blue brain project

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SEMINAR By Goutham atla Pharmacoinformatics 1st Sem, NIPER,Kolkata. :

SEMINAR By Goutham atla Pharmacoinformatics 1 st Sem , NIPER,Kolkata . BLUE BRAIN PROJECT

Slide 2:

INTRODUCTION

Slide 3:

The Blue Brain Project is an attempt to create a synthetic brain by reverse-engineering the mammalian brain down to the molecular level in order to understand brain function and dysfunction through detailed simulations. Simply modeling of Mammalian Brain i.e Nothing but Modeling of Neural Networks. Is it really possible to model a human brain ?

Slide 4:

“YES", The IBM is now developing a virtual brain known as the BLUE BRAIN. IBM’s Blue Gene supercomputer allows a quantum leap in the level of detail at which the brain can be modelled. It would be the worlds first virtual brain. In 2020 , we will be able to scan ourselves into the computers.

PEOPLE BEHIND BLUE BRAIN PROJECT : :

PEOPLE BEHIND BLUE BRAIN PROJECT : Project Director : Prof Henry markram

Slide 6:

On 1 July 2005, the Brain Mind Institute (BMI) and IBM (International Business Machines) launched the Blue Brain Project Using the enormous computing power of IBM’s prototype Blue Gene/L supercomputer. The aims of this ambitious initiative are to simulate the brains of mammals with a high level of biological accuracy and, ultimately, to study the steps involved in the emergence of biological intelligence.

The first phase of the project will be to make a software replica of a column of the neocortex. The neocortex constitutes about 85% of the human brain's total mass and is thought to be responsible for the cognitive functions of language, learning, memory and complex thought. An accurate replica of the neocortical column is the essential first step to simulating the whole brain and also will provide the link between genetic, molecular and cognitive levels of brain function.:

The first phase of the project will be to make a software replica of a column of the neocortex . The neocortex constitutes about 85% of the human brain's total mass and is thought to be responsible for the cognitive functions of language, learning, memory and complex thought. An accurate replica of the neocortical column is the essential first step to simulating the whole brain and also will provide the link between genetic, molecular and cognitive levels of brain function.

Slide 8:

Hardware and Software Requirements for modelling the Brain

Slide 9:

Memory with a very large storing capacity. Processor with a very high processing power. A very wide network. A program to convert the electric impulses from the brain to input signal, which is to be received by the computer and vice versa. very powerful Nanobots to act as the interface between the natural brain and the computer. Finally it is a SUPERCOMPUTER

Slide 10:

IBM’s BLUE GENE SUPERCOMPUTER. IBM, in partnership with scientists at Switzerland’s Brain and Mind Institute (BMI) started simulating the brain’s biological systems.

IBM BLUE GENE SUPERCOMPUTER:

IBM BLUE GENE SUPERCOMPUTER The system that will be installed at EPFL will occupy the floor space of about four refrigerators, and will have a peak processing speed of at least 22.8 trillion floating-point operations per second (22.8 teraflops), making it one of the most powerful supercomputers in the world.

NEURON 7.1:

NEURON 7.1 NEURON is a simulation environment for modeling individual neurons and networks of neurons The primary scripting language that is used to interact with it is hoc (High Order Calculator) but a Python interface is also available.

Slide 13:

//create two sections, the body of the neuron and a very long axon create soma, axon soma { //length is set to 100 micrometers L = 100 //diameter is set to 100 micrometers diam = 100 //insert a mechanism simulating the standard squid Hodgkin–Huxley channels insert hh //insert a mechanism simulating the passive membrane properties insert pas } axon { L = 5000 diam = 10 insert hh insert pas //the axon shall be simulated using 10 compartments. By default a single compartment is used nseg = 10 } //connect the distal end of the soma to the proximal end of the axon connect soma(1), axon(0) //declare and insert a current clamp into the middle of the soma Objref stim Soma stim = new Iclamp(0.5) //define some parameters of the stimulus: delay, duration (both in ms) and amplitude (in nA) stim.del = 10 stim.dur = 5 stim.amp = 10 //load a default NEURON library file that defines the run routine load_file("stdrun.hoc") //set the simulation to run for 50 ms tstop = 50 //run the simulation run()

Slide 14:

1. 2. Neuron 7.1 Screenshots

Slide 15:

Modelling Neurons receive inputs from thousands of other neurons, and has branches of highly complex dendritic trees and require tens of thousands of compartments to accurately represent them. Therefore a massive increase in computational power is required to make this quantum leap — an increase that is provided by IBM’s Blue Gene supercomputer . Using a Blue Gene supercomputer running Michael Hines's NEURON software , the simulation does not consist simply of an artificial neural network , but involves a biologically realistic model of neurons .

Slide 16:

Molecular, morphological and electrical properties of the different neurons and their synaptic pathways quantitated by Infrared differential interference microscopy in brain slices and the use of multi-neuron patch-clamping 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

Building the Blue Column:

Building the Blue Column The first step is to describe each three-dimensional morphology and correct errors due to the in vitro preparation and reconstruction. The repaired neurons are placed in a database from which statistics for the different anatomical classes of neurons are obtained. The next step is to take each neuron and insert ion channel models in order to produce the array of electrical types. Here biologically accurate Hodgkin-Huxley ion channel model is being produced.

Hodgkin-Huxley ion channel model built by using NEURON 7.1 (Screenshots):

Hodgkin-Huxley ion channel model built by using NEURON 7.1 (Screenshots)

Slide 19:

The simulator NEURON is used with automated fitting algorithms running on Blue Gene to insert ion channels and adjust their parameters to capture the specific electrical properties of the different electrical types found in each anatomical class. Rather than taking ~10,000 days to fit each neuron’s electrical behaviour with a unique profile, density and distribution of ion channels, applications are being prepared to use Blue Gene to carry out such a fit in a day. These functionalized neurons are stored in a database. The three-dimensional neurons are then imported into BlueBuilder , a circuit builder that loads neurons into their layers according to a ‘recipe’ of neuron numbers and proportions.

Slide 20:

A collision detection algorithm is run to determine the structural positioning of all axo-dendritic touches. The execution of this algorithm is computationally much more intense than the actual simulation of the NCC, and also requires Blue Gene. The manner in which the axons map onto the dendrites between specific anatomical classes and the distribution of synapses received by a class of neurons are used to verify and fine-tune the biological accuracy of the synaptic mapping between neurons. It is therefore possible to place 10–50 million synapses in accurate three-dimensional space, distributed on the detailed three-dimensional morphology of each neuron.

Slide 21:

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 configuration is exported. The configuration file 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 axons interconnecting the neurons — so the entire Blue Gene is essentially converted into a neocortical microcircuit.

Slide 22:

They developed two software programs for simulating such large-scale networks with morphologically complex neurons. A new MPI (message-passing interface) version of NEURON has been adapted by Michael Hines to run on Blue Gene. The second simulator uses the MPI messaging component of the large-scale NeoCortical Simulator (NCS), which was developed by Philip Goodman, to manage the communication between NEURON-simulated neurons distributed on different processors. The latter simulator will allow embedding of a detailed NCC model into a simplified large-scale model of the whole brain.

The template - The Blue Column:

The template - The Blue Column will be composed of ~10,000 neocortical neurons within the dimensions of a neocortical column (~0.5 mm in diameter and ~1.5 mm in height) . The Blue Column will include the 1. different types of neuron in layer 1, 2. multiple subtypes of pyramidal neuron in layers 2–6, 3. spiny stellate neurons in layer 4, and more than 4. 30 anatomical–electrical types of interneuron with variations in each of layers 2–6.

Slide 24:

The images show the neocortical column (NCC)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 . a . The microcircuits (from left to right) for layers 2, 3, 4 and 5. b . A single thick tufted layer 5 pyramidal neuron located within the column. c. One pyramidal neuron in layer 2, a small pyramidal neuron in layer 5 and the large thick tufted pyramidal neuron in layer 5. d . An image of the NCC, with neurons located in layers 2 to 5. Reconstructing the neocortical column.

Slide 25:

Visualization of an entire neocortical column with network activity.

Whole-brain simulations ::

Whole-brain simulations : Limitations : extreme temporal and spatial resolutions demanded, and Algorithms that are used to model biological processes. The most powerful supercomputers 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.

Slide 27:

Computational power needs to increase about 1-million-fold before we will be able to simulate the human brain, with 100 billion neurons, at the same level of detail as the Blue Column. Simulating the NCC could also act as a test-bed to refine algorithms required to simulate brain function, which can be used to produce field programmable gate array (FPGA)-based chips.

Slide 28:

A field-programmable gate array ( FPGA ) is an integrated circuit designed to be configured by the customer or designer after manufacturing—hence " field-programmable “. i.e Partially Configured. FPGAs could increase computational speeds by as much as two orders of magnitude

Slide 29:

References : Nature reviews : Neuroscience http://bluebrain.epfl.ch/

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