Seminar Report. Blue Brain. 2. INTRODUCTION. Human brain, the most valuable creation of God. The man is called intelligent because of the brain. Today we. The report is in Do Androids Dream of. Electric Sheep The Blue Brain project proposes a fantastic voyage.2 The first phase of this project succeeded in http ://ronaldweinland.info+html. Crochet, S. Free download complete engineering seminar Blue Brain Seminar Report pdf.
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Blue Brain, Ask Latest information, Abstract, Report, Presentation (pdf,doc,ppt), Blue Brain technology discussion,Blue Brain paper presentation details,Blue. blue brain seminar report - Free download as PDF File .pdf), Text File .txt) or read online for free. Word Document Blue Brain - Free download as Word Doc .doc /.docx), PDF File .pdf), Text File .txt) or read online for free. imp. IBM is now in research to create a virtual brain, called Blue brain. .. Blue Brain Technology Seminar Report.
Published on April 25, Abstract Blue brain " -The name of the world's first virtual brain. That means a machine that can function as human brain. 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. So that man can think, take decision without any effort. After the death of the body, the virtual brain will act as the man. So, even after the death of a person we will not loose the knowledge, intelligence, personalities, feelings and memories of that man that can be used for the development of the human society.
This organ transmits the vibration information to a nerve, which sends it to the brain for interpretation and memory. The mission in undertaking The Blue Brain Project is to gather all existing knowledge of the brain, accelerate the global research effort of reverse engineering the structure and function of the components of the brain, and to build accomplete theoretical framework that can orchestrate the reconstruction of the brain of mammals and man from the genetic to the whole brain levels, into computer models for simulation, visualization and automatic knowledge archiving by Biologically accurate computer models of mammalian and human brains could provide a new foundation for understanding functions and malfunctions of the brain and for a new generation of information-based, customized medicine.
Each single FPU can execute up to 2 multiply-adds per cycle, which means that the peak performance of the chip is 8 oating point operations per cycle 4 under normal conditions, with nouse of SIMD mode. This leads to a peak performance of 5.
So, the aggregate performance of a processor card in virtual node mode is: Microcircuits are composed of neurons and synaptic connections. To model neurons, the three-dimensional morphology, ion channel composition, and distributions and electrical properties of the different types of neuron are required, as well as the total numbers of neurons in the microcircuit and the relative proportions of the different types of neuron. To model synaptic connections, the physiological and pharmacological properties of the different types of synapse that.
Neurons receive inputs from thousands of other neurons, which are intricately mapped onto different branches of highly complex dendritic trees and require tens of thousands of compartments to accurately represent them. There is therefore a minimal size of a microcircuit and a minimal complexity of a neuron smorphology that can fully sustain a neuron.
A massive increase in computational power is required to make this quantum leap an increase that is provided by IBMs Blue Gene supercomputer. By exploiting the computing power of Blue Gene, the Blue Brain Project1 aims to build accurate models of the mammalian brain from rst principles.
The rst phase of the project is to build a cellularlevel as opposed toa genetic- or molecular-level model of a 2-week-old rat somato sensory neo cortex corresponding to the dimensions of a neocortical column NCC as dened by the dendritic arborizations of the layer 5 pyramidal neurons.
The combination of infrared differential interference microscopy in brain slices and the use of multi-neuron patch clamping allowed the systematic quantication of the molecular, morphological and electrical properties of the different neurons and their synaptic pathways in a manner that would allow an accurate reconstruction of the column.
Over the past 10 years, the laboratory has prepared for this reconstruction by developing the multi-neuron patch-clamp approach, recording from thousands of neocortical neurons and their synaptic connections, and developing quantitative approaches to allow a complete numerical breakdown of the elementary building blocks of the NCC. The recordings have mainly been in the day-old rat somato sensory cortex, which is a highly accessible region on which many researchers have converged following a series of pioneering studies driven by Bert Sakmann.
Much of the raw data is located in our data bases,but a major initiative is underway to make all these data freely available in a publicly accessible database. The so-called blue print of the circuit, although not entirely complete, has reached a sufcient level of renement to begin the reconstruction at the cellular level.
Highly quantitative data are available for rats of this age, mainly because visualization of the tissue is optimal from a technical point of view.
This age also provides an ideal template because it can serve as a starting point from which to study maturation and ageing of the NCC. As NCCs show a high degree of stereotypy, the region from which the.
The NCC should not be overly specialized, because this could make generalization to other neocortical regions difcult, but areas such as the barrel cortex do offer the advantage of highly controlled in vivo data for comparison. The image shows the Microcircuit in various stages of reconstruction.
Only a small fraction of reconstructed, three dimensional neurons is shown. Red denition of the NCC. The microcircuits from left to right for layers 2, 3, 4 and 5. A single thick tufted layer 5 pyramidal neuron located within the column.
One pyramidal neuron in layer 2, a small pyramidal neuron in layer 5 and the large thick tufted pyramidal neuron in layer An image of the NCC, with neurons located in layers 2 to 5. All the 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.
Analyses of individual neurons must be repeated thousands of times. And analyses dealing with the network activity must deal with data that easily reaches hundreds of gigabytes per second of simulation. Using massively parallel computers the data can be analyzed where it is created server-side analysis for experimental data, online analysis during simulation. Given the geometric complexity of the column, a visual exploration of the circuit is an important part of the analysis.
Mapping the simulation data onto themorphology is invaluable for an immediate verication of single cell activity as well as network phenomena.
Architects at EPFL have worked with the Blue Brain developers to design a visualization interface that translates the Blue Gene data into a 3Dvisual representation of the column.
A different supercomputer is used for this computationally intensive task. The visualization of the neurons shapes is a challenging task given the fact that a column of 10, neurons rendered in high quality mesh accounts for essentially 1 billion triangles for which about GB of management data is required. Simulation data with a resolution of electrical compartments for each neuron accounts for another GB.
As the electrical impulse travels through the column, neurons A visual interface makes it possible to quickly identify areas of interest that can then be studied more extensively using further simulations. A visual representation can also be used to compare the simulation results with experiments that show electrical activity in the brain.
The rst step is to parse each three-dimensional morphology and correct errors due to the in vitro preparation and reconstruction. The repaired neurons are placed in a data base from which statistics for the different anatomical classes of neurons are obtained.
These statistics are used to clone an in denite number of neurons in each class to capture the full morphological diversity. The next step is to take each neuron and insert ion channel models in order to produce the array of electrical types.
The eld has reached a sufcient stage of convergence to generate efforts to classify neurons, such as the Petilla Convention - a conference held in October on anatomical and electrical types of neocortical interneuron, established by the community.
Single cell gene expression studies of neocortical inter neurons now provide detailed predictions of the specic combinations of more than 20 ion channel genes that underlie electrical diversity.
A database of biologically accurate Hodgkin-Huxley ion channel models is being produced. The simulator NEURON is used with automated tting algorithms running on Blue Gene to insert ion channels and adjust their parameters to capture the specic electrical properties of the different electrical types found in each anatomical class. The statistical variations within each electrical class are also used to generate subtle variations in discharge behaviour in each neuron. So, each neuron is morphologically and electrically unique.
Rather than taking 10, days to t each neurons electrical behaviour with a unique prole, density and distribution of ion channels, applications are being prepared to use Blue Gene to carry out such a t in a day. These functionalized neurons are stored in a database. The three-dimensional neurons are then imported into Blue Builder, a circuit builder that loads neurons into their layers according to a recipe of neuron numbers and proportions.
A collision detection algorithm is run to determine the structural positioning of all axo dendritic touches, and neurons are jittered and spun until the structural touches match experimentally derived statistics. Probabilities of connectivity between different types of neuron are used to determine which neurons are connected, and all axo-dendritic touches are converted into synaptic connections.
The manner in which the axons map onto the dendrites between specic anatomical classes and the distribution of synapses received by a class of neurons are used to verify and ne tune the biological accuracy of the synaptic mapping between neurons.
It is therefore possible to place million synapses in accurate three-dimensional space, distributed on the detailed three dimensional morphology of each neuron. The synapses are functionalized according to the synaptic parameters for different classes of synaptic connection within statistical variations of each class, dynamic synaptic models are used to simulate transmission, and synaptic learning algorithms are introduced to allow plasticity. The distance from the cell body to each synapse is used to compute the axonal delay, and the circuit conguration is exported.
The conguration le is read by a NEURON subroutine that calls up each neuron and effectively inserts the location and functional properties of every synapse on the axon, soma and dendrites. One neuron is then mapped onto each processor and the axonal delays are used to manage communication between neurons and processors. Effectively, processors are converted into neurons, and MPI message-passing interface based communication cables are converted into axon sinter connecting the neurons - so the entire Blue Gene is essentially converted into an eocortical microcircuit.
We developed two software programs for simulating such large-scale networks with morphologically complex The latter simulator will allow embedding of a detailed NCC model into a simplied large scale model of the whole brain.
Both of these softwares have already been tested, produce identical results and can simulate tens of thousands of morphologically and electrically complex neurons as many as 10, compartments per neuron with more than a dozen Hodgkin-Huxley ion channels per compartment.
Up to 10 neurons can be mapped onto each processor to allow simulations of the NCC with as many as ,neurons. Optimization of these algorithms could allow simulations to run at close to real time. The circuit conguration is also read by a graphic application, which renders the entire circuit in various levels of textured graphic formats. The output from Blue Gene any parameter of the model can be fed directly into the SGI system to performing silico imaging of the activity of the inner workings of the NCC.
Eventually, the simulation of the NCC will also include the vasculature, as well as the glial network,to allow capture of neuron-glia interactions. If each atomic collision is simulated, the most powerful super-computers still take days to simulate a microsecond of protein folding, so it is, of course, not possible to simulate complex biological systems at the atomic scale.
However, models at higher levels, such as the molecular or cellular levels, can capture lower-level processes and allow complex large-scale simulations of biological processes.
The Blue Brain Projects Blue Gene can simulate a NCC of up to , highly complex neurons at the cellular or as many as million simple neurons about the same number of neurons found in a mouse brain. However, simulating neurons embedded in microcircuits, microcircuits embedded in brain regions, and brain regions embedded in the whole brain as part of the process of understanding the emergence of complex behaviors of animals is an inevitable progression in understanding brain function and function, and the question is whether whole-brain simulations are at all possible.
Computational power needs to increase about 1-million-fold before we will be able to simulate the human brain, with billion neurons, at the same level of detail as the Blue Algorithmic and simulation efciency which ensure that all possible FLOPS are exploited could reduce this requirement by two to three orders of magnitude.
Simulating the NCC could also act as a test-bed to rene algorithms required to simulate brain function, which can be used to produce eld programmable gate array FPGA -based chips. FPGAs could increase computational speeds by as much as two orders of magnitude. It could therefore be possible, in principle, to simulate the human brain even with current technology. The computer industry is facing what is known as a discontinuity, with increasing processor speed leading to unacceptably high power consumption and heat production.
This is pushing a qualitatively new transition in the types of processor to be used in future computers. These advances in computing should begin to make genetic- and molecular-level simulations possible. Software applications and data manipulation required to model the brain with. Experimental results that provide the elementary building blocks of the microcircuit are stored in a database. Before three-dimensional neurons are modelled electrically, the morphology is parsed for errors, and for repair of arborizations damaged during slice preparation.
The morphological statistics for a class of neurons are used to clone multiple copies of neurons to generate the full morphological diversity and the thousands of neurons required in the simulation. A circuit builder is used to place neurons within a three-dimensional column, to perform axodendritic collisions and, using structural and functional statistics of synaptic connectivity, to convert a fraction of axodendritic touches into synapses.
Detailed, biologically accurate brain simulations offer the opportunity to answer some fundamental questions about the brain that cannot be addressed with any current experimental or theoretical approaches. Over the past 10 years, the laboratory has prepared for this reconstruction by developing the multi-neuron patch- clamp approach, recording from thousands of neocortical neurons and their synaptic connections, and developing quantitative approaches to allow a complete numerical breakdown of the elementary building blocks of the NCC.
The recordings have mainly been in the day-old rat somatosensory cortex, which is a highly accessible region on which many researchers have converged following a series of pioneering studies driven by Bert Sakmann. Much of the raw data is located in our databases, but a major initiative is underway to make all these data freely available in a publicly accessible database.
Highly quantitative data are available for rats of this age, mainly because visualization of the tissue is optimal from a technical point of view. This age also provides an ideal template because it can serve as a starting point from which to study maturation and ageing of the NCC. As NCCs show a high degree of stereotypy, the region from which the template is built is not crucial, but a sensory region is preferred because these areas contain a prominent layer 4 with cells specialized to receive input to the neocortex from the thalamus; this will also be required for later calibration with in vivo experiments.
The NCC should not be overly specialized, because this could make generalization to other neocortical regions difficult, but areas such as the barrel cortex do offer the advantage of highly controlled in vivo data for comparison. The image shows the Microcircuit in various stages of reconstruction. Only a small fraction of reconstructed, three dimensional neurons is shown.
Red indicates the dendritic and blue the axonal arborizations. The columnar structure illustrates the 12 Fig. Reconstructing the neocortical column. All the 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.
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Hardware and software requirement 8. Current research work 9. Advantage and disadvantage The man is calledintelligent because of the brain. Today we are developed because we canthink, that other animals can not do. But we loss the knowledge of a brainwhen the body is destroyed after the death of man. That knowledge mighthave been used for the development of the human society.
What happen ifwe create a brain and up load the contents of natural brain into it. That means amachine that can function as human brain. Today scientists are in researchto create an artificial brain that can think, response, take decision, and keepanything in memory. The main aim is to upload human brain into machine. So that man can think, take decision without any effort. After the death of thebody, the virtual brain will act as the man.
So, even after the death of aperson we will not loose the knowledge, intelligence, personalities, feelingsand memories of that man that can be used for the development of the humansociety. No one has ever understood the complexity of human brain. It iscomplex than any circuitry in the world. Because what everman has created today always he has followed the nature. When man doesnot have a device called computer, it was a big question for all.
But today itis possible due to the technology. Technology is growing faster than everything. IBM is now in research to create a virtual brain.
If possible, this would be the first virtual brain of the world. The IBM is now developing a virtual brain known as theBlue brain. With in 30 years,we will be able to scan ourselves into the computers. Is this thebeginning of eternal life? What is Virtual Brain? We can say Virtual brain is an artificial brain, which does notactually the natural brain, but can act as the brain. It can think like brain,take decisions based on the past experience, and response as the naturalbrain can.
It is possible by using a super computer, with a huge amountof storage capacity, processing power and an interface between thehuman brain and this artificial one. Through this interface the data storedin the natural brain can be up loaded into the computer. So the brain andthe knowledge, intelligence of anyone can be kept and used for ever,even after the death of the person.
Seminar Report Blue BrainWhy we need virtual brain?