious and basic problems involve safety. Evaluation of the costs and benefits of these

implants requires a consideration of the surgical and long term risks. One question, —

whether the difficulties with development of non-toxic materials will allow long term usage?

— should be answered in studies on therapeutic options and thus, not be a concern

for enhancement usages. However, it is conceivable that there should be a higher standard for

safety when technologies are used for enhancement rather than therapy, and this issue needs

public debate. Whether the informed consent of recipients should be sufficient reason

for permitting implementation is questionable in view of the potential societal impact.

Other issues such as the kinds of warranties users should receive, and the liability

responsibilities if quality control of hard/soft/firmware is not up to standard, could be

addressed by manufacturing regulation. Provisions should be made to facilitate grade since

users presumably would not want multiple operations, or to be possessors of obsolete

systems. Manufacturers must understand and device programs for teaching users how to

implement recipient usefulness, and whether all users benefit equally. Additional practical

problems with ethical ramifications include whether there will be a competitive market in

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such systems and if there will be any industry-wide standards for design of the technology.

augmentedsensory capacities, the implications, even if positive, need consideration.Supersen

sory sight will see radar, infrared and ultraviolet images, augmented hearing will detect softer

and higher and lower pitched sounds, enhanced smell will intensify our ability to discern

scents, of touch will enable discernment of environmental stimuli like changes in barometric

pressure.

These capacities would change the "normal" for humans, and would be of

exceptional application in situations of danger, especially in battle. As the numbers of

enhanced humans increase, today's normal range might be seen as subnormal, leading to the

lexicalization of another area of life. Thus, substantial questions revolve around whether

there should be any limits placed upon modifications of essential aspects of the human

species. Although defining human nature is notoriously difficult, man's rational powers have

traditionally been viewed as his claim to superiority and the center of personal identity.

 On the other hand, not all philosophers espouse the materialist contention and use of

these technologies certainly will impact discussions about the nature of personal identity, and

the traditional mind-body problem. Modifying the brain and its powers could change our

psychic states, altering both the self-concept of the user, and our understanding of what it

means to be human. The boundary between me "the physical self" and me "the

precatory/intellectual self" could change as the ability to perceive and interact expands far

beyond what can be done with video conferencing. The boundaries of the real and virtual

worlds may blur, and a consciousness wired to the collective and to the accumulated

knowledge of mankind would surely impact the individual's sense of self. Whether this

would lead to bestowing greater weight to collective responsibilities andwhether this would

be beneficial are unknown.

Changes in human nature would become more pervasive if the

altered consciousness were that of children. In an intensely competitive society, knowledge is

often power. Parents are driven to provide the very best for their children. Will they be able to

secure implants for their children, and if so, how will that change the

e inequalities produced might create a demand for universal coverage of these devices in

health care plans, further increasing costs to society. However, in a culture such as ours, with

different levels of care available on the basis of ability to pay, it is plausible to suppose

investment, and that this will further widen the gap between the haves and the have-not, and

genders, but also, between rich and poor nations. As enhancements become more widespread, 25

enhancement becomes the norm, and there is increasing social pressure to avail oneself of the

"benefit." Thus, even those who initially shrink from the surgery may find it becomes a

necessity, and the consent part of "informed consent” would become subject to manipulation.

Beyond these more imminent prospects is the possibility that in thirty years, "it will be

possible to capture data presenting all of a human being's sensory experiences on a single tiny

chip implanted in the brain." This data would be collected by biological probes receiving

electrical impulses, and would enable a user to recreate experiences, or even to transplant

memory chips from one brain to another. In this eventuality, psychological continuity

personal identity would be disrupted with indisputable ramifications. Would the resulting

person have the identities of other persons?

The most frightening implication of this technology is the grave possibility that it

would facilitate totalitarian control of humans. In a prescient projection of experimental

protocols, George Anna’s writes of the "project to implant removable monitoring devices at

the base of the brain of neonates in three major teaching hospitals. The devices would not

only permit us to locate all the implanters at any time, but could be programmed in the future

to monitor the sound around them and to play subliminal messages directly to their brains."

Using such technology governments could control and monitor citizens. In a free society this

may military environment the advantages of augmenting capacities to createsoldiers with

faster reflexes, or greater accuracy, would exert strong pressures for requiring enhancement.

When implanted computing and communication devices with interfaces to weapons,

information, and communication systems become possible, the military of the democratic

societies might require usage to maintain a competitive advantage. Mandated implants for

criminals are foreseeable possibility even in democratic societies. Policy decisions will arise

the base of the brain of neonates in three major teaching hospitals. The devices would not

only permit us to locate all the implanters at any time, but could be programmed in the future

about this usage, and also about permitting usage, if and when it becomes possible, to affect

specific behaviors. A paramount worry involves who will control the technology and what

will be programmed; this issue overlaps with uneasiness about privacy issues, and the need

for control and security of communication links. Not all the countries of the

world prioritize autonomy, and the potential for sinister invasions of liberty and privacy are

alarming. Nobody seems to intuitively have a problem with implantable devices for the blind,

deaf, and impaired. However, biochips may become a (literal) invasion of privacy.

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4.6 Challenges faced by scientists A researcher at Johns Hopkins University is using a collection of VLSI chips

to confirm new insights into how the no cortex of the human brain unites information from

the senses to create a coherent picture of the orls.Nndrea Andreou of the university's

Department of Computer Science and Electrical engineering has wire the chips together with

optoelectronic connections to build an image-processing module modeled on Boston

University neural theorist Stephen Grossberg's latest into brain function.

Grossberg recently proposed what might be described as a "net-centric “view

of brain operation in which the communication channels between the brain's processing

modules perform a crucial blending of different perceptual units. This view is essentially

different from the conventional model that likens brain operation to parallel pro censors

found in digital computers or analog distributed processing networks. Andreou is convinced

that the shift in emphasis from processor to network holds the key to solving some of the

difficult problems facing computer scientists. "Despite the phenomenal success in

engineering rudimentary ears, eyes and noses for computers, our progress has not generalized

to more complex system and harder tasks".

Andreou said in a presentation at the Critical Technologies for the Future of

Computing conference, held last month in San Diego. It is at the neocortex level of

information processing, where sensed information is assembled into a full picture that current

technology seems to run into a brick wall.

The greatest challenge has been in building the interface between biology and

technology. Nerve cells in the brain find each other, stregthen connections and build patterns

through complex chemical signaling that is driven in part by the environment. Also, in a

stroke patient, whose cells are dying, we need to get surviving neurons to choose to interface

with silicon chip. We also need to make the neural interface stable, so that walking around or

nodding doesn’t disrupt the connection.

Another challenge is to give completely paralyzed patients full mental control over

robotic limbs or communication devices. The brain waves of such a person are very weak to

accomplish this task. Decreaing the size of the chip so that it can be implanted

subcutaneously, is yet another challaenge.This will help the patient to adopt though the

implant more eaily.In July 1996,information was released on research currently taking place

into creation of computer chip called the “soul Catcher 2025”,Dr.Chris Winter and a team of

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scientists at British Telecom’sMartlehamHeath Laboratories, near Ipswich, are developing a

chip that, when placed into the skull behind the eye, will record all visual and physical

sensations, as well as thoughts.

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CHAPTER 5

CONCLUSIONS

"Neuroscience," wrote author Tom Wolfe in Forbes magazine a couple years

ago, "is on the threshold of a unified theory that will have an impact as powerful as that of

Darwinism a hundred years ago.

“Wolfe is wowed by the combination of powerful imaging and tracking

technologies that now allow scientists not only to watch the brain assist functions not only to

identify centers of sensation “lighting up” in response to stimuli, but to track a thought as it

proceeds along neural pathways and traverses the brains cape on its way to the great cerebral

memory bank, where it queues up for short- or long-term storage. Now that you know what

condition your condition is in, you know that such devices are only a stopgap measure at

best in the evolutionary story. The implants you get may enhance our capabilities, but they

will expire when you do, leaving the next generation unchanged.

As we become more dependent on biotechnology, the standards of what is "alive"

will be up for grabs. Take a look at The Tissue Culture and Art Project's semi living worry,

cultured in a bioreactor by growing living cells on artificial scaffolds, or the Pig Wings

project, which explores if  pigs could fly.

Deciding who or what, exactly, is human will be an incendiary issue in the years to

come as our genetic engineering technologies progress and we go beyond implantable to

actual germ-line genetic modification. We are already creating chimerical creatures by

combining genes from different species. We will try to engineer improved human beings--

not because we're so concerned about the intelligent machine life we are creating, but

because we’re human, and it's embedded in our nature to explore, tinker, and create.

It will be several years before we see a practical application of the technology we’ve

discussed. Let’s hope such technologies will be used for restoring the prosperity and peace of

the world and not to give the world a devastating end. 

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REFERENCES

[1] Arbib M, The Handbook of Brain Theory and Neural Networks. Massachusetts:The

MIT Press, 1995.

[2] H. Abdi, D. Valentin, B. Edelman, Neural Networks, Thousand Oaks, CA: SAGE

Publication Inc., 1999.

[3] Kohonen T, Self-Organizing Maps, Third Edition. Berlin, Heidelberg, Springer, 2001.

[4] Edvinsson L, Krause D. N, Cerebral Blood Flow and Metabolism, Second Edition.

Philadelphia, Lippincott Williams & Wilkins, 2002.

[5] R. Sharma, V.I Pavlovic and T.S. Huang, “Toward multimodel human-computer

Interface”, Proc.IEEE,vol.86,no.5,pp.853-869,May 1998.

[6] http://www.conspiracyarchive.com/NWO/microchip_implants_mind_control.htm

[7] http://www.mindfully.org/Technology/2004/Brain-Chip-Cyberkinetics19apr04.htm

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