Yet another argument on creation(ism)

All of this was prompted by an article in the Guardian on creation that I found particularly well written in a sea of poor discourse on the subject.

I am convinced that nearly everything meaningful to be said about the argument of creationism, intelligent design (ID), and evolution has been said. My position from a scientist and Christian’s point of view is straightforward. Obviously, there is a lot of evidence that exists regarding evolution. With respect to origins of the universe, there is also a surprisingly consistent amount of evidence for the big bang theory. So, my current belief, based on the evidence available, is that the big bang happened, and separately science currently paints a reasonable picture of the origin of life.

Now there’s something very intuitive about the idea that something had to precede the big bang, and that’s fundamentally what I’ll call God. (Call it what you like.) So, the event of creation, in my mind, occurred, but it’s not God placing tiny Lego humans, in their modern forms (whatever that is), on a pre-formed Earth. Creation was the release of energy from an infinitesimally small point into the expanding universe.

So that explains my theism. I am particularly Christian because I believe the fundamental story of Christ, based on all available evidence (Biblical and extra-Biblical accounts). My understanding of the canonization of the New Testament makes me believe that the Bible is more of a human document than it is handed down from God. While I’m much more inclined to take the Hebrew Bible on faith, it remains a question of faith and not science, though I’m pleased to see scientific and archaeological inquiries into the subject. Yet many of these fundamental beliefs I take on faith, and many of these questions cannot be addressed by science directly. It’s perhaps in my nature, however, to attempt to reconcile these two logical worlds in order to ensure that some sort of weird singularity doesn’t implode my head.

The theory of evolution, on the other hand, asks questions that are within the realm of science and testable science. We as scientists would be remiss if we did not admit that explicit statements about the past may well not be testable, but our observations are still meaningful, like a forensic puzzle, and we have the unique opportunity of having systems we can closely monitor in labs and in real environments to check consistency with current ideas.

For one broad example, biological conservation is striking. The preservation of even single, complex ion channels is maintained throughout species whose brains are vastly different. Just one of many, many examples of this is the human 6 transmembrane domain K⁺ channel herg, which is 70% similar in genetic sequence to a channel in the worm C. elegans and also similar to channels in the Drosophila fly and elk.

Modern evolutionary theory accounts for this. At least one alternative explanation to evolution that one might hear from the ID folks is that God could have placed these sequences in each species when God created them. This is not a testable hypothesis, and it is not scientific by definition. As far as science is concerned, until this is reconciled, end of discussion with respect to science!

With respect to education policy, and I probably have more to say at a later time on this issue, all of this sums up to the following. Evolution is a theory (like ALL other theories in science). It is not proven (like all other ideas in science). Evolution and big bang theories should be presented as a theory in science curricula. ID and creationism, on the other hand, have zero place, whatsoever, in a science curriculum. What could be explained is why this is, since it is such a curiously heated topic. As far as this scientist and Christian is concerned, it’s pretty binary.

Automatically generated YouTube subtitles

It just dawned on me that one of many compelling reasons for Google to work on its voice recognition (and search by voice) is to automate subtitles for YouTube. Thanks to my brother for sending me this ridiculous Dutch video that transcends any language comprehension.

The linear approximation of SfN

I recently came back from the Society for Neuroscience (SfN) mega-conference that draws over 30 kNeuroscientists each year. That’s right, 3×10⁴. Compare this, of course, to the annual conference for the American Association for the Advancement of Science, which draws about 10.000, or 1/3 of the number. Yet neuroscience is a subset of science, so, something funny must be happening. Is there a conference larger than SfN? I’d do well to avoid it, I think.

I looked at SfN this year from the limited but functional perspective of a computational neuroscientist. For the experimental posters and talks I attended, I tried to think of ways in which they could be modeled. On my mind notably is modeling of calcium wave propagation dynamics, which is an active area of imaging and may benefit from computational approaches. Surely there is work being done on this currently.

For the computational presentations, I tried to learn about various techniques that I have not yet been exposed to. There was a lot of basic Hodgkin-Huxley type modeling, along with a bunch of compartmental modeling. But what caught my eye was the use of stochastic processes in creating a framework for studying spiking neurons. At its best it may provide a mathematically rigorous description for certain experimentally observable data.

The most fruitful conversation I had was with a fellow grad student who had access to some very unique electrocorticogram (ECoG) data in auditory cortex of humans and had done some preliminary frequency analyses during natural, meaningful sounds (such as a spoken sentence). There is a songbird analog to this idea using natural, complex sounds that constitutes a very active area of research for auditory physiologists and others.

One interesting (to me) observation about the conference was the inelastic collision between vendors, industry, funding sources, institutions, and scientists. A lot of toys (swag) were doled out. I asked an NIH person about computational neuroscience support, and while they financially support efforts of collaborative science that include computation (a very reasonable approach, especially considering the NIH mission), they do not have but a handful of computational neuroscientists staffed at NIH.

One guy whose job was to connect industries with researchers said, when asked about the perceived value of computational neuroscientists in industry, “None yet. But keep asking.” I don’t know how indicative of greater industry he represents, but this means to me that the value of computational approaches may not have quite received full understanding in that community.

There seems to be a lot of convincing to be done.

The conference was not overwhelming as cautioned because I think I put enough (perhaps too many?) constraints on my activities. If I tried to do too much, I felt that I would have learned nothing. But focusing on learning a few things I know about and a few things I’m interested in learning pertaining to my own work, and the conference was very useful.

The immediate future of multitouch

Multitouch technology is most prominent on the Apple iPhone and iPod touch, as well as their new laptops. With respect to each new series of laptops, they introduce a number of new gestures that perform various functions. They’re a big hit with consumers, but the real innovation with multitouch will be arbitrary gestures, which will allow users to program any arbitrary gesture to any arbitrary command.

This could currently be limited by some kind of touchpad firmware issue, since I don’t know how it works, but I presume that there is at least some capacity for software interfacing allowed.

Securing VNC over ssh in OS X

I begrudgingly have to revisit the idea of Virtual Network Computing (VNC), which is a way of viewing and controlling a secondary computer over the Internet. It’s a clumsy replacement for X11 window forwarding in many respects, but it’s in a respect more functional and allows you to have complete control of a remote computer. Additionally, it enables you to continue right where you left off in work without having to think about where windows “live.”

The trouble with VNC is that it’s pretty insecure (opening up another port on the server machine), requires a lot of bandwidth (piping graphics and input back and forth), and is generally unencrypted (packets can be picked up or “sniffed” by an intruder). VNC over ssh is a way of addressing both security implications.

There’s a phenomenal article on this topic at Fotinakis.com.

Changing file handlers in OS X Leopard

If you’ve ever used the “Open With … ” contextual menu item in Finder in OS X, you may have seen several duplicates of programs that are listed there. It’s confusing and cluttered. There is a buried application to reset this particular list called lsregister. Open up a Terminal window and copy and paste the following:

/System/Library/Frameworks/CoreServices.framework/Versions/A/ \
Frameworks/LaunchServices.framework/Versions/A/Support/lsregister -kill \
-r -domain local -domain system -domain user

Now your Open With list should be rebuilt with just the current apps in /Applications and possibly ~/Applications. This tip was originally found at Apple Discussions, but the location of lsregister has changed since then.

Often times when migrating systems or generally screwing around in OS X, you may find that, among other things, OS X gets confused by what applications you want to use by default.

Geeky solutions to silly problems belong to the world. PS. If this isn’t in TinkerTool or Onyx, it ought to be!

Possible preventative role of statins

The role of statins in lowering cholesterol (and thus preventing certain types of heart disease) has been shown, but a recent report in the New England Journal of Medicine underlined the potential use of statins as a preventative tool for heart health in older men and women. Reports are all over the web, from the New York Times to BBC Science, but the original article is available at the NEJM site. Some excellent commentary addressing the ever-unclear issue of long-term effects can be found at the WSJ Health Blog.

Information belongs to the world. Right?

Imagine a world in which data was available to researchers all around the globe. A lab in Germany could take neural recordings of patients and make them available to anyone willing to devote the time and skills to doing various analyses. Additionally, where n equaled 1 in that limited trial, perhaps an aggregate of the world’s data would make sample sizes large enough to find statistically significant meaning. Additional lines of inquiry could be made about data, perhaps far beyond the initial intent. For instance, a neuroimaging friend of mine was telling me how they used to observe cerebellar BOLD activation during certain working memory tasks and dismissed it as some kind of motion artifact. But in retrospect, with new evidence concerning the role of the cerebellum in higher order processes, this lost data could have been very valuable. It appears that researchers all over the country are sitting on data that they haven’t the time to fully sift through. In fact, it seems also that the rate of data collection in the digital age is far outpacing even the growth of researchers in the field, as it becomes increasingly easier to collect and store copious amounts of data. We’re becoming digital packrats, and it’s easy to hope that the secrets of the brain are waiting to be revealed on hard drives in labs across the globe.

Several aspects of this kind of data availability are appealing, as the pace of innovation could be seen to accelerate as more data are available to more analysis and aggregation. We’d also be one step closer to scientific transparency, as data availability, along with detailed methods, would enable anyone to go through and at least replicate the analysis portion of one’s work. However, there are a number of scientific, as well as pragmatic, issues that must be considered before such a project could be ultimately useful.

Currently, that level of transparency is not practiced for a number of reasons that are as tragic as they are understandable. Mistakes are bound to happen, but the price of admitting fault is far too high and unfortunately discourages total transparency. This is, in some sense, self correcting, since it requires that researchers are very careful in the way they carry out experiments and present results. However, the scientific community thus far has seemed to be content with allowing a certain level of skullduggery parading as brevity, and this means that certain methods continue to be willfully obtuse.

The nature of competition and funding also means that, though methods are presented in limited forms in papers, incomplete disclosure is appealing, since it can ensure a long run of guaranteed, exclusive publications. Sometimes methods can be financially lucrative and made available in limited ways to other researchers, but as in all other businesses, the secret is being a good middleman between one’s method and those who need to use it. It completely is contrary to the spirit of scientific openness.

One issue with respect to modeling work is the release of source code. Open source modeling represents a level of transparency that is ideologically appealing. At some point in my career, I plan on being an open source scientist. A few efforts to this end are available currently, including the primary database called ModelDB, a collection of neural models and their associated papers. However, to meet this responsibility properly, a lot of work must be invested in making code accessible and compatible for it to be truly useful. Just throwing it into a database is not, inherently, helpful or meaningful.

Releasing source code, detailed methods, or data opens a lab up to criticism that may find crucial flaws. For quality control purposes, data and model databases should have peer review processes like scientific articles, in which standards are maintained and completeness and accuracy are examined, before the data.

With respect to access, I believe that such access should be wide open to the world. Back room collaborations and data sharing are completely understandable to some extent, but data for the world should not be restricted by access. Of course, pragmatic ways of tracking downloads and access aren’t unreasonable, but that’s a finer point that need not be examined further here.

Two other problems of data sharing exist. The first is that the format of data varies greatly, from more common Comma Separated Value (CSV) data to the myriad of weird binary file formats of various types (such as MATLAB). Ensuring global compatibility is nearly impossible, though there are certain standards and automated converters that could make this a reasonable step. It would require a lot of work, however.

The second issue is much more serious. It involves the keen and unique knowledge that only the primary data collector might have over a particular data set. Aside from detailed methods of how the data is collected, cataloging the minute quirks and features of a particular data set that might affect any analysis requires a lot of work on the part of the person collecting the data. It’s very difficult to imagine this being a very fruitful exercise for each data set.

Perhaps more valuable than a database of real data would be a database of what kind of data exists. For instance, researchers who perform LFP recordings of behaving rats could volunteer to be listed in a database. Modelers or data analysis folks could then be in contact with them, so fruitful collaborations based on needs and availability could be created. The aim of this approach is different, in that it seeks to connect people, where the first method empowers anyone to examine data.

Thus there is something certainly appealing about the idea about knowledge, even in raw data form, being accessible to the world. In a sense, one could even make the reasonable argument that public funds paved the way to this research, which means that the public should get access to the fruits of the labor. But data sharing comes associated with it a whole host of intrinsic and pragmatic problems, and it also highlights major ideological flaws of the way research is currently conducted. And these are undoubtedly just a small subset of issues that exist for this type of endeavor. But an organized system of data availability would be a great ideological contribution to the neuroscientific community and could help accelerate the pace of understanding significantly.

Life 101

Before we were dumb adults, we were dumb kids. As dumb young adults, however, we’ve had to learn about a lot of adult-stuff, like cooking for ourselves, personal finance and taxes, health insurance, home buying, etc., and most of us knew next to nothing about these things, even though they are issues that most of us will deal with at some point in time. I’ve had acute experiences with all of these things recently, and I’ve learned a great deal about them. What’s surprising to me is that someone allowed me to pass high school, let alone college, without really having a good understanding of all of these things. While I’m certainly responsible for not knowing these things, I still think it’s a big hole in our education systems to properly teach nutrition and personal finance.

These are topics that should be in all high schools. Ok, so health class does exist, but I recall it being somewhat of a joke in the face of square pizza day. I feel like we could cut off a lot of future health issues in this country (coronary events, obesity, type 2 diabetes) if we at least got the right information out there about sodium intake and caloric excess.

With respect to personal finance, the number of young persons with unmanageable credit card debit is insane. It’s clearly enough of a problem in this country that it needs to be addressed on more of an epidemic level. I think this should be addressed publicly in schools as well.

While I think that the learning by living is useful and character building, blah blah, I think that there’s a level of unnecessary suffering going on with respect to these issues. We don’t have to make the mistakes of massive early debt and poor health to know the consequences of it.

Looking forward with Apple and NVIDIA

Apple recently made major changes to their laptop line, with several major updates from their previous iteration. Some of the more notable updates to me include the lack of FireWire in the MacBooks, the unibody Aluminum construction in both machines (which prompted a major board redesign), faster memory specifications, and the usage of NVIDIA’s platform in lieu of Intel.

You may say, “but the old ones had nVidia chips in them as well,” which is completely true, but it’s my guess that Snow Leopard (SL), Apple’s next generation operating system, will take the idea of general purpose graphics processing unit (GPGPU) and integrate it in two important ways. First, SL will take advantage of GPGPU on many OS level tasks that will improve performance. Second and perhaps more overlooked, I think that SL will ship with major new libraries in XCode 4, which will enable GPGPU integration in a typically programmer-friendly Apple way. In fact, it will likely be one of Apple’s core technologies, as mentioned on their site, especially if they write parallelization libraries that take advantage of the 16/32 stream parallel cores of the GPU. These innovations should benefit several machines released within the last couple of years, but these most recent updates represent a clear commitment to the cause.

Of course, more cores aren’t at all linearly scaling solutions, but there certainly are performance gains to be garnered from certain operations. I think GPGPU is brilliant right now for a few reasons. It turns out that quad core and higher in laptops will be a viable solution soon, but Apple demands portability and reasonable cooling on their laptops, so this is not quite ready.

GPGPU represents more than simply a stop-gap solution for Apple, while awaiting the solution to this quad core problem, because all of its hardware seems to be moving toward using NVIDIA graphics hardware, including their most recent MacBook and MacBook Pro updates.