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SUBMITTED BY :
2~ HISTORY OF THE PROJECT
3~ WHAT IS VIRTUAL BRAIN
4~ MOTIVATION OF THE BLUE BRAIN
5~ BRAIN SIMULATION
6~ STEPS INVOLVED TO BUILD A VIRTUAL BRAIN
7~ UPLOADING HUMAN BRAIN
8~ RESEARCH WORK
9~ ADVANTAGES AND DISADVANTAGES
10~ HARDWARE AND SOFTWARE REQUIREMENTS
11 ~ CONCLOSION
Human brain, the most valuable creation of God. The man is called
intelligent because of the brain. But we loss the knowledge of a
brain when the body is destroyed after the death .
“BLUE BRAIN” - The name of the world’s first virtual brain. That
means a machine that can function as human brain.
The Blue Brain Project is an attempt to create a synthetic brain
by reverse-engineering the mammalian brain down to the molecular
Today scientists are in research to create an artificial brain that
can think, response, take decision, and keep anything in memory. The
main aim is to upload human brain into machine.
HISTORY OF THE PROJECT
The aim of the project, founded in May 2005 by the Brain and Mind
Institute of the École Polytechnique Fédérale de Lausanne (Switzerland) is
to study the brain's architectural and functional principles.
The project is headed by the Institute's director, Henry Markram.
It use a Blue Gene Supercomputer built by IBM and Michael HinenS’s
software known as ‘NEURONS’ .
The simulations were carried on BLUE GENE computer and therefore it
was called the ‘BLUE BRAIN’ .
There are a number of sub-projects, including the Cajal Blue Brain,
coordinated by the Supercomputing and Visualization Center of
Madrid (CeSViMa), and others run by universities and independent
WHAT IS VIRTUAL BRAIN?
• A machine that can function as brain
• It can take decision.
• It can think.
• It can response.
• It can keep things in memory.
• It also has feelings and emotions.
MOTIVATION OF THE BLUE BRAIN
Four broad motivations behind the Blue Brain Project are :
Brain disease treatments .
Scientific curiosity about consciousness and the human mind.
Integration of all neuroscientific research results worldwide.
Progress towards building thinking machines (bottom
up approach) .
GOALS IDENTIFIED DURING BRAIN SIMULATION
Neocortical column modelling
The initial goal of the project, completed in December 2006, was
the simulation of a rat neocortical column, which can be considered
the smallest functional unit of the neocortex . Such a column is about
2 mm tall, has a diameter of 0.5 mm and contains about
60,000 neurons in humans; ratneocortical columns are very similar in
structure but contain only 10,000 neurons .
Whole brain simulation
A longer term goal is to build a detailed, functional simulation of the
physiological processes in the human brain. If build correctly , it
should speak and have an intelligence and behave very much as a
FUNCTIONING OF BRAIN
Sensory Input :-
Receiving input such as sound ,image, etc through sensory cell .
Interpretation of the received input by the brain by
defining states of neurons in the brain.
Motor Output :-
Receiving of electric responses from the brain to
perform any action .
NATURAL BRAIN VS SIMULATED BRAIN
Through the natural neurons
By different states of the
neurons in the brain.
Through the natural neurons.
Through silicon chips or
By a set of bits in the set
Through the silicon chips.
Through arithmetic and logical
Through permanent states of
Through arithmetic and logical
calculations and artificial
Through secondary memory.
STEPS INVOLVED IN BUILDING A VIRTUAL BRAIN
Three main steps to build the virtual brain are :
1) Data Acquisition
3) Visualisation of Results.
Data acquisition involves taking brain slices, placing
them under a microscope, and measuring the shape
and electrical activity of individual neurons. This is
how the different types of neuron are studied and
catalogued. The neurons are typed by morphology (i.e.
their shape), electrophysiological behaviour, location
within the cortex, and their population density. These
observations are translated into mathematical
algorithms which describe the form, function, and
positioning of neurons. The algorithms are then used
to generate biologically- realistic virtual neurons ready
The primary software used by the BBP for neural simulations is a package
called NEURON. This was developed starting in the 1990s by Michael Hines at
Yale University and John Moore at Duke University. It is written in C, C++,
and FORTRAN. The software continues to be under active development and,
as of July 2012, is currently at version 7.2. It is free and open source
software, both the code and the binaries are freely available on the website.
Michael Hines and the BBP team collaborated in 2005 to port the package to
the massively parallel Blue Gene supercomputer.
In 2012 simulations of one cortical column (~10,000 neurons) run at
approximately 300 x slower than real time. So one second of simulated time
takes about five minutes to complete. The simulations show approximately
linear scaling - that is, doubling the size of the neural network doubles the
time it takes to simulate. Currently the primary goal is biological validity
rather than performance.
The simulation step involves synthesising virtual cells using the algorithms
that were found to describe real neurons. The algorthims and parameters
are adjusted for the age, species, and disease stage of the animal being
simulated. Every single protein is simulated, and there are about a billion of
these in one cell. First a network skeleton is built from all the different
kinds of synthesised neurons. Then the cells are connected together
according to the rules that have been found experimentally. Finally the
neurons are functionalised and the simulation brought to life
A basic unit of the cerebral cortex is the cortical column. Each column can
be mapped to one function, e.g. in rats one column is devoted to each
whisker. A rat cortical column has about 10,000 neurons and is about the
size of a pinhead.
Visualisation of results
RTNeuron visualisation of a neuron
RTNeuron is the primary application used by the BBP for visualisation
of neural simulations. The software was developed internally by the
BBP team. It is written in C++ and OpenGL . RTNeuron is ad-hoc
software written specifically for neural simulations, i.e. it is not
generalisable to other types of simulation. RTNeuron takes the
output from Hodgkin-Huxley simulations in NEURON and renders
them in 3D. This allows researchers to watch as activation potentials
propogate through a neuron and between neurons. The animations
can be stopped, started and zoomed, thus letting researchers
interact with the model.
NEURON is a simulation environment for modeling
individual neurons and networks of neurons. It was
primarily developed by Michael Hines, John W.
Moore, and Ted Carnevale at Yale and Duke.
NEURON models individual neurons via the use of
sections which are subdivided into individual
compartments by the program, instead of requiring
the user to manually create the compartments. The
primary scripting language that is used to interact
with it is hoc but a Python interface is also available.
The programs for it can be written interactively in a
shell, or loaded from a file. NEURON supports
parallelization via the MPI protocol.
NEURON along with the analogous software
platform GENESIS are used as the basis for
instruction in computational neuroscience in many
courses and laboratories around the world.
UPLOADING HUMAN BRAIN
The uploading is possible by the use of small robots known as the
These robots are small enough to travel through out our
Traveling into the spine and brain, they will be able to monitor the
activity and structure of our central nervous system.
They will be able to provide an interface with computer while we
still reside in our biological form .
Nanobots could also carefully scan the structure of our brain,
providing a complete readout of the connection.
This information, when entered into a computer, could then continue to
function as us.
Thus the data stored in the entire brain will be uploaded into the
Simulating the micro circuit
Once the microcircuit is built, the exciting work
of making the circuit function can begin. All the
8192 processors of the Blue Gene are pressed
into service, in a massively parallel computation
solving the complex mathematical equations that
govern the electrical activity in each neuron when
a stimulus is applied. As the electrical impulse
travels from neuron to neuron, the results are
communicated via inter-processor communication
(MPI). Currently, the time required to simulate
the circuit is about two orders of magnitude
larger than the actual biological time simulated.
The Blue Brain team is working to streamline the
computation so that the circuit can function in
real time - meaning that 1 second of activity can
be modeled in one second.
IBM and Stanford University researchers modeled a cat's cerebral cortex
using the Blue Gene/IP supercomputer, which currently ranks as thefourth
most powerful supercomputer in the world.
The simulated cat brain still runs about 100 times slower than the real
thing. But PhysOrg reports that a new algorithm called BlueMatter allows
IBM researchers to diagram the connections among cortical and sub-cortical
places within the human brain.
. They had simulated a full rat brain in 2007, and 1 percent of the human
cerebral cortex this year.
A separate team of Swiss researchers also used an IBM supercomputer for
their Blue Brain project, where a digital rat brain's neurons began creating
self-organizing neurological patterns. That research group hopes to simulate a
human brain within 10 years.
Another more radical approach from Stanford University looks to recreate
the human brain's messily chaotic system on a small device called Neurogrid.
Unlike traditional supercomputers with massive energy requirements,
Neurogrid might run on the human brain's power requirement of just 20 watts
-- barely enough to run a dim light bulb.
By 2005 the first single cellular model was completed.
first artificial cellular neocortical column of 10,000 cells was built by
By July 2011 a cellular mesocircuit of 100 neocortical columns with a million
cells in total was built.
A cellular rat brain is planned for 2014 with 100 mesocircuits totalling a
hundred million cells.
Finally a cellular human brain is predicted possible by 2023 equivalent to
1000 rat brains with a total of a hundred billion cells.
•Remembering things without any effort.
• Making decision without the presence of a person.
• Using intelligence of a person after the death .
• Understanding the activities of animals .
• Allowing the deaf to hear via direct nerve stimulation.
•We become dependent upon the computer .
• Others may use technical knowledge against us.
HARDWARE AND SOFTWARE REQUIRMENT
22.8 TFLOPS peak processing speed.
8,096 CPUs at 700 MHz (downgraded to handle massive parallel
256MB to 512MB memory per processor.
Linux and C++ software.
100 kilowatts power consumption.
Very powerful Nanobots to act as the interface between the
natural brain and the computer.
We will be able to transfer ourselves into computers
upto some extent .