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April 11, 2014


Apologies for all these images that I’m posting, I promise I’m not turning this into my tumblr. I’m just a tad bit busy finishing my thesis with little time to write cogently about science. So enjoy this example of your visual system screwing with you.

Have you tried optogenetics?

April 9, 2014


It has become pretty common to see someone, after a neuroscience talk, ask, “Interesting – but have you tried doing this with optogenetics?” As if this technique to precisely activate neurons was something that was trivial to implement. It’s not, obviously, it’s quite expensive and hard (for many systems). Otherwise everyone would be doing it already! Who wouldn’t want to turn neurons on and off at will?

Florian Engert, who imaged all the neurons in the zebrafish brain, used this picture at the end of his talk at CSHL Synapses last year when asking for suggestions on experiments.

[via Cian O'Donnell]

‘Primitive’ cardiovascular systems

April 8, 2014

A 520-million year old cardiovascular system:

It was both modern and unsophisticated. A simple, tubelike heart was buried in the creature’s belly—or thorax—and shot single blood vessels into the 20 or so segments of its primitive body. In contrast, x-ray scans of the specimen revealed profoundly intricate channels in the head and neck. The brain was well supplied with looping blood vessels, which extended branches into the arthropod’s alienlike eyestalks and antennae and rivaled the complexity of today’s crustaceans.

The study of cardiovasculature is actually extremely important to understanding neuroscience. There is a complicated apparatus in the brain designed to interact with the vasculature in order to get ATP (energy), etc. Though a commenter (Ginny Freeman) notes:

The “heart” was NOT buried in the “creature’s belly”. It’s a crustacean, for heaven’s sake! Crustaceans like all arthropods have a DORSAL circulatory system.

Here is the original paper.

Information theory of behavior

April 8, 2014

Biology can tell us what but theory tells us why. There is a new issue of Current Opinion in Neurobiology that focuses on the theory and computation in neuroscience. There’s tons of great stuff there, from learning and memory to the meaning of a spike to the structure of circuitry. I have an article in this issue and even made the cover illustration! It’s that tiny picture to the left; for some reason I can’t find a larger version but oh well…

Our article is “Information theory of adaptation in neurons, behavior, and mood“. Here’s how it starts:

Recently Stephen Hawking cautioned against efforts to contact aliens [1], such as by beaming songs into space, saying: “We only have to look at ourselves to see how intelligent life might develop into something we wouldn’t want to meet.” Although one might wonder why we should ascribe the characteristics of human behavior to aliens, it is plausible that the rules of behavior are not arbitrary but might be general enough to not depend on the underlying biological substrate. Specifically, recent theories posit that the rules of behavior should follow the same fundamental principle of acquiring information about the state of environment in order to make the best decisions based on partial data

Bam! Aliens. Anyway, it is an opinion piece where we try to push the idea that behavior can be seen as an information-maximization strategy. Many people have quite successfully pushed the idea that sensory neurons are trying to maximize their information about the environment so that they can represent it as well as possible. We suggest that maybe it makes sense to extend that up the hierarchy of biology. After all, people generally hate uncertainty, a low information environment, because it is hard to predict what is going to happen next.

Here is an unblocked copy of the article for those who don’t have access.


Sharpee, T., Calhoun, A., & Chalasani, S. (2014). Information theory of adaptation in neurons, behavior, and mood Current Opinion in Neurobiology, 25, 47-53 DOI: 10.1016/j.conb.2013.11.007

Keeping up with the scientific Joneses

April 4, 2014

It’s really hard to find relevant articles in the morass of papers that are out there. xcorr has an excellent post up detailing recommendations on how to keep up with the scientific literature:

To do good research, you have to be well-informed of the latest scientific developments both in your narrow field of study and in science at large. I recommend the following workflow to make this as painless as possible:

  • Use feedly to keep up-to-date with blogs, journals
  • Use PubChase to get personalized paper recommendations
  • Use Zotero to organize papers you read
  • Use PaperShip to read and comment on papers on your iPad

Here’s a more detailed exposition, along with further resources and alternatives, to help you keep up the scientific literature.

I currently just use feedly, which means every wednesday/thursday I am flooded with articles (I think I subscribe to ~20 different journal feeds?), and every day brings new and relatively useless articles from a few static pubmed rss feeds. It sounds like I need to get started using PubChase!

How should you judge a theoretical model?

April 4, 2014

When faced with a model of the world (in physics, neuroscience, economics, ecology), how should you judge that theory? Cyrus Samii suggests 5 ways. Here is number 2:

2. If any result can be engineered then results themselves have no special ontological status.

This is another way of asking whether a model has empirical content, which we typically take as falsifiability. Yet Karl Popper suggested:

The empirical content of a statement increases with its degree of falsifiability: the more a statement forbids, the more it says about the world of experience.

And he suggested “two criteria determine the empirical content of a theory are their level of universality (Allgemeinheit) and their degree of precision (Bestimmtheit).”

I also really like the question at the start of number 4:

How complicated can the problems be that we allow our agents to solve in a model? Is a dynamic program ever admissible as a reasonable assumption on the objective function of an agent?

Charles Krebs (or Judy Myers) says:

Recommendation – no paper on models should be published or talked about unless it makes specific, testable predictions of how the model can be tested.

I actually disagree with this rather strenuously. There are several reasons to make models, only one of which is to make predictions. Another is to confirm hypotheses.

Let’s say that you think that honeybees are dying because of the excessive use of mint toothpaste and you collect data to prove it. The problem is that data is simply a collection of facts (or “facts”) with no organizing structure. A model can give those facts that structure: you put what you know together with some of the data, and see if what you know is sufficient to replicate the observations of the world. Of course, you have to interpret these types of models carefully; they are not predictive models in the sense that they tell you anything about the world. Rather, they tell you about whether you have a consistent and complete story. But it’s still just a story.

Cold Spring Harbor

April 3, 2014

Just a note that I am currently at Cold Spring Harbor Labs for the Circuits conference, where I am speaking on Saturday morning. Someone already surprised me by saying, “I think I’ve seen your blog” which always make me a bit self-conscious. Real people might hold me accountable for the stupid things I say on here… But if any of my other readers are here, try to stop me and say hi!


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