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. Note also that only individuals whose primary affiliation is this tree are included. Nodes cross-listed from other academic trees are included on their primary tree.
Mean inter-node distance|
|Number of nodes|
20 most tightly coupled nodes.
Below are the Neurotree 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!
|1||6.62||Nigel Atkinson (Info)||University of Texas at Austin||2008-04-17|
|2||6.87||Murim Choi (Info)||Seoul National University||Developmental Biology, Craniofacial, Neurobiology, Mouse, Zebrafish||2015-09-28|
|3||8.02||John A. Klingensmith (Info)||Duke University||Developmental Biology, Craniofacial, Neurobiology, Mouse, Zebrafish||2014-10-26|
|4||9.05||Peter Hegemann (Info)||Humboldt-Universität zu Berlin||microbial opsins||2011-06-02|
|5||9.06||Andrew M. Ravanelli (Info)||University of Colorado Anschutz Medical Campus, Denver||Developmental Biology, Cilia, Neurobiology, Oligodendrocyte, Myelin, Zebrafish||2014-10-26|
|6||9.26||Elizabeth A. Carroll (Info)||Duke University||Developmental Biology, Craniofacial, Neurobiology, Mouse, Zebrafish||2015-09-28|
|7||9.61||A. Nazli Basak (Info)||Bogazici University||2016-02-16|
|8||10.45||Terrence J. Sejnowski (Info)||University of California, San Diego||Computation & Theory||2005-01-15|
|9||10.49||Stephen W. Kuffler (Info)||Harvard Medical School||Visual system||2005-01-15|
|10||10.61||Eric R. Kandel (Info)||Columbia University||Learning and Memory||2005-01-26|
|11||10.64||Sir John Carew Eccles (Info)||Australian National University||Synapses||2005-01-16|
|12||10.66||Torsten Wiesel (Info)||Rockefeller University||Visual system||2005-01-15|
|13||10.94||Otto D. Creutzfeldt (Info)||Kraepelin Institute (Munich)||visual system||2005-01-16|
|14||10.95||Michael M. Merzenich (Info)||University of California, San Francisco||Auditory system, plasticity||2005-01-29|
|15||10.96||Peter H. Schiller (Info)||Massachusetts Institute of Technology||Visual system||2005-01-15|
|16||10.98||Roger A. Nicoll (Info)||University of California, San Francisco||Neurobiology||2005-08-03|
|17||10.98||David Hubel (Info)||Harvard University||Vision||2005-01-16|
|18||10.99||Michael P. Stryker (Info)||University of California, San Francisco||Development, Visual system||2005-01-20|
|19||11||Mark Konishi (Info)||California Institute of Technology||Auditory system||2005-01-15|
|20||11.08||David C. Van Essen (Info)||California Institute of Technology||Visual system||2005-01-15|
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 16 or more connections. The actual distribution has a very long tail, with a maximum of 199 connections. Thanks to Adam Snyder for suggesting this analysis!
Edge vs node distribution|
|Number of connections|