Where have I been?

So... not much blogging has happened in awhile, and that's a bit uncool on my part.

However, since late April, I have been on a tear, spending 5 days in SoCal (Death Valley: I guess it's really southeast Cal), 5 days in DC, 10 days in Canada, 3 days cruising around SF bay on my boat, and 3 days in Sequoia National park. Add in trying to get some research done, and not much blogging has happened. But, for those aspiring grad students out there, let this be informative: being in Grad school and having fun traveling are totally not mutually exclusive!

Canada STEM Award for Americans

This post is mainly intended for undergrads who are thinking about going to grad school.

I did my undergrad degree in Canada, and was subsequently very fortunate to receive one of the US Fulbright science and tech PhD fellowships to attend UC Berkeley. These are great fellowships, and if you are a non-american, and interested in coming to the US for PhD studies, I strongly encourage you to look into that program.

Recently, I became aware of a new program which is basically the inverse of the one I am currently a part of. This is a program run by Fulbright Canada to bring top US students to Canada's best universities to pursue PhD studies. The benefits are many, so I would encourage any potential PhD students to investigate more fully.

Even if you've never considered studying in Canada, I urge you to think about it. From my experiences in materials science, nuclear physics, astrophysics, and particle physics, the research facilities in Canada are top-notch, and Canada has some of the world's most liveable cities. Fortunately, our best universities also tend to be in our nicest cities!

Best of luck!

Uncertainty and decision making

So.... here is a post about my first biology publication: "how should prey animals respond to uncertain threats?".

I'll summarize very briefly some ideas about gambling, and the Kelly criterion, and then discuss what that has to do with prey animals.

Let's start our discussion by imagining that you and I are going to gamble on coin flips. We will flip a coin, and bet at even odds (so if it's heads, I pay you the amount of the bet, and it it's tails, you pay me that same amount). But, the coin is biased in your favor, so that it comes up heads 55% of the time, and tails 45% of the time. This means that you have a 10% edge on the bet: on average, you expect to get back 110% of the bet, for each bet you make.

If we only do one coin flip, and you want to maximize your expected profit, you would bet everything you have. You might lose, but your expected profit is positive.

Instead, let's consider the case where we keep flipping the coin over and over again, and you try to maximize your long-term profit. In that case, it would be silly to bet all of your money on the first coin flip because, if you lost that one, you would lose the ability to make money on future bets (because you would be broke and not able to keep betting).  Back in the 1950's, J Kelly demonstrated that the best possible strategy in this case is to bet 10% of your money on each coin flip. As your bankroll grows, you bet more. This strategy provides the best balance between betting big (since you expect to make money on each bet, and bigger bets mean more profit), and avoiding going bankrupt (which gets rid of any chance of future profit).

In my paper, I discuss a semi-related problem, which is as follows.

Imagine that you are a deer, in a forest. You spot movement out of the corner of your eye, but you don't know for sure what is causing it. If it's a wolf (or whatever predator), you should run away to avoid being killed, but if it's not a predator (say, just some leaves blowing in the wind), then running away would waste energy, and cost you whatever mating or foraging opportunities were presently available to you.

Now we want to figure out what the deer (you) should do in that situation.

Interestingly, much like in our gambling example, the "correct" decision (the one that would be favored by evolution; the one that allows the deer to have the most offspring in its lifetime) is very heavily influenced by the uncertainty of the outcome. So, even if it might be immediately (on average) advantageous to "risk it", and not flee, when you are uncertain about whether or not a predator is present, the fact that you lose all future mating chances if you are wrong makes the "correct" decision strategy more cautious.

This issue - the influence of uncertainty on prey escape decisions - was not previously understood in the behavioral ecology models, but I am hopeful that future work in this field will be influenced by my result.

criticality

After a long hiatus, I am back to blogging.

Yesterday's physics colloquium was given by Bill Bialek, physicist and theoretical biologist at Princeton (and the PhD thesis advisor of my PhD thesis advisor). His talk was based on a recent paper titled "Are biological systems poised at criticality?". In the context of neuroscience, Bialek's basic observation is that, yes, neural systems appear to have this special "critical" property.

In particular, the observed correlations between the activities of two neurons are such that, if they were any stronger, the brain would be epileptic (recall that, in epileptics, the activities of neurons are amplified such that you get huge cascades of activity, resulting in seizures), but if those correlations were any weaker, the brain would effectively be "dead" (there would be no significant collective behavior).

Now, Bialek's work also discusses criticality in protein sequences, and collective animal behavior, but my interest is mainly in the brain.

Now, from a purely functional standpoint, this "criticality" seems to be sensible, and I could imagine it arising as a product of evolution; animals with more strongly correlated neurons would be epileptic, and they would die off, but so would those with less strongly correlated neurons, as they might be unable to effectively process information.

However, the brain is not static over the lifetime of the animal. We learn and adapt, and as we do, the correlations between neurons in our brains change.

How, then, is this criticality maintained? In other words, is there some kind of homeostatic mechanism that adjusts the correlations (or synaptic connection strengths that, presumably, alter these correlations), to keep them at this critical point?

These are, admittedly, ill-formed ideas at present, but I may very well get back to them when I have a chance.

In other news, my first biology paper was just accepted for publication in "frontiers in computational neuroscience." I will post a link to the paper when it is all copy edited and ready for public consumption.

Utah!

Off to Salt Lake City tomorrow for the annual computational and systems neuroscience (cosyne) meeting. I'm giving a talk on Friday, with an expected audience of many hundreds of people. Definitely a great opportunity to spread the word about my latest results, but clearly also a nerve-wracking experience.

I'm also pretty excited to see what other people are up to, and to spend some time in the mountains!

networking for dummies (and scientists)

If you're anything like me, you've been told your whole life that it's crucial that you "network". No one really knows what this means, but it is apparently critical to future job getting. Today's post is about what I think about networking for scientists.

The basic idea is that, if people know you, like you, and respect your abilities, they will want to work with you. So, when they have job openings, they may remember you and call on you.

Case in point, I was recently sailing with an old friend who works for (insert local tech firm's name here), and they are looking for staff. He mentioned that he might be able to set something up if I am interested. Now, I'm staying in grad school 'till I'm done this PhD thing, but clearly having potential job opportunities is great, and is (in some sense) the "goal" of what people mean when they say "networking".

So, how does this good thing (possible job offering) arise, and how do you get there?

The canonical advice is "meet people who can do things for you, and make them remember you". That's why science conferences (and other places, I'm sure) are full of eager young go-getters foisting their business cards on anyone who will take one. I posit that this is an ineffective strategy, because those interactions lack meaning.

My advice is instead to do fun things, and to make friends who have similar hobbies (ideally who work in a diverse set of businesses). That way, you make meaningful connections with people, based on something real (as opposed to the fake friendliness that arises when you want something from them). Forget about networking! Go have fun!

Much later, when you are looking for work, feel free to call up people you know, especially those with connections in the industry in which you want to work.

Now, about conferences: obviously, science conferences are great places to meet smart people who share your interests (and may be able to offer you jobs). Clearly, they have value in this whole "networking" world. Thus, you should indeed go to conferences eager to share your work with others, and to learn what they are working on.

But, instead of trying to play every angle to give out your business card, I suggest you focus instead on learning, having fun, and meeting people for the sake of making friends. Once you have friends, the networking game is basically solved.

Hopefully, a lot of this is obvious. But, I have been given a lot of advice in the past that is quite contrary to what I have written here, so I think it's worth putting on (virtual) paper.

Best of luck!

why your brain loves raves (even if you don't take ecstasy)

Does anyone remember this ridiculous thing called a "rave"?

Waaaayyyy back in the nether years of my youth, these parties were semi-popular excuses to take copious amounts of MDMA (ecstasy) and listen to really bad techno music.

Aside from the love of MDMA, why were these things so popular? As a more general question, why do people enjoy music, and music with lots of heavy bass in particular? Tony Bell turned me on to one interesting idea a few months back, that I will explain to you now.

Your brain is a big mass of nerves cells called neurons that emit pulses of electrical activity to communication with each other. At a close glance, this activity appears chaotic, but there is some underlying structure to it; there are waves of activity called neural oscillations (brain waves) that travel across your brain.  There are different kinds (frequencies) of brain waves: alpha waves oscillate 8-12 times per second, delta waves oscillate 1-4 times per second, and so on.

Lots of research has shown that these waves help to synchronize the activity of neurons, enhancing cognitive processes like memory and attention.

What, you might ask, do neural oscaillations have to do with raves?

Well, it has been demonstrated that your brain waves synchronize (lock in to) external stimuli, like music, when that stimulus has the right frequency. Listen closely to the bass component (like the bass drum) of your favorite bass-heavy song, and you should notice that it has a few beats per second. In other words, it occurs at precisely the frequency of delta waves in your brain.

For example, if you google "rave music", you find this youtube clip. Try listening to the bass line and counting how many beats you get in a 5-second window. It's probably around 10-14, depending on where you are in the song. In other words, right in the delta range, that would allow your attention to lock on to it.

If you pay close attention, you'll find beats that fall neatly into the alpha range as well (8-12 beats per second).

So.. what's my point? Well, maybe these brain oscillations are one of the reasons why bass can have such a strong effect on people.

I hope this provides some food for thought when you're at burning man this fall, or your own favorite music scene.