principles of brain simulation and famous brain simulation projects

1. Brain Simulation
Sepehr Rasouli
Computer
Simulation Class
winter 2018

2. Neuroscience
Introduction

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

4. 4
Neuron Anatomy
(Brain Cells)

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

16. SIMULATION
PROJECTS

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.

21. BLUE BRAIN
21

22. 22
http://mouse.brain-map.org/static/brainexplorer

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

27. 27
THANKS!
ANY QUESTIONS?