Cosyne is the largest COmputational and SYstems NEuroscience conference. Many many years ago, I thought it would be a good idea to study the conference. Who goes? Who dominates the conference? If this is the place where people come to exchange ideas, it is useful to know who is doing that and who is dominating the conversation.
The first thing I look at is who is most active (who is an author on the most abstracts) – and this year it is a four-way tie between Larry Abbott, Mehrdad Jazayeri, Jonathan Pillow, and Byron Yu who I dub this year’s Hierarchs 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
- 2020: L. Abbott/M. Jazayeri/J. Pillow/B. Yu
If you look at the most across all of Cosyne’s history, you can see things shift. Looking across time below, you can see that Jonathan Pillow is starting to catch up with Liam Paninski and is breaking away from Larry Abbott. The other startling ascent is Carlos Brody – there’s a whole lot of Princeton going on at Cosyne.
What are in the abstracts? In the past I have tried to find words that are more common in accepted than rejected abstracts. I can visualize this using everyone’s favorite data visualization technique, WOOOORD CLOOOUUUDS. If you wanted to get accepted, it would have been better to write about decision-making trajectories using stable optogenetic attractor choices and worse to write about intrinsic geometry algorithms in tools and datasets.
What is more common in accepted abstracts today than when Cosyne was in Denver two years ago? There are fewer intrinsic dendritic attention pathways and more primate context shape timescales.
At the Cognitive Computational Neuroscience (CCN) conference last fall, Richard Gao presented a super cool poster where he analyzed conference abstracts from different computational neuroscience conferences and used word2vec to make useful embeddings (which is hard! – I have tried this before and failed). I asked him if he could take a crack at this year’s Cosyne abstracts and he was kind enough to agree.
Just looking at the most common topics it looks like recurrent and deep networks are, uh, very popular.
His word2vec embedding representations only need about ~5 PCs to capture most of the variance in the words. COSYNE is low-dimensional 😦
But this is really cool: he used UMAP to look at how similar the embeddings were in different topics. It looks to me like there are classic sensory/processing abstracts in the top left, decision-making in the top right, and models on the bottom? Maybe?
And if he performs hierarchical clustering:
Finally Richard can look at how similar words are in the abstracts. What is most similar to dimensionality-reduction? RNNs.
What are most like MANIFOLDS? Deep networks, population coding, and cerebellum (???).
Who looks at oscillations? People who study pyramidal neurons and hippocampus.
All of this can be found in a notebook at Richard’s GitHub.
Finally, how is everyone connected? I have plotted everyone who is attending Cosyne2020, where connections are between any two people who have co-authored an abstract. Please note that for technical and historical reasons, I find authors by (first initial, lastname). This leads to some ambiguity because sometimes two people share this ID.
Click the picture for a high-res PDF.
There are two many people who have attended Cosyne throughout the years to meaningfully visualize everyone so I have split them into two groups. The Superusers – people who have been an author on 10+ abstracts, and their co-authors who have also been on 10+ abstracts.
I’m just going to pull out a few (colored) clusters here:
And finally, the connected components of everyone at Cosyne2020!