research paper

1. Artificial Brain:A step towards Imortality
Dr Shilpa Sharma1
* and Mrugank Narendra Nimje1
and Chanpreet Singh1
*and Bhavesh Kumawat1
and Utkarsh1
and Swastik
Suman Sahoo1
and Puneet Thapar1
and Korada Saketh Gupta
1
Lovely Professional University, Phagwara, Jalandhar, 144411
2
Sherub Gatshel Middle Secondary School, Samtse Ministry of Education and Skill Development, Bhuta
Abstract—Technology that aims to produce computational
models of the brain and its activities is represented by artificial
brains. These models can take many different forms, from
straightforward simulations of certain brain regions to intricate
neural networks meant to simulate the behavior of the entire brain.
Artificial brains have a wide range of possible uses, from
enhancing our comprehension of the brain and brain illnesses to
creating new types of robotics and artificial intelligence. But this
technology also has important ethical and cultural ramifications,
including concerns about privacy, autonomy, and the possibility of
abuse. The objective of this study is to investigate the current
status of artificial brain technology, its possible advantages and
disadvantages, and the ethical issues that need to be considered
when developing and implementing this technology.
Keywords—Artificial Brain, Technology, review, Artificial
Intelligence
I. INTRODUCTION
An Artificial Brain is a term used to describe the creation of
machines that can perform tasks that require cognitive abilities
similar to those of humans. The primary goal behind this concept is
to develop machines that can mimic the functionality and
complexity of the human brain, which includes tasks such as
perception, reasoning, decision-making, and learning.
The concept of Artificial Brain originates from the field of Artificial
Intelligence (AI), which focuses on creating The field of artificial
intelligence is revolving around the development of intelligent
machines that can complete tasks that normally require human-level
intelligence..[1]. Developing an Artificial Brain is a significant step
forward in the advancement of AI because it would allow machines
to process information more efficiently, learn from experience, and
make more accurate predictions. There are various approaches to
creating an Artificial Brain, such as machine learning, deep
learning, and neural networks. These methods involve training
machines to recognize patterns, process data, and make decisions
based on the information they receive. Although the field is not yet
well-established, it holds the capacity to bring about a significant
transformation in several industries, including healthcare, finance,
and robotics. Further advancements in technology and research
could lead to the development of machines that can think and learn
like humans. Ultimately, this could lead to significant
improvements in many fields, making our lives easier and more
productive.
II. LITERATURE REVIEW
A. BLUE BRAIN :
IBM is working on developing the Blue Brain, which is a virtual
brain that has the potential to be the first of its kind. This technology
would allow people to transfer their consciousness into computers,
giving artificial brains the ability to think and act like human brains.
The process involves using a powerful supercomputer with a large
storage capacity and an interface that allows data to be uploaded
from a natural brain into a computer. This would enable knowledge
and intelligence to be preserved and utilized even after an
individual's death. However, there are many technical and ethical
challenges that must be overcome to achieve this goal.
B. VIRTUAL BRAIN :
The virtual brain is an artificial intelligence that has the potential to
imitate the cognitive functions of a natural brain. This means that it
has the capacity to think, artificial intelligence systems are capable
of making decisions based on past experiences and responding
similarly to the human brain. The development of a virtual brain
also raises the possibility of preserving the knowledge and
intelligence of an individual even after their death. The ability to
upload and store a person's consciousness in a virtual brain means
that their experiences and insights can be kept and utilized long
after they have passed away. However, it is important to note that
creating a virtual brain is a complex and challenging task that
involves many technical and ethical considerations. As of now, the
technology required to achieve this goal is still in its early stages of
development and further research and experimentation will be
required to fully understand its potential benefits and risks.
C. POSSIBILITY :
There are two primary techniques for transferring an individual's
consciousness to a computer: invasive and non-invasive methods.
In his paper, Raymond Kurzweil proposed that nanobots could be
the most viable approach. These nanobots are microscopic robots
capable of navigating through the human bloodstream to reach the
brain and spine. They can monitor the central nervous system's
structure and activity while providing a close connection to
computers without requiring the person to abandon their biological
form. Nanobots can scan the brain's structure and capture the
connections between neurons to create a detailed digital copy of
the person's consciousness, which requires a computer with
extensive storage space and processing power to handle the vast
amount of data generated [2]. While the idea of uploading
consciousness into a computer is still in its early stages, it has the
potential to provide significant benefits, including preserving
knowledge and intelligence beyond the physical body's lifespan.
Nonetheless, there are ethical considerations related to transferring
human consciousness into an artificial form, and further research is
needed to comprehend this technology's possibilities and
limitations.
D. NEED FOR VIRTUAL BRAIN :
Intelligence is a natural ability that some individuals possess,
allowing them to think and comprehend beyond the average
person. Society values intelligence highly, as it enables us to make
progress and advancements in various fields. However, when a
person dies, their intelligence and knowledge are lost forever. The
concept of a virtual brain offers a potential solution to this
problem. By uploading our consciousness into a computer, we can
preserve our intelligence and knowledge beyond the lifespan of our
physical bodies. This would allow us to continue to exist and
potentially contribute to society long after our biological death.
Furthermore, technology such as a virtual brain could also offer
practical benefits in our daily lives. For example, it could serve as
a memory aid, helping us to remember important dates, facts, and
details that we might otherwise forget due to our busy lives. It

2. could also potentially serve as a personal assistant, providing
guidance and support as needed.
However, the idea of uploading ourselves onto a computer raises
significant ethical questions and potential consequences that need
to be carefully considered. It is crucial to fully understand the
implications of such technology before implementing it in any
significant way. Further research and development are necessary to
explore the possibilities and limitations of a virtual brain.
E. NATURAL BRAIN :
1. KNOW ABOUT HUMAN BRAIN :
The human brain is vital in processing sensory
information from the body's neurons and generating
signals that instruct the body to react. It comprises three
main divisions: the cerebrum, cerebellum, and brainstem.
Electrical impulses transmitted through the nervous
system allow us to feel, interpret, and perceive. The
nervous system is an intricately organized electronic
mechanism that operates through three basic functions:
sensory input, integration, and motor output.
Sensory input:
The sensory cells, or neurons, in our body receive
information from the environment through our
senses.This information is transmitted through electric
impulses to the brain.
Integration:
The brain integrates the information received from the
sensory neurons to form a coherent perception of the
environment.
This involves the cooperation of many neurons in
different areas of the brain.
Motor output:
The neurons in the brain transmit signals to effecter cells,
such as muscles or glands, which produce a response to
the external stimuli. This response can manifest as bodily
functions, movement, or secretion, among other actions.
2. FOUR SENSE ORGANS :
Nose:
The process of smelling begins when odor molecules
enter the nose and travel to the olfactory bulb, which
consists of sensory nerves. From there, nerve impulses
are transmitted to the smell sensory cortex of the brain,
where the odor is analyzed, recognized, and stored in
memory.
Eye:
When we look at an object, the lens in our eye makes it
appear larger. This image is then detected by specialized
cells in the retina located at the back of the eye. The
retina then converts the image into electrical signals that
are transmitted via the optic nerve to the brain, where
they are processed and stored.
Tongue:
Tiny taste receptors called taste buds on the tongue are
responsible for categorizing the taste of everything we
eat or drink into four types, which are then converted
into nerve signals and transmitted to the brain for
processing and identification.
Ear:
When sound waves enter the ear, they are divided into
pitches in the cochlea, which is then measured by the
Corte. The vibration information is then transmitted to a
nerve and sent to the brain for interpretation and
memory.
3. FUNCTIONALITY OF BLUE BRAIN:
Input:
A silicon chip has been developed by scientists to create
artificial neurons, which can receive inputs from sensory
cells in a way that resembles the functioning of natural
neurons in the brain. The electrical signals from sensory
cells can be transmitted to the artificial neurons, which
can then send the information to a supercomputer for
further processing and interpretation.
Interpretation:
A set of registers can be used to interpret the electric
impulses received by the artificial neuron.
The different values in these registers represent different
states of the brain.
Output:
The artificial neurons in the body can receive output
signals based on the states of the register, and these
signals will be detected by the sensory cells.
Memory:
Information can be permanently stored in secondary
memory to maintain the required states of the registers
and retrieved and utilized as needed.
Processing:
Computers can make decisions based on stored data and
received input, utilizing arithmetic and logical operations
to aid in the decision-making process.
F. DIFFERENCE BETWEEN NATURAL AND
SIMULATED BRAIN :
Natural Brain Simulated Brain
Input
Sensory cells produce electric
impulses that are received by
neurons, which then transmit
them to the brain.
Input
Artificial neurons created by
replacing biological ones with
silicon chips can receive input
from sensory cells.
Interpretation
The brain interprets electrical
impulses received from neurons
through specific states of
neurons.
Interpretation
The electrical impulses
received by artificial neurons
can be interpreted using
registers. The values stored in
these registers represent
different states of the brain.
Output
The brain sends electric
impulses based on neuron states,
received by sensory cells for a
corresponding response. The
specific body part responding
depends on neuron states at the
time.
Output
The output signal can be sent
to artificial neurons in the
body based on the states of the
register, which will then be
received by the sensory cells.
Memory
Repetitive and intense activation
of certain neurons in our brain
can lead to the representation of
certain states permanently,
enabling us to remember things.
This happens both explicitly and
implicitly for important or
interesting matters.
Memory
Permanent storage of data is
achievable through secondary
memory, and similarly, the
required states of registers can
be stored permanently for later
retrieval and use.

3. Processing Processing
The process of decision-
making in computers involves
the use of stored states,
received input, and performing
logical and arithmetic
calculations to generate an
output.
When making decisions,
thinking or computing, our
neural circuitry uses logical and
arithmetic calculations. It
utilizes stored experiences and
current inputs to change the
states of certain neurons to
generate an output.
III. METHODOLOGY
WORKING OF BLUE BRAIN PROJECT
A. GOALS AND OBJECTIVES:
The Blue Brain Project is dedicated to comprehending brain
function and dysfunction by simulating the mammalian brain in
detail. Its primary objectives are to accumulate all existing
knowledge of the brain, expedite global research efforts, and
establish a theoretical framework for rebuilding the brain into
computer models from genetic to whole-brain levels [3]. The
project aims to enhance our comprehension of brain functions and
malfunctions and allow for information-based personalized
medicine. The project had set a deadline to achieve this goal by
2015.
B. STRUCTURE OF BLUE GENE:
The Blue Gene/L supercomputer was developed by IBM and is
known for its efficient design and high computational power. Its
system-on-a-chip technology allows for multiple processing cores
to be integrated into a single chip, reducing power consumption
and increasing efficiency. The Blue Gene/L's dual-core PowerPC
440 processors, each with a 64-bit floating-point unit, provide
high-performance computing capabilities.
The Blue Gene/L has different modes of operation, including co-
processor mode and virtual node mode, which allow for different
combinations of processing and I/O capabilities. In virtual node
mode, both CPUs are used for computation, providing even greater
performance capabilities.
The Blue Brain Project, a neuroscience project aimed at
stimulating the human brain, has used the Blue Gene/L
supercomputer to run simulations and analyze data. The 4-rack
system used in the project had a peak performance of 22.4
TFLOPS, making it one of the most powerful supercomputers in
the world at the time.
Overall, the Blue Gene/L is a powerful and efficient
supercomputer that has been used in a variety of scientific research
projects. Its advanced technology and high-performance
capabilities have contributed to advancements in fields such as
neuroscience, climate modeling, and molecular dynamics.
Fig 1. The Blue Gene/L supercomputer architecture
C. CONSTRUCTION OF MICROCIRCUIT:
This Project aims to construct precise models of the mammalian
brain at a cellular level, which requires detailed modeling of
neurons and their synaptic connections. The project aims to map
the 3D morphology, ion channel composition, electrical properties,
and distribution of different types of neurons, as well as the
statistics and properties of the various types of synapses connecting
these neurons.
Precisely simulating these neurons necessitates a considerable
amount of information on the physiology and pharmacology of
various types of synapses, the statistical connections among
different neuron types, and the patterns of innervation between
presynaptic and postsynaptic neurons. Since these neurons receive
input from numerous other neurons, it is necessary to represent
their intricate dendrite trees with tens of thousands of
compartments.
The Blue Brain Project is a significant endeavor, requiring a large
amount of computational power and data to create precise models
of the mammalian brain. These models have the potential to
revolutionize our understanding of the brain, leading to novel
approaches in personalized medicine.
Fig 2. Elementary building blocks of neural microcircuits.

4. This paragraph discusses a project aimed at reconstructing the
neural circuitry of the neocortical column, which is a fundamental
unit of the neocortex. The neocortex is a critical part of the brain
responsible for sensory perception, spatial reasoning, and
conscious thought. The project involves using a multi-neuron
patch-clamp technique to record the activity of thousands of
neocortical neurons and their synaptic connections in the somatic
sensory cortex of 14-16-day-old rats. This region of the brain is
particularly accessible, making it a useful model for this research.
The raw data obtained from the experiment is currently stored in
the lab's databases, and efforts are being made to make it publicly
available in a freely accessible database.
The team working on the Blue Brain Project has made significant
progress in refining the blueprint of the circuit, enabling them to
start reconstructing it at the cellular level using highly precise data
available for rats at a specific age. The project's starting point is a
sensory region that includes a layer that plays a crucial role in
receiving input to the neocortex from the thalamus, which is
essential for later calibration with in vivo experiments. To ensure
the accuracy of the reconstruction, it is necessary to choose a
region that is not too specialized, such as the barrel cortex, which
provides a significant advantage.
One of the challenges that the project faces is the difficulty in
recording detailed dendrites of pyramidal cells. Pyramidal cells
make up a significant percentage of the neurons in the neocortical
column but recording their dendrites is challenging in smaller
species like mice. The accompanying image displays the
microcircuit in different reconstruction stages, with the red color
representing the dendrite and blue color representing the axonal
arborizations. The columnar structure in the image demonstrates
the definition layers of the neocortical column.
Fig 3. Recreating the neocortial column
D. SIMULATION OF MICROCIRCUIT:
The following passage elaborates on the next phase of the project,
which entails enabling the microcircuit to function. After
reconstructing the microcircuit through a multi-neuron patch-
clamp method, the aim now is to simulate the electrical activity in
each neuron when stimulated through intricate mathematical
equations. This involves utilizing all 8192 processors of the Blue
Gene in a massively parallel computation while utilizing inter-
processor communication (MPI) to transmit the results as the
electrical impulse moves from one neuron to the next.
At present, simulating the circuit takes significantly longer than the
actual biological time, with one second of biological time requiring
much more than one second of simulation time. The current
simulation time is approximately two orders of magnitude greater
than the biological time. The Blue Brain Project team is focused on
optimizing the computation process to reduce the time required for
simulation, with the goal of achieving real-time functionality. This
means that the circuit should be able to simulate one second of
activity in one second of real time.
E. DECODING THE OUTPUT:
The Blue Brain simulation generates an enormous amount of data
that requires extensive analysis. Individual neurons and network
activity generate hundreds of gigabytes of data per second of
simulation, which can be challenging to process and manage. To
handle such large amounts of data, the Blue Brain team uses
massively parallel computers for both server-side and online
analysis of experimental data. This approach allows the team to
perform data analysis in real-time, which is crucial for identifying
areas of interest and validating simulation results.
To help researchers visualize the Blue Brain data, architects at
EPFL developed a sophisticated visualization interface that uses a
different supercomputer to render the data into a 3D visual
representation of the column. This interface allows researchers to
observe the electrical activity in neurons and the network
phenomena in real-time. However, the process of visualizing
neurons' shapes is computationally intensive and requires
rendering a high-quality mesh of a column consisting of 10,000
neurons has been created, which includes 1 billion triangles and
100GB of management data. Furthermore, the simulation data for
each neuron, with a resolution of electrical compartments, adds an
additional 150GB.
Despite the challenges, the visualization interface is a valuable tool
for identifying areas of interest and comparing simulation results
with experimental data. Researchers can use visual representation
to analyze single-cell activity and network phenomena quickly,
which helps to streamline the research process and improve the
overall efficiency of the Blue Brain Project.
F. DATA TRANSFORMATION PIPELINE:
The process of constructing the Blue Column involves multiple
steps that are performed sequentially, with each step building upon
the previous one. It commences with an analysis of the three-
dimensional morphology of individual neurons to identify any
discrepancies resulting from the in vitro preparation and
reconstruction. The next step is to input the corrected neurons into
a database where statistics are compiled for various anatomical
categories of neurons [5]. This compiled data is then utilized to
create an infinite number of neurons within each category, which
captures their full morphological range.
After the neurons have been cloned and their morphologies have
been refined, the next step involves adding ion channel models to
each neuron. This produces a range of electrical types for the
neurons. This enables the specific electrical properties of each
neuron to be captured. This entire process is repeated for each
neuron, leading to the creation of a database that contains
functionally unique neurons.
The imported 3-d neurons are subsequently integrated into Blue
Builder, a software tool that organizes neurons into their
corresponding layers based on a predetermined recipe that
specifies the number and proportion of neurons. A collision
detection algorithm is employed to calculate the structural
arrangement of axon-dendrite contacts, and the neurons are
adjusted until the structural touches are consistent with
experimentally derived data. Connectivity probabilities among
various types of neurons are taken into consideration to determine

5. which neurons should be connected, and all axon-dendrite contacts
are transformed into synaptic connections.
After the synaptic connections are established, the synapses are
assigned functional properties according to the statistical variation
of each class of synaptic connection. This involves specifying the
strength, location, and timing of the synapses, which determine
how signals are transmitted between neurons. By modeling these
properties based on experimental data, the Blue Brain team can
simulate the electrical activity of the microcircuit with a high
degree of accuracy.
To simulate transmission in the Blue Column, dynamic synaptic
models are employed, along with synaptic learning algorithms to
enable plasticity. This creates a biologically accurate and detailed
3D model of the neocortical column that includes comprehensive
information on the electrical behavior and morphology of each
neuron, as well as the synaptic connections between them.
The last stage of the Blue Column's creation is to assign each
functional neuron to a processor and use the axonal delays to
control the communication between the neurons and processors.
This process effectively converts the processors into neurons and
the cables between them into axons that interconnect the neurons.
As a result, the entire Blue Gene system can be turned into a
neocortical microcircuit with detailed information on the
morphology and electrical behavior of each neuron, along with the
synaptic connections between them.
which can simulate large-scale networks consisting of
morphologically complex neurons. One of these programs is a
customized version of the NEURON simulator, which can be run
on Blue Gene. The other program uses the messaging component
of the Neo Cortical Simulator (NCS) to handle communication
between NEURON-simulated neurons distributed across various
processors. Both programs have undergone testing, demonstrating
their ability to simulate tens of thousands of neurons that are both
morphologically and electrically complex.
A program developed by the Blue Brain Project can simulate
networks with morphologically complex neurons. This program
can run on Blue Gene and is able to model tens of thousands of
neurons that are both morphologically and electrically complex.
Additionally, a second program has been created which uses the
messaging component of the Neo Cortical Simulator (NCS) to
manage communication between NEURON-simulated neurons
distributed across different processors.
G. NEUROLOGICAL CIRCUIT MODELLING:
The hinderances for digital computers in the analysis of biological
processes are due to the incredible complexity and scale of
biological systems. At the atomic level, biological processes
involve an enormous number of particles that are constantly
interacting with one another, making simulations of these
processes extremely computationally intensive. Even with the most
powerful supercomputers available today, it can take days to
calculate just a microsecond of protein folding at the atomic scale.
It is worth mentioning that simulating all biological processes at
the atomic level is not always necessary to comprehend their
functioning.
The Blue Brain Project's use of the Blue Gene supercomputer
allows for the simulation of a considerable number of neurons at a
cellular level, such as up to 100,000 complex neurons or 100
million simple neurons. This is like the number of neurons found
in a mouse brain. Nevertheless, the project still faces a significant
obstacle in simulating neurons within microcircuits, microcircuits
within brain regions, and brain regions within the entire brain,
which is necessary to gain a better understanding of the emergence
of complex behaviors.
Computational power will need to increase significantly before we
can simulate the human brain, with its approximately 100 billion
neurons, at the same level of detail as the Blue Column. However,
improvements in algorithmic and simulation efficiency, as well as
advances in specialized hardware like FPGAs and ASICs, could
reduce the required computational power by several orders of
magnitude.
Despite these challenges, the potential benefits of simulating
biological processes and the human brain are significant. These
simulations could help us better understand brain function and
dysfunction, develop new treatments for diseases and conditions,
and even create new artificial intelligence and robotics
technologies based on biological principles.
To further elaborate on the process of building a microcircuit
model using computational simulations, after experimental results
are collected and stored in a database, the morphology of the
neurons is checked for errors and repaired if necessary. This
involves analyzing the shape and structure of the neurons and
making sure that they are accurate representations of the real
neurons.
Once the morphology is verified, multiple copies of each neuron
are created using statistical distributions to generate a full range of
morphological diversity. Ion channels are then inserted into each
neuron to capture its unique electrical properties. This involves
adjusting the conductance and distribution of the channels based on
known statistical distributions to create a range of electrical classes
that match the behavior of the real neurons.
The next step is to put the neurons within a 3D column, which is
done using a circuit builder. Axo-dendritic collisions are simulated,
and synaptic connectivity is determined using structural and
functional statistics. This information is then used to convert a
fraction of the axo-dendritic touches into synapses, creating a
functional network of neurons.
NEURON reads the circuit configuration and inserts the thousands
of synapses into the corresponding locations in each modelled
neuron. The resulting microcircuit can be placed into a brain region
using the brain builder, while the stimulus and recording
conditions are established using an environment builder.
In the final step, the neurons are allocated to processors in integer
numbers, and the output is analyzed, displayed, or updated into
real-time algorithms for feedback stimulation [6]. This process
integrates experimental data with computational simulations to
produce an intricate microcircuit model, enabling a better
understanding of complex brain behavior.
Fig 4. Data Manupulation Cascade
APPLICATIONS OF BLUE BRAIN PROJECT
A. Define the Functions of the basic elements:
The Blue Brain Project aims to provide researchers with
a new understanding of brain functions and dysfunctions
by creating accurate models of the mammalian brain
from the cellular level up. The use of the Blue Gene
supercomputer enables the project team to simulate and

6. visualize complex neural microcircuits, allowing for the
study of interactions between different neurons,
synapses, and ion channels. By identifying and
quantifying the contributions of each element to the
emergent behavior of the brain, researchers can gain new
insights into how the brain works and how it can be
treated when it malfunctions [7]. This information-based
approach is paving the way for a new generation of
customized medicine, and the Blue Brain Project is
leading the charge.
B. Grasping the Intricacy:
Currently, the only workable strategy to understand why
the brain necessitates a variety of ion channels, neurons,
and synapses, along with complicated dendritic and
axonal arborizations featuring diverse receptors instead
of standardized types commonly found in many models,
is using intricate and precise brain simulations.
C. Investigating the function of dendrites:
The latest method to study the dendritic object theory,
which suggests the constant formation of three-
dimensional voltage structures across dendritic segments
regardless of the neuron's type, and the involvement of
spikes in maintaining these structures, is through
intricate and precise brain simulations.
D. Uncovering functional heterogeneity:
Many models are designed with a particular function in
mind, whereas a biologically inspired design could
potentially accommodate a variety of functions.
Uncovering the mechanisms of memory formation and
recall. This approach holds the potential to decipher how
information is encoded within the neural circuit for the
purpose of storing and recalling it and may unveil the
distinct roles played by various types of neurons in these
critical processes.
E. Observing the development of cognition:
This approach provides the opportunity to follow the
path of a network of neurons as they develop the
electrical states that represent the organism and its
environment, ultimately leading to the emergence of
intelligent behavior.
F. Finding weak spots:
The neocortex is a crucial component of mammalian
brains, providing significant computational capabilities.
However, it is also vulnerable to defects that can have
severe cognitive consequences. A comprehensive model
is currently the only approach to pinpoint the most
vulnerable circuit parameters, uncovering potential areas
of dysfunction and identifying targets for treatment.
G. Modeling pathology and devising therapies:
Computational simulations can be utilized to assess
different hypotheses for the pathophysiology of
neurological and psychiatric disorders, and to create and
evaluate innovative treatment methods.
H. Offering a blueprint for circuit design:
Elaborated models could provide a comprehensive
blueprint for designing circuits that mimic the neural
networks found in the brain. This would allow for the
creation of more powerful and efficient computing
systems that could be used in a variety of applications,
such as artificial intelligence and robotics.
APPLICATIONS OF BLUE BRAIN
A. Collecting and validating a century of data:
The primary advantage of this approach is that it offers a functional
model that can be utilized to accumulate and validate a century's
worth of knowledge regarding the neocortical column's
microstructure and functions. As a result, the Blue Column will
create a digital library where researchers can analyze the
neocortex's microarchitecture in three dimensions and access all
pertinent research related to its anatomy and operation.
B. Decoding the Neural Code:
The foundation of how the brain creates representations through
electrical patterns is known as the Neural Code. In the neocortex,
the NCC, or Neural Code Converter, is the primary network for
computations, it is crucial to have an accurate replica of the NCC
that can precisely reproduce the emergent electrical dynamics of
the actual microcircuit.
C. Comprehending how the neocortex processes
information:
Understanding how the neocortex processes, stores, and retrieves
information requires a precise replica of the NCC. The NCC,
which is the fundamental network for computing in the neocortex,
is based on the Neural Code, which forms representations through
electrical patterns. Precise simulation is advantageous because it
can produce accurate forecasts about neocortical information
processing. A continuous exchange between simulations and
experiments is necessary to construct a reliable replica of the NCC.
Through these iterations, the roles of individual components such
as neurons, synapses, ion channels, and receptors, as well as
pathways like mono-synaptic, di-synaptic, and multi-synaptic
loops, and physiological mechanisms such as functional
characteristics, learning, reward, and goal-directed behavior, can
be identified.
D. A new approach for drug discovery for brain
disorders:
Gaining insight into the roles of various elements and pathways of
the NCC will establish a solid basis for investigating the cellular
and synaptic underpinnings of diverse neurological and psychiatric
disorders. Such simulations can be used to evaluate the effects of
receptor, ion channel, cellular and synaptic abnormalities, and to
determine the most effective experimental tests. This could aid in
identifying potential drug targets and developing new treatments
for brain disorders.
E. An International Center:
A digital model of the NCC will serve as a platform for researchers
to investigate hypotheses related to brain function and dysfunction,
ultimately speeding up the research process. Simulations can also
aid in identifying which parameters should be measured in
experiments. Additionally, a sophisticated visualization system,
including 2D, 3D, and 3D immersive capabilities, can provide
detailed imaging of neural dynamics during various cognitive
processes, which may not be feasible or practical to obtain through
real-world experiments.
F. A Basis for Complete Brain Simulations:
An exact copy of a mammalian brain with all its cellular and
synaptic complexity above the molecular level is unlikely to be
simulated with current and foreseeable future computer
technology. Therefore, it is crucial to have a precise model of an
NCC, which can be used to create simplified models that still
retain essential functions and computational abilities. These
models can then be duplicated and connected to form neocortical
brain regions. The understanding of the NCC architecture can also
be applied to assist in the reconstruction of subcortical brain
regions.
G. A Basis for Molecular Modeling of Brain
Function:
Creating a precise replica of the neocortical column is crucial to
gradually increase the complexity of the model, leading to a
description of the neocortex at the molecular level, including the
simulation of biochemical pathways. Developing a molecular-level
model of the NCC will serve as a basis for understanding brain
function on a molecular level, as the NCC acts as an interface
between genes and complex cognitive functions. This connection
will enable the prediction of cognitive consequences caused by
genetic disorders and allow the identification of the genetic and

7. molecular origins of cognitive deficits. Achieving this level of
simulation will require the most advanced phase of Blue Gene
development, providing a foundation for molecular modeling of
brain function.
ADVANTAGES AND DISADVANTAGES
A. ADVANTAGES:
 We are capable of recalling information effortlessly.
 Decision-making processes can occur independently,
without human intervention.
 The intelligence of a deceased individual can continue to
be utilized even after their passing.
 The thought processes of various animals can be
comprehended by analyzing the electrical signals from
their brains, allowing for a better understanding of their
behavior.
 Direct nerve stimulation could enable hearing in the deaf,
and this technology could prove beneficial in treating
several psychological disorders. Additionally, by
uploading and downloading the contents of the brain, a
person could potentially overcome madness.
B. DISADVANTAGES:
 Our reliance on computer systems increases.
 Our technical knowledge may be used against us by
others.
 The threat of computer viruses will become more and
more dangerous in the future.
 The fear of new technologies is a greater danger than the
technologies themselves, as people may resist and reject
them. This resistance can be seen in contemporary
society's stance on human cloning.
FUTURE VISION:
The synthesis era in neuroscience refers to a new phase
in which the field is focused on synthesizing large
amounts of data from various experiments to create
accurate models of the brain, rather than just collecting
data. This phase was initiated by the Human Brain
Project, which aimed to simulate the entire human brain.
It is not necessary to have a complete data set to begin
the synthesis phase, and it is seen as a complementary
approach to experimental research.
Detailed models of the brain created during the synthesis
era will likely become the final databases used to
organize all knowledge of the brain. These models will
enable hypothesis testing, fast diagnoses of brain
disorders, and the development of new treatments. It
takes time to build detailed models of the brain, and the
time needed depends on the level of detail required. For
instance, the Blue Column, a detailed model of a
neocortical column with 10,000 neurons, has already
been built and simulated. However, it takes time to refine
the detailed properties and calibration of the circuit, and
a model of the entire brain at the cellular level may take
another decade.
Although it takes time to build accurate models of the
brain, there are no fundamental obstacles to modeling the
brain, and it is likely that detailed models of mammalian
brains, including humans, will be available soon. The
Blue Brain project, which aims to simulate the entire
mammalian brain at the cellular level, will allow
scientists to challenge our understanding of intelligence
and generate new theories of consciousness.
IV. CONCLUSION:
However, the idea of transferring ourselves into computers raises
ethical, moral, and existential questions. For example, if we
transfer ourselves into computers, are we still human? What
happens to our consciousness? Would we still have free will?
Additionally, there is the issue of inequality and accessibility.
Furthermore, the idea of transferring ourselves into computers
raises concerns about the potential for misuse and abuse of
technology. It could be used for nefarious purposes, such as
creating an army of digital slaves or even for the ultimate
destruction of humanity. The development of this technology
should be carefully monitored and regulated to ensure its safe and
responsible use.
In conclusion, while the transfer of human consciousness into
computers may be possible in the future, it is important to carefully
consider the ethical, moral, and societal implications of this
technology. It is crucial to ensure that the development and use of
this technology align with our values and serve the betterment of
humanity.
V. ACKNOWLEDGMENT
I would like to express my deepest gratitude to Dr. Shilpa Sharma
ma’am, my supervisor, for his invaluable guidance, support, and
encouragement throughout the process of writing this term paper.
His expertise and feedback have been instrumental in shaping and
improving the quality of my work.
I would also like to extend my appreciation to my friends for their
help in providing resources, feedback, and support. Their
contributions have greatly enriched my research and writing
process.
REFERENCES
[1] “Engineering in Medicine and Biology Society”, 2008. EMBS
2008. 30th Annual International Conference of the IEEE
[2] Henry Markram, “The Blue Brain Project”, Nature Reviews
Neuroscience 2006 February.
[3] Simulated brain closer to thought BBC News 22 April 2009.
[4] “Project Milestones”. Blue Brain.
http://bluebrain.epfl.ch/Jahia/site/bluebrain/op/edit/pid/19085
[5] Graham-Rowe, Duncan. “Mission to build a simulated brain
begins”, New Scientist, June 2005. pp. 1879-85.
[6] Blue Gene: http://www.research.ibm.com/bluegene
[7] The Blue Brain Project: http://bluebrainproject.epfl.ch