The document presents an overview of a proposed brain simulator that models brain structures and functions to enhance understanding of human cognition and related diseases. It highlights brain informatics as an interdisciplinary field that utilizes advanced technologies including network analysis to study complex brain networks. The ongoing development of the simulator aims to create an accurate representation of the human brain for further research and testing.

1. On the Development of a Brain Simulator
Author: Wen-Hsien Tseng, Song-Yun Lu, Hsing Mei
Web Computing Laboratory(WECO Lab)
Computer Science and Information Engineering Department
Fu Jen Catholic University

2. Outline
 Introduction
 Background
 The proposed brain simulator
 Conclusion and future work
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3. Introduction
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4. Introduction
 Brain Informatics (BI) is a new
interdisciplinary field to study Human
Information Processing System (HIPS)
mechanism systematically from both macro
and micro points of view by cooperatively
using
 Experimental brain/cognitive technology
 Web intelligence centric advanced
information technology
 Ex: data mining, graph analysis, layered analysis
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5. Introduction
Web Infrastructure
Overlay Model
Layered Protocol
Deep Web Intelligence
Deep Web
Intelligence
Behavioral : Learning, Education
Cognitive : Psychology Macro : Brain
Micro : Neuron
Cognitive Neuroscience
Science (Brain)
Brain Informatics
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6. Introduction
 Brain simulator is a computer simulation in
modeling brain structural dynamics and
functionality
 Can be applied to the studies of brain
related disease
 Ex: Alzheimer’s disease, sleep disorder,
epilepsy, etc.
 It is a great steps toward better
understanding of the human brain
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7. Background
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8. Background
 Network analysis
 Brain networks are complex networks
 complex network analysis has been developed
as an emerging research field
 It is based on the classical graph theory and
mathematical models
 We applied network analysis to describe the
brain networks
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9. Background
 The human connectome project
 Aims to map the connectivity of the human
brain.
 Brain networks
Three basic brain networks:
 Thalamocortical Motif
 Polysynaptic Loop Structure
 Diffuse Ascending Projection
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10. Background
Thalamocortical
motif
Polysynaptic loop Defuse ascending projection
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11. Background
 Internet vs. human brain
Internet Human Brain
Scale Millions of unit elements 1011 unit elements
Anatomical structure, network
Layered structure OSI model
overlay, and functional outputs
Mechanisms of fault Error correction, recomputation Degeneracy mechanism,
tolerance of routing pathway replaceable functional area
Properties of complex Motif, communities, hubs, Motif, communities, hubs, shortest
networks shortest path way, etc. path way, etc.
Capability of an unit
Versatile Specific
element
Physical structure Relatively Stable Relatively Dynamic
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12. Background
 Current Status of the Brain Simulator
Our Proposed
IBM’s C2 Blue Brain
Brain Simulator
Neuron-Level Neuron-Level Brain-Level
Perspective
Microscopic Microscopic Macroscopic
Basic Nuclei, Region,
Neuron Neuron
Component Tracts
Communication
Connection Synapse Synapse
Pathway
Protocol Data
Communication Electrical Signal Electrical Signal
Unit
layered layered
Architecture P2P Network
Architecture Architecture
Neocortical
Focus Area Cortex Whole Brain
column
Supercomputer Supercomputer Cloud Computing
Computation
Blue Gene Blue Gene Environment
Granularity Fine-grained Fine-grained Coarse-grained
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13. The Proposed Brain Simulator
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14. The Proposed Brain Simulator
 Provides an overlay and P2P network to
represent the complexity and dynamicity of
brain in time and space
 Case-based Incremental Delivery Approach
Define outline Assign requirements Design system
requirements to increments architecture
Develop system Validate Integrate Validate Final system
increment increment increment system
System incomplete
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15. The Proposed Brain Simulator
 Architecture
A layered architecture is used to design our brain
simulator according to our study on human brain.
The layered architecture consists of the following
layer:
 Brain Connectivity Layer
 Processing Layer
 Causal Layer
 Application Layer
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16. Short Long
Term Term
Time Scale
Sleep Learning Brain Disease Aging
Resting State Decision Brain Disease Neural Darwin
Making Model Model Selection Application Layer
……
REM
Stage N1
Stage N2 Network Network (Behavior/Disease
Damage Model Development
Stage N3
Model
Layer)
Causal Layer
…… (Overlay
Layer)
By case
……… Processing Layer
Thalamocortical Motif Polysynaptic Loops Diffuse Ascending Projection
Brain Connectivity
Layer
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17. Conclusion and Future Work
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18. Conclusion and Future Work
 A brain simulator models brain structure and
functionality for better understanding how
cognition works and rapidly testing their ideas
on an accurate replica of the brain
 The development of proposed brain simulator
is still ongoing. In order to simulate more
accurate human brain structure and function,
further study is required.
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19. Reference
1. Zhong, N., Li, K., Lu, S.; Chen, L. (Eds.): Brain Informatics. Springer (2010)
2. Sendhoff, B., Körner, E., Sporns, O., Ritter, H., Doya, K. (Eds.): Creating Brain-
Like Intelligence: From Basic Principles to Complex Intelligent Systems.
Springer (2009)
3. Catani, M., Ffytche, D. H.: The Rises and Falls of Disconnection Syndromes. J.
Brain. 128, 2224--2239 (2005)
4. Catani, M., Ffytche, D. H.: Graph Theoretical Analysis of
Magnetoencephalographic Functional Connectivity in Alzheimer’s Disease. J.
Brain. 132, 213--224 (2009)
5. Deuschl, G. et al.: Deep-Brain Stimulation for Parkinson's Disease. J.
Neurology. 249, 36--39 (2002)
6. Tononi,G., Edelman, G. M., Sporns, O.: Complexity and Coherency: Integrating
Information in the Brain. J. Trends in Cognitive Science. 2, 474--484 (1998)
7. Strogatz, S. H.: Exploring complex network. J. Nature. 410, 268--276 (2001)
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20. Reference
8. Bullmore, E., Sporns, O.: Complex Brain Networks: Graph Theoretical Analysis
of Structural and Functional Systems. J. Nat. Rev. Neurosci. 10, 186--198
(2009)
9. Sporns, O., Chialvo, D. R., Kaiser, M., Hilgetag, C. C.: Organization, Development
and Function of Complex Brain Networks. J. Trends in Cognitive Science. 8,
418--425 (2004)
10. Mikail Rubinov, Olaf Sporns, “Complex network measures of brain
connectivity: uses and interpretations”, NeuroImage (2009)
11. NIH Launches the Human Connectome Project to Unravel the Brain
Connections, http://www.nih.gov/news/health/jul2009/ninds-15.htm
12. Human Connectome Project, http://www.humanconnectomeproject.org/
13. Sporns, O., Tononi, G., Kotter, R.: The Human Connectome: A Structural
Description of the Human Brain. J. PLoS Comput. Biol. 1(4), e42 (2005)
14. Edelman, G. M.: Wider than the Sky: The Phenomenal Gift of Consciousness.
Yale University Press (2004)
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21. Reference
15. Thalamocortical Radiations,
http://en.wikipedia.org/wiki/Thalamocortical_radiations
16. Basal Ganglia, http://en.wikipedia.org/wiki/Basal_ganglia
17. Neurotransmitter, http://en.wikipedia.org/wiki/Neurotransmitter
18. IBM Unveils a New Brain Simulator,
http://spectrum.ieee.org/computing/hardware/ibm-unveils-a-new-brain-
simulator
19. Blue Brain Project, http://bluebrain.epfl.ch/
20. Markram, H.: The Blue Brain Project. J. Nat. Rev. Neurosci. 7, 153--160 (2006)
21. King, J. G. et al.: A Component-based Extension Framework for Large-scale
Parallel Simulations in NEURON. J. Frontiers in Neuroinformatics. 3, (2009)
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22. Thanks for Your Attention!
Q&A
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