Comment by yummyfajitas | Hacker News Reader

Comment by yummyfajitas | Hacker News Reader

I'll repost my comment from the last version of this article, since I don't think any new developments have invalidated it.

This connection to QFT is almost surely nonsense. The flag manifold in question comes from matrix groups; basically, consider some subset of 3x3 matrices which satisfy a certain property. I.e., certain types of transformations of a 3d space. While the space of 3x3 matrices has 3x3=9 dimensions, subsets can have fewer dimensions (in this case, 6).

The two-dimensional projections she discovered are functions of the 6 dimensional matrices. So here is the occams razor explanation for what is happening. Inside the bee's head is an ODE (ordinary differential equation) solver, basically an analog computer. Just think of the ODE solver as a complicated timer, but with output depending on both time and some hidden variable. These are common objects in biology, and don't require many neurons.

The output of the ODE solver is wired to some neurons which reduce 6 variables to 2, according to the formula she discovered.

This is a fantastic discovery, a triumph of applied math. But the connection to QFT is almost surely coincidence: QFT uses symmetries of matrix groups over 3 dimensions, and the bee (which lives in 3 dimensions) also does. This phenomenon is called universality. Certain objects repeat across diverse areas simply because it is the only logical way (or the most common logical way) that things could happen.

http://news.ycombinator.com/item?id=391396

You've summed up the technical bit of this perfectly, nothing to add there. Would be a very interesting bit of research, but the speculation really drives me mad, as it's so out there and without evidence to back it up that it's not even worth considering.

I have to go further, though - that the researcher is so quick to automatically draw that line connecting two phenomena that both involve group theory as being causally related indicates, to me, that we should be skeptical even about whether the explanation of the bee behavior is correct in any detail. Sometimes when you already know what you really want to "discover", it's very easy to see connections and patterns that don't hold up very well under further scrutiny.

The "this group has something to do with bees, and it's also in QFT, ZOMG bees must read quantum fields!" line of logic is so naive (yummyfajitas explained why) that it really makes me suspicious about the whole bit of research. Scientists should keep sloppy thinking like that to themselves (analogy is a great way to guide your research efforts, and once in a while two appearances of the same group in seemingly unrelated phenomena will, in fact, be connected), and when they don't even realize that this kind of thinking is sloppy, it's not a good sign...

That made me think more than the article. Why anyone would assume that I find it strange that the articles subject would would be so good at math and yet so willing to disregard Occam's razor. It is a good example of how peoples desires shape their thinking.

Consider the complexity of bee brains, http://www.sciencedirect.com/science?_ob=ArticleURL&_udi...

The scientist is quite clear that the QFT connection is largely speculative. However, you've hazarded making the same kind of "nonsense" association that you are critiquing regardless of how much more "plausible" or "simpler" you think it is.

And from someone who studies neurobiology and animal behavior with a specialty in social insects...

This is bullshit. The dance is a solved problem. There are plenty of mysteries about honey bees, and this isn't one of them. A few summers ago I worked with a guy that could read the dance so well that we could physically locate the place the bee was dancing to. (We were studying quorum sensing behavior, which isn't a solved problem; how do bees collaborate to decide which new location to move to?)

Are you a biologist?

  > Inside the bee's head is an ODE (ordinary differential equation) solver
  > basically an analog computer. Just think of the ODE solver as a complicated
  > timer, but with output depending on both time and some hidden variable. 
  > These are common objects in biology, and don't require many neurons.

Do you have any sources to back any of this up at all?

EDIT: Here's a very long list of papers on Honeybee navigation. http://www.sciencemag.org/content/287/5454/851.abstract. From the looks of no one really knows how it works. That includes the OP.

An "analog computer" is what a neuron is, or, if you prefer, an analog computing element.

And yes, neural nets can do some varieties of them, see http://ieeexplore.ieee.org/Xplore/login.jsp?url=http%3A%2F%2... for one example (consult Google for many more, '"ordinary differential equation" neuron' just goes on and on) and while that may be computational neurons, real neurons are not less powerful. This is not such an amazing assertion, it's more the sort of thing that is easy to forget non-specialists in the field (or interested laymen) might require citations for.

Whether artificial neural nets can approximate ODE's and whether neural networks in nature actually do this is not clear. Biology citations please.

EDIT: To the reply. None of those papers state that evolution causes naturally occurring neural networks to become ODE solvers.

I usually hate to argue by authority, but in this case I'll make an exception. Perhaps you are familiar with the work that got Hodgkin and Huxley the 1963 Nobel Prize in Physiology and Medicine?

Ok, I won't just be lazy. Here are the original papers:

http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=139221...

http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=139221...

http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=139220...

http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=139221...

http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=139241...

By the way, the question is not whether ANN's approximate ODEs, but whether ODEs can approximate BNNs (or whatever other processes guide the honeybee dance).

[edit in response to your edit: those papers show that a neuron's behavior is well described as an ODE solver. How it evolved is irrelevant.]

You're badly missing the point. A neuron is not an ODE solver in the sense that you feed it some sort of mathematical specification of the ODE and it sits there and performs algebraic manipulations until there is some solution that pops out in the form of IEEE 754 floating point numbers. Neurons aren't Mathematica. A set of neurons is the ODE solver. If there is a set of ODEs that describes some neural system in terms of the solution to the ODE, then the neural system profoundly is an ODE solver.

You're now asking for a demonstration of something that can't exist because having already demonstrated a reasonable isomorphism between some ODEs and some neural nets, there isn't another layer of some sort of "REALLY REAL isomorphism", that is only a product of human cognition and the failures it has thereof. (I mean that in general, not specific to one person, humans in general are surprisingly good at creating distinctions where there shouldn't be.) A system is what it does. If the activities of some set of neurons can be accurately described as a solution to an ODE, then it is an ODE solver. If the activities of a set of neurons can be accurately modeled by a clock function, then the neurons are a clock corresponding to that function. If the activities of a set of neurons can be accurately described as an edge-detection algorithm, then the neurons are edge detectors. (Both of my other examples here are real too, by the way, I didn't just make them up for didactic purposes.)

I didn't miss that point, and I didn't say neuron. What set of neurons in a bee's brain is the ODE solver? Can we provide our own inputs and experimentally show that that specific set of neurons actually occurring in nature do what is described?

Again, all we have is a theory with no more evidence than the original article. Models that match our observations in the world are our means to reason about the world. But they are not the world, this why our models constantly to change or have to be refined.

In fact the cited papers below are quite clear about that.

This paper is illuminating on the usefulness and limitations of the H-H model: http://www.ncbi.nlm.nih.gov/pubmed/12392930

What set of neurons in a bee's brain is the ODE solver?

I didn't claim to have evidence for my theory. All I claimed is that it was simpler and more plausible than Shipman's.

Look, if you flick a switch on the wall, and a light goes on, the simplest explanation is that some configuration of electrical wires inside the wall caused the production of light from an electrically powered light bulb. You can postulate all sorts of other exotic mechanisms, e.g. levers snapping the glass in a chemical light. But the "occams razor explanation" (which is all I attempted to provide) is electricity in wires.

Same thing with bee behavior - you should postulate some configuration of neurons with behavior modeled by an ODE solver long before you invoke quantum field theory.

No, I'm a (former) mathematical physicist. My main source for that particular paragraph is a math bio textbook I used in grad school (standard yellow book), combined with attendance at a huge number of math bio seminars [1].

However, if you want a source for the general statement that a lot of systems biology works out to ODEs, I'll point you to some general models which form the basis for most work in mathematical neuroscience:

http://en.wikipedia.org/wiki/Biological_neuron_models

[1] If you are in quantitative grad school, I highly recommend you to do the same even if you are studying something completely different (e.g., quantum physics or abstract algebra). The math and biology are usually simple and explained very well, and you wind up learning a lot about both.

Sure. But the point here is that your simply presenting another theory, possibly as far-fetched as the one presented in the original article.

ahahaha. The idea that bee brains contain a structure known to exist already in many other types of brain is equivalent to bees being able to use virtual particles for computation?

That's crazy talk, and you know it.

I AM a biologist, and a specialist on social insects, and sure, it's not understood perfectly. But this guy's statement is valid.

One could just as well say that thrown objects interact with squares, as a square's area and a thrown object's flight path are both modeled by parabolas.

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