We're all (sort of) blind, even if we can "see"

When your brain processes visual inputs, some information is ignored / discarded. This is pretty well known, and most of us have had experiences where we've failed to notice something that was right in front of us (for example, the "where's Waldo?" books).

As a (slightly weak, but really cool) example, consider the pictures on this website. The first picture shows a man standing in front of a shelf in a supermarket aisle. It's not hard to imagine that, if you didn't know he was there, and looked pretty quickly at the scene, you might miss him.

Now, the question that I want to know the answer to (and that Freeman and Simoncelli have helped answer), is "what information is used, and what information is discarded?".

To help answer this question, they hypothesized a certain set of statistics that might characterize an image. The details of these statistics are technical, and are based on a model of visual cortex.

Then, they took real images, and for each image they computed their statistics, and then generated synthetic images that had all of their statistics correct, but were otherwise as random as possible.

They then had human subjects perform a discrimination task, where they were shown one picture (a real one), then another one shortly after, and were asked whether the two images were the same or not.

What they found was there were certain (pretty severe!) image manipulations for which their subjects couldn't tell the difference between synthetic and real images, thus performing at chance levels (50%) on the discrimination task.

The structure of the un-noticeable manipulations they performed let them infer several properties of the visual system, which agree well with what others have measured by using invasive electrophysiology techniques.

So, next time you look out your window, and think you are seeing all the "stuff" that's out there, think again! You're actually only seeing a (very!) impoverished fraction of the available information.

"bad science" is really good

Frequent readers of this blog may remember an earlier post, in which I discussed the problem of publication bias in medical literature.

Recently, I came accross an excellent blog called bad science that chronicles the issues of communicating statistical results (specifically about medical research) to the broader community, and especially the difficulties that arise when (oftentimes sensationalist) media are involved.

The posts are generally very accessible, and serve to highlight the (growing?) enormity of this issue. Kudos to Goldacre!

Howard Hughes is my Patron (?)

For those who don't know, Howard Hughes was an eccentric american gazillionaire, who founded the Howard Hughes Medical Institute (HHMI), and subsequently bequeathed his substantial fortune to HHMI upon his demise.

HHMI currently funds huge amounts of biological and medical research, predominantly in the US.

Recently, HHMI executives decided to start offered PhD fellowships to foreign graduate students, to support them for the last 2 or 3 years of their doctoral studies. I was lucky enough to be chosen as one of the recipients of this new award.

I am pretty excited about this for a few reasons:

1) I know some of the other students who won (and a few who were turned down) for this award, and they are a pretty talented bunch, so it's an honor to be included in this group

2) I can finish my studies at Berkeley without worrying about how to pay my tuition and salary

3) Unlike most PhD fellowships (NSF, for example), this HHMI grant includes a (modest) budget
for travel to professional meetings. With the current state of Berkeley economics, I probably wouldn't get to go to many neat conferences otherwise.

4) I think this recognition will help me get grants and/or jobs in the future (although I could always be mistaken).

Anyhow, many thanks to Howard Hughes for ponying up the cash to support my studies! If you are interesting, the HHMI press release has more details about the fellowship.

Also, if you are a foreign graduate student, doing a PhD in the US in a biology-related field, definitely consider applying for next year's competition!

Treating Parkinson's with Math

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.