#Cosyne18, by the numbers

Where does the time go? Another year, another look at my favorite conference: Cosyne. Cosyne is a Computational and Systems Neuroscience conference, this year held in Denver. I think it’s useful to use it each year to assess where the field is and where it may be heading.

First is who is the most active – and this year it is Ken Harris 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

If you look at the most across all of Cosyne’s history, well nothing ever changes.

Visualizing the network diagram of co-authorships reveals some of the structure in the computational neuroscience community (click for high-resolution PDF):and zooming in:

Plotting the network of the whole history of Cosyne is a mess – there are too many dense connections. Here are three other ways of looking at it. First, only plotting the superusers (people who have 20+ abstracts across Cosyne’s history, click for PDF):

Or alternately, the regulars (10+ abstracts across Cosyne’s history, click for PDF):

And, finally, the regulars + everyone they have collaborated with (click for PDF):

I’d say the long-term structure looks something like the New York Gang (green), the European Crew (purple), the High-Dimensional Deities (blue), the Ecstasy of Entropy (magenta), and some others that I can’t come up with good names for (comments welcome).

Memming asked whether the central cluster was getting more dispersed or less cliquey with time. This is kind of a hard question to answer. If you just look at how large the central connected group is over time the answer is a resounding no. The community is more cohesive and is more connected than ever before.

On the other hand, we can look within that central cluster. How tightly connected is it? If you look at mean path length – how long it takes to get from one author to another, like degrees of Kevin Bacon or an Erdos number (a Paninski number?) – then the largest cluster is becoming more dispersed. Dan Marinazzo suggested looking at the network efficiency as a metric that is more robust to size. Network efficiency is kind of the inverse of path length, where one would mean you can get from one author to another in a single step and 0 means it takes forever.

I now also have two years of segmented abstracts (both accepted and rejected). What are the most popular topics at Cosyne? I used doc2vec, a method that can take a document and embed it in a high-dimensional space that represents the semantic topics that are being used, and then visualized it with t-SNE. The Cosyne Island that you see above is the density of abstracts at each given point. I’ve given the different islands names that represent the abstracts in each of them.

If you look at the words that you see more in 2018’s accepted abstracts they are “movements”, “uncertainty”, “motion”; looks like behavior!

The rejected abstracts are “orientation”, “techniques”, “highdimensional”,”retinal”, “spontaneous” 😦

We can also look at words that are more likely to be accepted in 2018 than 2017 (which are the big gainers):

And the big losers this year versus last year:

Here is a list of the twitter accounts that will be at Cosyne.

Previous years: [201420152016, 2017]

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Communication by virus

‘Some half-baked conceptual thoughts about neuroscience’ alert

In the book Snow Crash, Neil Stephenson explores a future world that is being infected by a kind of language virus. Words and ideas have power beyond their basic physical form: they have the ability to cause people to do things. They can infect you, like a song that you just can’t get out of your head. They can make you transmit them to other people. And the book supposes a language so primal and powerful it can completely and totally take you over.

Obviously that is just fiction. But communication in the biological world is complicated! It is not only about transmitting information but also about convincing them of something. Humans communicate by language and by gesture. Animals sing and hiss and hoot. Bacteria communicate by sending signaling molecules to each other. Often these signals are not just to let someone know something but also to persuade them to do something else. Buy my book, a person says; stay away from me, I’m dangerous, the rattlesnake says; come over here and help me scoop up some nutrients, a bacteria signals.

And each of these organisms are made up of smaller things also communicating with each other. Animals have brains made up of neurons and glia and other meat, and these cells talk to each other. Neurons send chemicals across synapses to signal that they have gotten some information, processed it, and just so you know here is what it computed. The signals it sends aren’t always simple. They can be exciting to another neuron or inhibiting, a kind of integrating set of pluses and minuses for the other neuron to work on. But they can also be peptides and hormones that, in the right set of other neurons, will set new machinery to work, machinery that fundamentally changes how the neuron computes. In all of these scenarios, the neuron that receives the signal has some sort of receiving protein – a receptor – that is specially designed to detect those signaling molecules.

This being biology, it turns out that the story is even more complicated than we thought. Neurons are cells and just like every other cell it has internal machinery that uses mRNAs to provide instructions for building the protein machinery needed to operate. If you need more of one thing, the neuron will synthesize more of the mRNA and transcribe it into new proteins. Roughly, the more mRNA you have the more of that protein – tiny little machines that live inside the cell – you will produce.

This synthesis and transcription is behind much of how neurons learn. The saying goes that the neurons that fire together wire together, so that when they respond to things at the same time (such as being in one location at the same time you feel sad) they will tend to strengthen the link between them to create memories. And the physical manifestation of this is transcribing proteins for a specific receptor (say) so that now the same signal will activate more receptors and result in a stronger link.

And that was pretty much the story so far. But it turns out that there is a new wrinkle to this story: neurons can directly ship mRNAs into each other in a virus-like fashion, avoiding the need for receptors altogether. There is a gene called Arc which is involved in many different pieces of the plasticity puzzle. Looking at the sequence of the gene, it turns out that there is a portion of the code that creates a virus-like structure that can encapsulate RNAs and bury through other cells’ walls. This RNA is then released into the other cell. And this mechanism works. This Arc-mediated signaling actually causes strengthening of synapses.

Who would have believed this? That the building blocks for little machines are being sent directly into another cell? If classic synaptic transmission is kind of like two cells talking, this is like just stuffing someone else’s face with food or drugs. This isn’t in the standard repertoire of how we think about communication; this is more like an intentional mind-virus.

There is this story in science about how the egg was traditionally perceived to be a passive receiver during fertilization. In reality, eggs are able to actively choose which sperm they accept – they have a choice!

The standard way to think about neurons is somewhat passive. Yes, they can excite or inhibit the neurons they communicate with but, at the end of the day, they are passively relaying whatever information they contain. This is true not only in biological neurons but also in artificial neural networks. The neuron at the other end of the system is free to do whatever it wants with that information. Perhaps a reconceptualization is in order. Are neurons more active at persuasion than we had thought before? Not just a selfish gene but selfish information from selfish neurons? Each neuron, less interested in maintaining its own information than in maintaining – directly or homeostatically – properties of the whole network? Neurons do not simply passively transmit information: they attempt to actively guide it.