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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5274- |
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4219- |
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3164- |
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2110- |
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1055- |
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5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
16 |
17 |
18 |
19+ |
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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 |
5.33 |
Stephen Kuffler (Info) |
Harvard University |
Visual system |
2005-01-15 |
|
| 2 |
5.39 |
Terrence J Sejnowski (Info) |
Salk Institute for Biological Studies |
Computation & Theory |
2005-01-15 |
|
| 3 |
5.41 |
Torsten Wiesel (Info) |
Rockefeller University |
Visual system |
2005-01-15 |
|
| 4 |
5.43 |
John Carew Eccles (Info) |
Australian National University |
Synapses |
2005-01-16 |
|
| 5 |
5.49 |
Eric Kandel (Info) |
Columbia University |
Learning and Memory |
2005-01-26 |
|
| 6 |
5.62 |
Peter Schiller (Info) |
Massachusetts Institute of Technology |
Visual system |
2005-01-15 |
|
| 7 |
5.65 |
David Hubel (Info) |
Harvard University |
Vision |
2005-01-16 |
|
| 8 |
5.67 |
David C Van Essen (Info) |
Washington University, Saint Louis |
Visual system |
2005-01-15 |
|
| 9 |
5.71 |
John D.E. Gabrieli (Info) |
Massachusetts Institute of Technology |
Cognitive neuroscience |
2005-01-26 |
|
| 10 |
5.77 |
Otto D Creutzfeldt (Info) |
Kraepelin Institute (Munich) |
visual system |
2005-01-16 |
|
| 11 |
5.77 |
Michael P Stryker (Info) |
University of California, San Francisco |
Development, Visual system |
2005-01-20 |
|
| 12 |
5.8 |
Roger A Nicoll (Info) |
University of California, San Francisco |
Neurobiology |
2005-08-03 |
|
| 13 |
5.81 |
Rodolfo R. Llinas (Info) |
New York University |
channel physiology, cerebellum, thalamus, cortex, synaptic transmission, MEG, inferior olive, calcium currents |
2005-01-27 |
|
| 14 |
5.87 |
John HR Maunsell (Info) |
Harvard University Medical School |
Vision |
2005-01-15 |
|
| 15 |
5.89 |
Karl H Pribram (Info) |
Stanford University |
Psychology, frontal lobe |
2005-01-16 |
|
| 16 |
5.9 |
John Nicholls (Info) |
SISSA |
Regeneration |
2005-09-08 |
|
| 17 |
5.9 |
Horace Barlow (Info) |
University of Cambridge |
Computation & Theory |
2005-01-15 |
|
| 18 |
5.92 |
Michael M Merzenich (Info) |
University of California, San Francisco |
Auditory system, plasticity |
2005-01-29 |
|
| 19 |
5.92 |
Karl Spencer Lashley (Info) |
Harvard University |
Learning and memory |
2005-01-16 |
|
| 20 |
5.92 |
Ken Nakayama (Info) |
Harvard University |
Vision |
2005-02-18 |
|
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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11063- |
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8850- |
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6638- |
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4425- |
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2213- |
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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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