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Brain simulation

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Sepehr Rasouli

This document discusses brain simulation and neuroscience. It begins by defining neuroscience as the study of the brain and nervous system, and their interactions with other physiological systems. This allows researchers to understand human behavior, improve health, and study learning and memory. The document then discusses neuron anatomy and different theoretical approaches in neuroscience like informatics, theoretical neuroscience, and computational neuroscience. It outlines principles of brain simulation like dense reconstruction from sparse data and iterative reconstruction and testing. Major brain simulation projects are summarized, including the Blue Brain Project which aims to simulate the entire human brain on a supercomputer.

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Brain Simulation Sepehr Rasouli Computer Simulation Class winter 2018
Neuroscience Introduction
WHAT IS NEUROSCIENCE? WHY IS IT IMPORTANT? ▪ The study of the brain and the nervous system, their interactions with other physiological systems ▪ Understand Human Behavior ▪ Improve human psychological and somatic health ▪ Understand learning & memory 3
4 Neuron Anatomy (Brain Cells)
Brain simulation
THEORY - HYPOTHESIZE ▪ Informatics Try to find out the trends, correlations, and patterns in the data. ▪ Theoretical Neuroscience Try to explain the data, to predict outcomes of an experiment according to a theory about the data and the observation. 6
THEORY - HYPOTHESIZE ▪ Computational Neuroscience Try to build a model to replicate the experimental data, more generally, to build a minimal model which can fit specific experimental data and explain a phenomenon in the simplest possible way with your deepest understanding. 7
SIMULATION – UNIFY THE THEORIES 1. Consider every detail of the brain 2. Integrate data according to current knowledge 3. Fill gaps using hypotheses 8
9
SIMULATION PRINCIPLES 1. Dense Reconstruction from sparse Data 10
SIMULATION PRINCIPLES 2. Reconstruct Bottom-up 1. Follow biological principles 2. Build the smallest components first 3. Freeze component 4. Combine components 5. Validate against emergent properties 6. Never fit to emergent properties and if you have to then only the very next level up 11
Scheme of Brain High-level sketch of the brain structures, with connections based on different types of neurotransmitters marked in different colors. 12
SIMULATION PRINCIPLES 3. Iteratively Reconstruct and Test ▫ New understanding when it works ▫ New knowledge when it fail 13
SIMULATION REQUIRMENTS Computing Requirments 1. A Supercomputer 2. Parallel code for simulation 3. Challenges 1. Algorithmic Efficiency 2. Load balance 3. Resource management 4. Data managment 14
SIMULATION STRATEGIES Value of the Simulation 1. Validation Tests 1. Behaviour as expected? 2. Reproduces biological experiments? 15
SIMULATION PROJECTS
EPFL Blue Brain Project ▪ The Blue Brain Project was launched by the Brain Mind Institute, EPFL, Switzerland and IBM, USA in May’05, now over 120'000 WWW pages. ▪ The EPFL Blue Gene is the 8th fastest supercomputer in the world. Can simulate about 100M minimal compartment neurons or 10- 50'000 multi-compartmental neurons, with 103-104 x more synapses. Next generation BG will simulate >109 neurons with significant complexity. 17
Models at different level of Complexity 1. The Blue Synapse: A molecular level model of a single synapse. 2. The Blue Neuron: A molecular level model of a single neuron. 3. The Blue Column: A cellular level model of the Neocortical column with 10K neurons, later 50K, 100M connections. 4. The Blue Neocortex: A simplified Blue Column will be duplicated to produce Neocortical regions and eventually and entire Neocortex. 5. The Blue Brain Project will also build models of other Cortical and Subcortical models of the brain, and sensory + motor organs. 18
Blue BrainThe supercomputer-based reconstructions and simulations built by the Blue Brain Project offer a radically new approach for understanding the multilevel structure and function of the brain. Digital reconstructions of brain tissue represent a snapshot of the anatomy and physiology of the brain at one moment in time. 19 EPFL BLUE BRAIN PROJECT The Neocortical Microcircuitry neocortex of the highly organized network of neurons
20 Virtual golgi staining Simulated image of five pyramidal neurons stained virtually with Golgi’s stain and visualized using a simulated microscope Virtual brainbow of the neocortex Staining of neurons on different layers with fluorescent proteins.
BLUE BRAIN 21
22 http://mouse.brain-map.org/static/brainexplorer
Conscious machines: Haikonen ▪ Haikonen has done some simulations based on a rather straightforward design, with neural models feeding the sensory information (with WTA associative memory) into the associative “working memory” circuits. 23
What does the future hold? ▪ more integration between the separate fields of science, such as brain sciences, genetics and computer science. ▪ Reaching high levels of computation in super computers 24
25 Ray Kurzweil's 2005 book The Singularity Is Near: When Humans Transcend Biology
Resources ▪ Eric R. Kandel, J. H. Schwartz, and Thomas M. Jessell. Principles of Neural Science. McGraw-Hill Medical, 5th edition, July 2012. ▪ EPFL Simulation Neuroscience Online Course https://actu.epfl.ch/news/mooc-simulation- neuroscience-reconstruction-of-a-s/ 26
27 THANKS! ANY QUESTIONS?

More Related Content

Brain simulation

  • 3. WHAT IS NEUROSCIENCE? WHY IS IT IMPORTANT? ▪ The study of the brain and the nervous system, their interactions with other physiological systems ▪ Understand Human Behavior ▪ Improve human psychological and somatic health ▪ Understand learning & memory 3
  • 6. THEORY - HYPOTHESIZE ▪ Informatics Try to find out the trends, correlations, and patterns in the data. ▪ Theoretical Neuroscience Try to explain the data, to predict outcomes of an experiment according to a theory about the data and the observation. 6
  • 7. THEORY - HYPOTHESIZE ▪ Computational Neuroscience Try to build a model to replicate the experimental data, more generally, to build a minimal model which can fit specific experimental data and explain a phenomenon in the simplest possible way with your deepest understanding. 7
  • 8. SIMULATION – UNIFY THE THEORIES 1. Consider every detail of the brain 2. Integrate data according to current knowledge 3. Fill gaps using hypotheses 8
  • 9. 9
  • 10. SIMULATION PRINCIPLES 1. Dense Reconstruction from sparse Data 10
  • 11. SIMULATION PRINCIPLES 2. Reconstruct Bottom-up 1. Follow biological principles 2. Build the smallest components first 3. Freeze component 4. Combine components 5. Validate against emergent properties 6. Never fit to emergent properties and if you have to then only the very next level up 11
  • 12. Scheme of Brain High-level sketch of the brain structures, with connections based on different types of neurotransmitters marked in different colors. 12
  • 13. SIMULATION PRINCIPLES 3. Iteratively Reconstruct and Test ▫ New understanding when it works ▫ New knowledge when it fail 13
  • 14. SIMULATION REQUIRMENTS Computing Requirments 1. A Supercomputer 2. Parallel code for simulation 3. Challenges 1. Algorithmic Efficiency 2. Load balance 3. Resource management 4. Data managment 14
  • 15. SIMULATION STRATEGIES Value of the Simulation 1. Validation Tests 1. Behaviour as expected? 2. Reproduces biological experiments? 15
  • 17. EPFL Blue Brain Project ▪ The Blue Brain Project was launched by the Brain Mind Institute, EPFL, Switzerland and IBM, USA in May’05, now over 120'000 WWW pages. ▪ The EPFL Blue Gene is the 8th fastest supercomputer in the world. Can simulate about 100M minimal compartment neurons or 10- 50'000 multi-compartmental neurons, with 103-104 x more synapses. Next generation BG will simulate >109 neurons with significant complexity. 17
  • 18. Models at different level of Complexity 1. The Blue Synapse: A molecular level model of a single synapse. 2. The Blue Neuron: A molecular level model of a single neuron. 3. The Blue Column: A cellular level model of the Neocortical column with 10K neurons, later 50K, 100M connections. 4. The Blue Neocortex: A simplified Blue Column will be duplicated to produce Neocortical regions and eventually and entire Neocortex. 5. The Blue Brain Project will also build models of other Cortical and Subcortical models of the brain, and sensory + motor organs. 18
  • 19. Blue BrainThe supercomputer-based reconstructions and simulations built by the Blue Brain Project offer a radically new approach for understanding the multilevel structure and function of the brain. Digital reconstructions of brain tissue represent a snapshot of the anatomy and physiology of the brain at one moment in time. 19 EPFL BLUE BRAIN PROJECT The Neocortical Microcircuitry neocortex of the highly organized network of neurons
  • 20. 20 Virtual golgi staining Simulated image of five pyramidal neurons stained virtually with Golgi’s stain and visualized using a simulated microscope Virtual brainbow of the neocortex Staining of neurons on different layers with fluorescent proteins.
  • 23. Conscious machines: Haikonen ▪ Haikonen has done some simulations based on a rather straightforward design, with neural models feeding the sensory information (with WTA associative memory) into the associative “working memory” circuits. 23
  • 24. What does the future hold? ▪ more integration between the separate fields of science, such as brain sciences, genetics and computer science. ▪ Reaching high levels of computation in super computers 24
  • 25. 25 Ray Kurzweil's 2005 book The Singularity Is Near: When Humans Transcend Biology
  • 26. Resources ▪ Eric R. Kandel, J. H. Schwartz, and Thomas M. Jessell. Principles of Neural Science. McGraw-Hill Medical, 5th edition, July 2012. ▪ EPFL Simulation Neuroscience Online Course https://actu.epfl.ch/news/mooc-simulation- neuroscience-reconstruction-of-a-s/ 26