The Blue Brain project aims to create a digital reconstruction of the human brain by simulating neural circuits at a microscopic level. The initial phase involved modeling a small cubic millimeter of rat brain containing 10,000 neurons. Future phases will expand this to model larger sections of the rat brain and eventually the entire human brain through continued data collection, algorithm improvements, and increased computing power. The goal is to better understand brain function and emergent behaviors through observing interactions between simulated neurons.

1. Blue Brain
Do neurons dream of
electric sheep?
By: Michael R. Rink

2. Main Points
 Objectives of the Project
 Data Collection
 Model
 Expected Consequences
 Future Work

3. Objectives
 Phase 1: 1cm^3 NCC
 June 6, 2005 – November 26, 2007
 Detailed Cellular scale (10,000 neurons)
 Full Neocortex
 Modular
 Atomic (days/ms), Genetic (another 5-10
years)
 Cellular – 100,000
 Point Source -100 million (mouse brain)

4. Full Mammal Brain
 2 years
 Emergent behavior
 Learning/ self-wiring
 Interact with environment
 Japanese built robot rat
 Whole new models to connect sensory cortex
to hardware

5. Full Human Brain
 Need speed increase of ~10million fold
 Algorithm improvements 100-1,000 fold
 ASICS 1,000-10,000 fold
 Multi-core & Multi-threading are really
useful

6. On Going Data Collection
 Decades of
research on 2
year old rat
somatosensory
cortex
 Hundreds of new
data points a day
 50% success rate Fig A. Patch Clamp Robot

7. Blue Column 1
The
microcircuits
(from left to
right) for
layers 2, 3, 4
and 5

8. Blue Column 2
 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.

9. Blue Column Construction
 Model the 3d neuron
 Repair defects from in vitro preparation
 Patch clamping damages the cell
 Insert 12+ ion channels in each
component compartment based on HH
models & studies
 October
2005 Petilla Classification
Convention

10.  Each neuron is unique within statistically
determined bounds – procedurally generated
 Stored in database after creation
 Each layer procedurally generated from
database then shifted/rotated to avoid collisions
 Generating the column takes longer than running it
 Simulation runs at 1/100th real time

11.  Individual synapses functionalized and
plasticized with learning algorithms
 Map Neuron to Processor 1:1 – 10:1
 Axons mapped onto inter-processor communications

12. Expected Consequences
 Each NCC will be unique
 Hopefully,NCC output will approach
convergence
 NCCs will begin to appear similar (lack of
molecular detail to tie in genetic elements)
 Connected Neurons fire together
 Predictedby Hebbian learning
 Emergent behavior

13.  Blue = rest
 Yellow = group 1
 Red = group 2

14.  Global
Excitatory state
 Notice the blue,
inhibited
neurons

15. Future Work
 Experimental Aid
 How complex of a problem can an NCC
solve
 Emergence of Conciousness
 Director Markram

16. Any Questions?