A Musician on Science

Science isn't nearly as honest as music is. Yes, neither are monolithic. I no more study science now than I studied music almost decade ago. Even so, my experiences with each are deep enough to justify some simple comparisons.

In neither, is the most esteemed pathway- making a significant new discovery or being a rock star- likely to happen.

Music provides alternatives to being a rock star that are based on skill. Becoming a rock star almost always comes down to luck. But, you can be a studio musician. You can train in classical or jazz music. Years of practice can develop artistic talent into the ability to bend sound into an expression of yourself.

The best musician got ahead often enough for it to be a rule of thumb that the best way to make it as a musician was to develop your hands and mind as a musician.


How science determines its losers and runners-up seems arbitrary.  I don't know what all other budding scientists do at night. Some are in the lab. Others aren't and I've seen no emerging trend that those who work the hardest make it. It's not clear who will succeed or why.


Science has a new crop of believers every year and so it can counter unstable practices with access to new markets and specious war stories.

Some Thoughts on Animal Experimentation

A recent confluence of events motivated me to think more deeply about the use of animals in my research. The confluence:

• I read a Financial Times article on how McDonald's pledged to stop buying pork from farmers who use gestation crates. I didn't know this practice existed
• I saw The Daily Show's take on PETA's lawsuit against Seaworld
• The lab boss asked me to write protocols to get approval to do major surgery on rats to better understand how cocaine affects the brain.

The FT article reminded me how industrial farming, while commercially beneficial, has a dark side. I don't agree with PETA that all species are equal. I support it pushing this issue because, in an increasingly competitive economy, the pressure to use exploit anything will only mount. Finally, the lab boss's request recalls the similarity between the rationales both agribusiness and science use; namely, "the ends justify the means". Justifiable doesn't mean justified. There's always that twinge that gives me pause before the animal is fully anesthetized or when you are euthanizing it- the euphemism is "sacrificing". Unless we are willing to do more speculative experiments on humans, using animals for research, even when done properly, seems to be a necessary evil due to how little we know about disease.

Modern science is very much a business. Published papers are something like quarterly reports. Curing cancer, however, is different from making the next iPad. The creep of business vocabulary into science is just as disturbing as it was in medicine. Are grad students a waste of money because the cost of training them outweighs the data (widgets) they produce? Why not underspend on equipment for animal experimentation because the animals won't "complain"?

Unlike other blog entries, there's no promise of code. I'm also sure I'll edit the exposition. Even in the alpha version I hope I've conveyed that I'm not sure why appealing to "it's for science", especially as that becomes code more and more for "it's for the bottom line of my lab", is different from industry claiming cost-efficiency.

Javascript Tools for Neuroscience

MATLAB,C, and Python are, in my experience, the programming languages of choice in computational neuroscience. They, however, lack the capability for dynamic visualization that Javascript, Julia, or Mathematica allow. By dynamic visualization I mean the ability to alter, in real-time, the data and see how the results of analyses change. This is useful not only for teaching the methods but also for the type of interactive simulation that, I feel, does the most for designing experiments. Of those latter three languages, Javascript is the most widely supported and, perhaps, familiar.

In the coming weeks you will find versions of neuroTools.js available for download in the code section of my website. In the courses and notes section you will example visualization to explain the data analysis methods I use.

I'll begin with the interspike interval distance (ISI-D), Granger causality, Causal State Splitting Reconstruction, and Lempel-Ziv complexity.

I greatly appreciate any constructive feedback.