Node connection strength in Neurotree.
Each node in Neurotree can be characterized by its mean distance from every other
node. Below is a histogram of mean distances for every node in the tree.
(The final bin includes nodes that are not connected to the main tree.)
Mean inter-neuroscientist distance
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5668- |
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4534- |
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3401- |
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2267- |
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1134- |
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5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
16 |
17 |
18 |
19 |
20+ |
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Mean distance |
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Number of neuroscientists |
20 most tightly coupled nodes.
Below are the nodes with shortest mean distance. Note the strong bias
toward systems and, in particular, the visual system. This suggests either that visual neuroscientists are highly promiscuous or that the population of the tree is biased by having been started in a vision lab. This question will only be answered with more data!
| Rank |
Mean dist |
Name |
Institution |
Area |
Date |
| 1 |
4.9 |
Terrence J Sejnowski (Info) |
Salk Institute for Biological Studies |
Computation & Theory |
2005-01-15 |
|
| 2 |
5.04 |
Stephen Kuffler (Info) |
Harvard University |
Visual system |
2005-01-15 |
|
| 3 |
5.12 |
Torsten Wiesel (Info) |
Rockefeller University |
Visual system |
2005-01-15 |
|
| 4 |
5.25 |
Rodolfo R. Llinas (Info) |
New York University |
channel physiology, cerebellum, thalamus, cortex, synaptic transmission, MEG, inferior olive, calcium currents |
2005-01-27 |
|
| 5 |
5.28 |
John Carew Eccles (Info) |
Australian National University |
Synapses |
2005-01-16 |
|
| 6 |
5.28 |
Eric Kandel (Info) |
Columbia University |
Learning and Memory |
2005-01-26 |
|
| 7 |
5.29 |
John D.E. Gabrieli (Info) |
Massachusetts Institute of Technology |
Cognitive neuroscience |
2005-01-26 |
|
| 8 |
5.38 |
Paul Greengard (Info) |
Rockefeller University |
http://www.researchprofiles.collexis.com/jad/expert.asp?u_id=67 |
2005-01-26 |
|
| 9 |
5.4 |
David C Van Essen (Info) |
Washington University, Saint Louis |
Visual system |
2005-01-15 |
|
| 10 |
5.43 |
Peter Schiller (Info) |
Massachusetts Institute of Technology |
Visual system |
2005-01-15 |
|
| 11 |
5.43 |
David Hubel (Info) |
Harvard University |
Vision |
2005-01-16 |
|
| 12 |
5.47 |
Mark D'Esposito (Info) |
University of California, Berkeley |
Cognitive Neuroscience |
2005-01-22 |
|
| 13 |
5.48 |
Sabine Kastner (Info) |
Princeton University |
|
2005-01-16 |
|
| 14 |
5.5 |
Michael P Stryker (Info) |
University of California, San Francisco |
Development, Visual system |
2005-01-20 |
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| 15 |
5.51 |
Roger A Nicoll (Info) |
University of California, San Francisco |
Neurobiology |
2005-08-03 |
|
| 16 |
5.54 |
Peter Dayan (Info) |
University College London |
|
2005-08-10 |
|
| 17 |
5.55 |
Horace Barlow (Info) |
University of Cambridge |
Computation & Theory |
2005-01-15 |
|
| 18 |
5.56 |
Mortimer Mishkin (Info) |
National Institute of Mental Health |
Systems |
2005-01-15 |
|
| 19 |
5.57 |
Heinrich H. Buelthoff (Info) |
Max Planck Institute for Biological Cybernetics |
Psychophysics, Cognition, Computer Vision, Robotics |
2005-02-18 |
|
| 20 |
5.59 |
John Nicholls (Info) |
SISSA |
Regeneration |
2005-09-08 |
|
Distribution of individual connectivity.
Another way to look at the Neurotree graph is to plot a histogram of
researchers (nodes) based according to the number of immediate connections
(edges) they have to other researchers. The final bin includes nodes with
or more connections. The actual distribution has a very long tail, with a maximum of 109 connections. Thanks to Adam Snyder for suggesting this analysis!
Edge vs node distribution
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13595- |
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10876- |
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8157- |
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5438- |
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2719- |
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1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
16+ |
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Number of connections |
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Neuroscientist count |
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