Blue Brain


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Blue Brain

  3. 3.  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 level.  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. INTRODUCTION
  4. 4. 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 laboratories.
  5. 5. 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.
  6. 6. 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) .
  7. 7. GOALS IDENTIFIED DURING BRAIN SIMULATION  Neocortical column modelling The initial goal of the project, completed in December 2006,[3] 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 human does.
  8. 8. FUNCTIONING OF BRAIN  Sensory Input :- Receiving input such as sound ,image, etc through sensory cell .  Interpretation :- 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 .
  9. 9. BRAIN SIMULATION NATURAL BRAIN VS SIMULATED BRAIN  INPUT Through the natural neurons  INTERPRETATION By different states of the neurons in the brain.  OUTPUT Through the natural neurons.  INPUT Through silicon chips or artificial neurons.  INTERPRETATION By a set of bits in the set of registers.  OUTPUT Through the silicon chips.
  10. 10.  PROCESSING Through arithmetic and logical calculations.  MEMORY Through permanent states of neurons. PROCESSING Through arithmetic and logical calculations and artificial intelligence.  MEMORY Through secondary memory.
  11. 11. STEPS INVOLVED IN BUILDING A VIRTUAL BRAIN Three main steps to build the virtual brain are : 1) Data Acquisition 2) Simulation 3) Visualisation of Results.
  12. 12. 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 for simulation.
  13. 13. SIMULATION NEURON 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. Simulation speed 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.
  14. 14. WORKFLOW 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.
  15. 15. 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.
  16. 16. NEURON (SOFTWARE) 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.
  17. 17. UPLOADING HUMAN BRAIN  The uploading is possible by the use of small robots known as the nanobots.  These robots are small enough to travel through out our circulatory system.  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.
  18. 18.  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 computer.
  19. 19. 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.
  21. 21.  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.
  22. 22. Progress  By 2005 the first single cellular model was completed.  first artificial cellular neocortical column of 10,000 cells was built by 2008.  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.
  23. 23. ADVANTAGES •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.
  24. 24. DISADVANTAGES •We become dependent upon the computer . • Others may use technical knowledge against us.
  25. 25. HARDWARE AND SOFTWARE REQUIRMENT  22.8 TFLOPS peak processing speed.  8,096 CPUs at 700 MHz (downgraded to handle massive parallel processing).  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.
  26. 26. CONCLUSION We will be able to transfer ourselves into computers upto some extent .