#Cosyne19, by the numbers

As some of you might know, there’s been a lot of tumult surrounding this year’s Cosyne (Computational and Systems Neuroscience) conference. The number of submissions skyrocketed from the year before and the rejection rate went from something like 40% to something like 60% – there were over 1000 abstracts submitted! Even crazier, there is a waitlist to even register for the conference. So what has changed?

Lisbon, Lisbon, Lisbon. This is the first year that the conference has been in Europe and a trip to Portugal in the middle of winter is pretty appealing. On the other hand, maybe Cosyne is going the way of NeurIPS and becoming data science central? Let’s see what’s been going on.

You can see from the above that the list of most active PIs at Cosyne should look pretty recognizable.

First is who is the most active – and this year it is Jonathan Pillow who I dub this year’s Hierarch of Cosyne. The most active in previous years are:

  • 2004: L. Abbott/M. Meister
  • 2005: A. Zador
  • 2006: P. Dayan
  • 2007: L. Paninski
  • 2008: L. Paninski
  • 2009: J. Victor
  • 2010: A. Zador
  • 2011: L. Paninski
  • 2012: E. Simoncelli
  • 2013: J. Pillow/L. Abbott/L. Paninski
  • 2014: W. Gerstner
  • 2015: C. Brody
  • 2016: X. Wang
  • 2017: J. Pillow
  • 2018: K. Harris
  • 2019: J. Pillow

If you look at the most across all of Cosyne’s history, you can see things shift and, remarkably, someone is within striking distance of taking over Liam Paninski’s top spot (I full expect him to somehow submit 1000 posters next year despite there being a rule specifically designed to limit how much he can submit!).

It is interesting to look at the dynamics through time – I have plotted the cumulative posters by year below and labeled a few people. It looks like you can see when the Paninski rule was implemented (2008 or 2009) and when certain people became PIs (Surya Ganguli became suspiciously more productive in 2012).

Adam Charles suggested that we should looked at viral – if a person’s ideas were a disease, who would spread their ideas (diseases) most effectively? Working from a measure defined here, he calculated the most viral people at Cosyne19:

And also if you normalize for the number of nodes the people are directly connected to:

And similarly for Cosyne 2004 – 2019:

In other words, are you viral because you are linked to a lot of people who are in turn are linked to a lot of people (top figure)? Or are you viral because you are connected to a broad collection of semi-viral co-authors (bottom figure)?

It’s been remarked that the lists above are pretty male-heavy. I thought that maybe the non-PIs would be more diverse? So I plotted the number of posters from 2013 – 2019 (mislabeled below) where I have author ordering: how many posters does each person have that are non last or second-to-last authors, given that are not on the PI list above? The list below is, uh, not any better at representation.

 

What is it that got accepted in Cosyne19? These are the most common words in the abstracts:

These are the words that are more popular in 2019 than in the 2018 abstracts:

Conversely, these are the words that are less popular this year than previous years. Sorry, dopamine.

I had thought that maybe the increased popularity at Cosyne was because of an increase in participation from NeurIPS refugees. If so, it doesn’t show up in the list of words above. I tried various forms of topic modeling to try to parse out the abstracts. I’ve never found a way of clustering the abstracts that I find satisfying – the labels I get out never correspond to my intuition for how the subfields should be partitioned – but here is an embedding using doc2vec of all the abstracts from 2017 – 2019:

And here is an embedding in the same space but only for 2019 abstracts. Not so different!

And if we look at the number of abstracts that contain word relating to different model organisms – or just “modeling”, “models”, “simulations”, etc, we see it’s stayed pretty much the same year-to-year.

Maybe it is a different group of people who are at the conference? Visualizing the network diagram of co-authorships reveals some of the structure in the computational neuroscience community (click image for zoomable PDF):

Some highlights from this:

IDK WTF is going on at the Allen Institute but I like it:

Geography is pretty meaningful. The Northeast is more clustered than you would expect from chance:

As are the Palo Alto Pals

Here is a clustering of everyone who has been to Cosyne since 2004 and has at least five co-authors. It’s a mess! (click image for zoomable PDF)

Okay this grouping looks pretty similar. Are they the same people? If I look at the proportion of last authors on each abstract who have never been to Cosyne before, it looks like the normal level of inflow – no big new groups of people.

But the number of authors on each abstract has grown pretty heavily:

One thing that is changing is the proportion of authors who belong to the largest subgraphs of the network – that is, who is connected to the “in-group” of Cosyne. And the in-group is larger than ever before:

It’s a bit harder to see here – partly because there are two large subgraphs this year instead of one big glob – but mean path length (how long it takes to get from one author to another) and the network efficiency (a similar metric that is more robust to size) all indicate a more dispersed set of central clusters. I’m not quite sure why, but it is possible that the central group is replicating itself. You are getting the same people still weakly connected to former PIs/collaborators opening their own labs, getting a little further away but not too far…

All in all, it looks like there was an increase in submissions – probably because of the European/Lisbonian location – but no real change in the submissions that were accepted.

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