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Brain Networks
    Thalamocortical Motif

                  Speaker : Jimmy Lu
                  Advisor : Hsing Mei

        Web Computing Laboratory(WECO Lab)
Computer Science and Information Engineering Department
                Fu Jen Catholic University
Outline
 Report proposal
 Background and review
 Small-world network
 Scale-free network
 Reentrant mapping
 Links, issues, and opinions
 Future work
                  WECO Lab, CSIE dept., FJU
2010/3/19                                     2
                   http://www.weco.net
Report Proposal
 Thalamocortical motif
 Polysynatic loop structure
 Diffuse ascending projection
 Cognitive science
 Social networks
 Collective intelligence
 Web intelligence
                WECO Lab, CSIE dept., FJU
2010/3/19                                   3
                 http://www.weco.net
Background and Review                   (1/3)




 Thalamus
      process sensory
       information as well
       as relaying it
      As a gateway, a
       switch, or a relay
      Reticular nucleus,
       intralaminar nuclei
                    WECO Lab, CSIE dept., FJU
2010/3/19                                               4
                     http://www.weco.net
Background and Review                   (2/3)




 Cerebral cortex
      Brain map
      Plays a key role
       in memory, attention,
       perceptual awareness,
       thought, language,
       and consciousness
      Small-world, Scale-free
                    WECO Lab, CSIE dept., FJU
2010/3/19                                               5
                     http://www.weco.net
Background and Review                 (3/3)




Thalamocortical radiations
 Reentrant mapping
 Sensory information
  go through the spine
  cord to the thalamus,
  and then relay it to
  the cortical areas
 Dynamic core
                  WECO Lab, CSIE dept., FJU
2010/3/19                                             6
                   http://www.weco.net
Small-world network                 (1/7)




 The intermediate region between
  regular and random




                WECO Lab, CSIE dept., FJU
2010/3/19                                           7
                 http://www.weco.net
Small-world network                 (2/7)




 Clustering coefficient and path length




                WECO Lab, CSIE dept., FJU
2010/3/19                                           8
                 http://www.weco.net
Small-world network                      (3/7)




 Three examples
      Film actors
      Power grid
      C. elegans




                     WECO Lab, CSIE dept., FJU
2010/3/19                                                9
                      http://www.weco.net
Small-world network                  (4/7)




 The time to global infection is nearly as
  short as for a random graph




                 WECO Lab, CSIE dept., FJU
2010/3/19                                            10
                  http://www.weco.net
Small-world network                 (5/7)




 Small-world in the brain: micro view




                WECO Lab, CSIE dept., FJU
2010/3/19                                           11
                 http://www.weco.net
Small-world network                 (6/7)




 Small-world in the brain: macro view




                WECO Lab, CSIE dept., FJU
2010/3/19                                           12
                 http://www.weco.net
Small-world network                     (7/7)




 Small-world in the brain
      Truncated power law
      Economical(close, cost, conservation)
      Global efficiency, short cut
      High degree nodes are less often highly
       clustered
      Isomorphism, patterns of connectivity
      Robust, less vulnerable, balance
                    WECO Lab, CSIE dept., FJU
2010/3/19                                               13
                     http://www.weco.net
Scale-free network                      (1/3)




 The problem of ER and WS model
      The probability with which a new vertex
       connects to the existing vertices is not
       uniform
      Most real world networks are open. The
       number of vertices may increase



                    WECO Lab, CSIE dept., FJU
2010/3/19                                               14
                     http://www.weco.net
Scale-free network                       (2/3)




 Two mechanisms
      Networks expand continuously by the
       addition of new vertices
      New vertices attach preferentially to sites
       that are already well connected
 The development of large networks is
  governed by robust-organizing
  phenomena
                     WECO Lab, CSIE dept., FJU
2010/3/19                                                15
                      http://www.weco.net
Scale-free network                       (3/3)




 Power law distribution
      Citation of scientific papers
      Rich-get-richer




                     WECO Lab, CSIE dept., FJU
2010/3/19                                                16
                      http://www.weco.net
Reentrant mapping                      (1/3)




 Directions
      Corticocortical
      Thalamocortical
      Corticothalamic


 Mutual information


                   WECO Lab, CSIE dept., FJU
2010/3/19                                              17
                    http://www.weco.net
Reentrant mapping                   (2/3)




 Theory of Neuronal Group Selection




                WECO Lab, CSIE dept., FJU
2010/3/19                                           18
                 http://www.weco.net
Reentrant mapping                   (3/3)




 Reentrant mapping and consciousness




                WECO Lab, CSIE dept., FJU
2010/3/19                                           19
                 http://www.weco.net
Links, issues, and opinions          (1/4)




 Undirected and Directed graph(I/O)?
 Hubs/weighted graph ?
 Does a vertex which connect to a hub
  increase the probability of being
  attached?
 The region between small-world and
  scale-free?
                 WECO Lab, CSIE dept., FJU
2010/3/19                                       20
                  http://www.weco.net
Links, issues, and opinions           (2/4)




 Three types of projections and
  structural, functional, and effective
  point of view
 Micro/macro, local/global
 Structural-functional relation
 Reconfiguration, dynamics
 Is the hypothesis of reentry right?

                  WECO Lab, CSIE dept., FJU
2010/3/19                                        21
                   http://www.weco.net
Links, issues, and opinions          (3/4)




 What parameters can be mapped from
  brain to social networks?

                     Small-world




                      Degeneracy?




                 WECO Lab, CSIE dept., FJU
2010/3/19                                       22
                  http://www.weco.net
Links, issues, and opinions             (4/4)




 What’s the stimulus of social network?
 What stimulus force the structure of
  social networks change?
 What characteristics should a brain
  simulator has?
      Degeneracy, evolutionary, self-organize,
       etc…

                    WECO Lab, CSIE dept., FJU
2010/3/19                                          23
                     http://www.weco.net
Reference
 [1] Ed Bullmore, Olaf Sporns, “Complex brain networks: graph
  theoretical analysis of structural and functional systems”, Nature
  Reviews Neuroscience 10, 186-198 (March 2009)
 [2] Duncan J. Watts, Steven H. Strogatz, “Collective dynamics of 'small-
  world' networks”, Nature 393, 440-442 (4 June 1998)
 [3] Albert-László Barabási, Réka Albert, “Emergence of Scaling in
  Random Networks”, Science 15 October 1999: Vol. 286. no. 5439, pp.
  509 – 512
 [4] Gerald M. Edelman, “Wider than the Sky: The Phenomenal Gift of
  Consciousness”, Yale University Press (March 10, 2004)




                             WECO Lab, CSIE dept., FJU
2010/3/19                                                                24
                              http://www.weco.net

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Brain Network - Thalamocortical Motif

  • 1. Brain Networks Thalamocortical Motif Speaker : Jimmy Lu Advisor : Hsing Mei Web Computing Laboratory(WECO Lab) Computer Science and Information Engineering Department Fu Jen Catholic University
  • 2. Outline  Report proposal  Background and review  Small-world network  Scale-free network  Reentrant mapping  Links, issues, and opinions  Future work WECO Lab, CSIE dept., FJU 2010/3/19 2 http://www.weco.net
  • 3. Report Proposal  Thalamocortical motif  Polysynatic loop structure  Diffuse ascending projection  Cognitive science  Social networks  Collective intelligence  Web intelligence WECO Lab, CSIE dept., FJU 2010/3/19 3 http://www.weco.net
  • 4. Background and Review (1/3)  Thalamus  process sensory information as well as relaying it  As a gateway, a switch, or a relay  Reticular nucleus, intralaminar nuclei WECO Lab, CSIE dept., FJU 2010/3/19 4 http://www.weco.net
  • 5. Background and Review (2/3)  Cerebral cortex  Brain map  Plays a key role in memory, attention, perceptual awareness, thought, language, and consciousness  Small-world, Scale-free WECO Lab, CSIE dept., FJU 2010/3/19 5 http://www.weco.net
  • 6. Background and Review (3/3) Thalamocortical radiations  Reentrant mapping  Sensory information go through the spine cord to the thalamus, and then relay it to the cortical areas  Dynamic core WECO Lab, CSIE dept., FJU 2010/3/19 6 http://www.weco.net
  • 7. Small-world network (1/7)  The intermediate region between regular and random WECO Lab, CSIE dept., FJU 2010/3/19 7 http://www.weco.net
  • 8. Small-world network (2/7)  Clustering coefficient and path length WECO Lab, CSIE dept., FJU 2010/3/19 8 http://www.weco.net
  • 9. Small-world network (3/7)  Three examples  Film actors  Power grid  C. elegans WECO Lab, CSIE dept., FJU 2010/3/19 9 http://www.weco.net
  • 10. Small-world network (4/7)  The time to global infection is nearly as short as for a random graph WECO Lab, CSIE dept., FJU 2010/3/19 10 http://www.weco.net
  • 11. Small-world network (5/7)  Small-world in the brain: micro view WECO Lab, CSIE dept., FJU 2010/3/19 11 http://www.weco.net
  • 12. Small-world network (6/7)  Small-world in the brain: macro view WECO Lab, CSIE dept., FJU 2010/3/19 12 http://www.weco.net
  • 13. Small-world network (7/7)  Small-world in the brain  Truncated power law  Economical(close, cost, conservation)  Global efficiency, short cut  High degree nodes are less often highly clustered  Isomorphism, patterns of connectivity  Robust, less vulnerable, balance WECO Lab, CSIE dept., FJU 2010/3/19 13 http://www.weco.net
  • 14. Scale-free network (1/3)  The problem of ER and WS model  The probability with which a new vertex connects to the existing vertices is not uniform  Most real world networks are open. The number of vertices may increase WECO Lab, CSIE dept., FJU 2010/3/19 14 http://www.weco.net
  • 15. Scale-free network (2/3)  Two mechanisms  Networks expand continuously by the addition of new vertices  New vertices attach preferentially to sites that are already well connected  The development of large networks is governed by robust-organizing phenomena WECO Lab, CSIE dept., FJU 2010/3/19 15 http://www.weco.net
  • 16. Scale-free network (3/3)  Power law distribution  Citation of scientific papers  Rich-get-richer WECO Lab, CSIE dept., FJU 2010/3/19 16 http://www.weco.net
  • 17. Reentrant mapping (1/3)  Directions  Corticocortical  Thalamocortical  Corticothalamic  Mutual information WECO Lab, CSIE dept., FJU 2010/3/19 17 http://www.weco.net
  • 18. Reentrant mapping (2/3)  Theory of Neuronal Group Selection WECO Lab, CSIE dept., FJU 2010/3/19 18 http://www.weco.net
  • 19. Reentrant mapping (3/3)  Reentrant mapping and consciousness WECO Lab, CSIE dept., FJU 2010/3/19 19 http://www.weco.net
  • 20. Links, issues, and opinions (1/4)  Undirected and Directed graph(I/O)?  Hubs/weighted graph ?  Does a vertex which connect to a hub increase the probability of being attached?  The region between small-world and scale-free? WECO Lab, CSIE dept., FJU 2010/3/19 20 http://www.weco.net
  • 21. Links, issues, and opinions (2/4)  Three types of projections and structural, functional, and effective point of view  Micro/macro, local/global  Structural-functional relation  Reconfiguration, dynamics  Is the hypothesis of reentry right? WECO Lab, CSIE dept., FJU 2010/3/19 21 http://www.weco.net
  • 22. Links, issues, and opinions (3/4)  What parameters can be mapped from brain to social networks? Small-world Degeneracy? WECO Lab, CSIE dept., FJU 2010/3/19 22 http://www.weco.net
  • 23. Links, issues, and opinions (4/4)  What’s the stimulus of social network?  What stimulus force the structure of social networks change?  What characteristics should a brain simulator has?  Degeneracy, evolutionary, self-organize, etc… WECO Lab, CSIE dept., FJU 2010/3/19 23 http://www.weco.net
  • 24. Reference  [1] Ed Bullmore, Olaf Sporns, “Complex brain networks: graph theoretical analysis of structural and functional systems”, Nature Reviews Neuroscience 10, 186-198 (March 2009)  [2] Duncan J. Watts, Steven H. Strogatz, “Collective dynamics of 'small- world' networks”, Nature 393, 440-442 (4 June 1998)  [3] Albert-László Barabási, Réka Albert, “Emergence of Scaling in Random Networks”, Science 15 October 1999: Vol. 286. no. 5439, pp. 509 – 512  [4] Gerald M. Edelman, “Wider than the Sky: The Phenomenal Gift of Consciousness”, Yale University Press (March 10, 2004) WECO Lab, CSIE dept., FJU 2010/3/19 24 http://www.weco.net