So... I'm back in the USofA now, after a long-ish trip to Sweden for the CNS conference. Overall, the meeting was pretty good, and there was some great science presented! On top of that, Stockholm is a gorgeous city, and well worth a visit.
One of the keynote talks at this meeting was by a German physicist-turned-neuroscientist (much like myself), on a very exciting new treatment for Parkinson's Disease.
For those of you who don't know, Parkinson's is a debilitating condition often associated with uncontrolled shaking of the limbs, and difficulty in controlling movement.
They key to treatment is the realization that Parkinson's arises from overly synchronized neural activity in the midbrain, often caused by a lack of dopamine-producing cells. Normally, neurons fire relatively asynchronously (not all at the same time), so that synchrony is a clear atypical situation.
The question is, then, can that synchrony be removed, and if so, will that restore functionality for the Parkinson's patient? Schockingly, the answer is yes!
This, on it's own, is nothing really new. In particular, a technique called deep brain stimulation (DBS) has been around for awhile, and amounts to implanting something akin to a pacemaker in the brain. While that is already a big advance in Parkinson's treatment, it's not really a cure because as soon as one turns off the pacemaker, the symptoms return, and the effectiveness of the pacemaker often decreases over time.
What Tass and his colleagues did, however, is a bit more interesting. They started by modeling the diseased condition as a set of coupled oscillators (a standard physicsy thing to do), wherein the couplings were affected by the neural activity (via STDP, a well-known form of neural plasticity that is though to underly learning and adaptation).
They then realized that, if they could co-activate subsets of these oscillators, the STDP adaptation would, over time, break those connections that were forcing the synchronous activity.
So far, I think it's a fairly neat story, but not an unusual one: a physicist sees some real-world thing and says "ah... I think that's easy to model", and writes down some equations.
However, Tass took this a bit further, and invented a device to perform that neural co-activitation, leading to a technique he calls Coordinated Reset stimulation. He got permission to implant it into some Parkinson's patients, and studied their outcomes.
The results were surprising: after only a short period of treatment, the Parkinson's symptoms were gone, and they did not return when the treatment ended (much unlike the standard DBS pacemaker treatements).
A summary of this talk is available online. I think it's a great reminder to physicists to keep tackling real-world problems, and not to stop once the equations are solved, but rather to keep pushing until the solution is implemented, or it becomes apparent that it is not implementable.
Monday, August 8, 2011
Wednesday, July 13, 2011
White is the color of... LGN?
A lot of computational neuroscientists use something called information theory to try and understand how the parts of the brain communicate with each other. Info theory is a relatively young field, dating back to some work by Claude Shannon in the mid 1900's, and basically formalizes (mathematically) a lot of ideas about how much one could learn from a signal.
The goal of this blog post is to understand a beautiful experimental result published in 1996 by Yang Dan and colleagues. To understand this, we need to first understand how redundancy affects information transfer efficiency.
Let's imagine that you and I are in a conversation, and I choose to repeat every word twice (so it starts as "Hi Hi how how are are you you doing doing today today??"). Now clearly that is not an efficient use of my speech, because we know of a simple way I could have said the same thing in less (1/2 as many) words. One way to formalize that notion is by observing that, the way I spoke, you could predict every 2nd word, once you knew the odd-numbered words, so 1/2 the words are redundant.
What Yang Dan and colleagues showed is that the outputs of LGN neurons have the minimum possible amount of redundancy (like in the case where I only say "Hi how are you doing today?" instead of repeating myself), when presented with naturalistic movies; they showed Casablanca to their subjects.
Now, on it's own, that might seem unimpressive: maybe LGN is just set up so that it always has non-redundant outputs. Well, they did a great control experiment to show that that's not true: they presented their subjects with white noise stimuli (like the static you might see on old-timey televisions when the cable is out), and found that, in that case, LGN outputs were highly redundant! What gives?
Well, it turns out that movies (and images) of real-world stuff (forests, cities, animals, etc.) all have very similar statistical properties. This means that, if you were to make a system for communicating those signals, you could set it up in a way that removes all the redundancies that occur in those movies (like, for example, nearby parts of an image tend to be the same brightness). But, if you took that highly engineered system and applied it to movies with different redundancies, it wouldn't work quite right.
The result of Yang Dan's experiment suggests that, by adapting to the natural environment (possibly over evolutionary time scales), our brains are set up so as to do the most efficient possible job for typical real-world movies!
This remains to me one of the best success stories of systems neuroscience, in which a combination of mathematics (understanding information theory) and experimentation lead us to better understand how it is that our brains work.
The goal of this blog post is to understand a beautiful experimental result published in 1996 by Yang Dan and colleagues. To understand this, we need to first understand how redundancy affects information transfer efficiency.
Let's imagine that you and I are in a conversation, and I choose to repeat every word twice (so it starts as "Hi Hi how how are are you you doing doing today today??"). Now clearly that is not an efficient use of my speech, because we know of a simple way I could have said the same thing in less (1/2 as many) words. One way to formalize that notion is by observing that, the way I spoke, you could predict every 2nd word, once you knew the odd-numbered words, so 1/2 the words are redundant.
What Yang Dan and colleagues showed is that the outputs of LGN neurons have the minimum possible amount of redundancy (like in the case where I only say "Hi how are you doing today?" instead of repeating myself), when presented with naturalistic movies; they showed Casablanca to their subjects.
Now, on it's own, that might seem unimpressive: maybe LGN is just set up so that it always has non-redundant outputs. Well, they did a great control experiment to show that that's not true: they presented their subjects with white noise stimuli (like the static you might see on old-timey televisions when the cable is out), and found that, in that case, LGN outputs were highly redundant! What gives?
Well, it turns out that movies (and images) of real-world stuff (forests, cities, animals, etc.) all have very similar statistical properties. This means that, if you were to make a system for communicating those signals, you could set it up in a way that removes all the redundancies that occur in those movies (like, for example, nearby parts of an image tend to be the same brightness). But, if you took that highly engineered system and applied it to movies with different redundancies, it wouldn't work quite right.
The result of Yang Dan's experiment suggests that, by adapting to the natural environment (possibly over evolutionary time scales), our brains are set up so as to do the most efficient possible job for typical real-world movies!
This remains to me one of the best success stories of systems neuroscience, in which a combination of mathematics (understanding information theory) and experimentation lead us to better understand how it is that our brains work.
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!
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!
Thursday, April 28, 2011
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!
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!
Tuesday, April 19, 2011
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.
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.
Tuesday, April 5, 2011
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.
Wednesday, February 23, 2011
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!
I'm also pretty excited to see what other people are up to, and to spend some time in the mountains!
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