A ground-breaking proof

Today I mathematically proved something that has long been suspected, but never confirmed in such a rigorous way.

I was working on my Models of the Neuron homework, and after three or four intense lines of arithmetic, I got back to the equation I started with.

What did this prove?

Well, I was feeling pretty tired today, as I probably needed about another hour of sleep, but I had a ton of espresso from our magic and convenient little X6. Despite the coffee, and probably due to the lack of sleep, I got absolutely nowhere (fast!) with my arithmetic.

Thus, I proved that coffee is not an adequate substitute for sleep. QE-effin’-D.

Anyway, this brings me ’round to another thing I thought of. I have a favorite, rather bad science joke. I certainly didn’t make it up, but I should have. It goes like this:

Werner Heisenberg was speeding down the road one day in a little two-seater Mercedes when he was pulled over by a police officer. The officer walked up to the car and looked down at Heisenberg, “Do you know how fast you were going?”

“No,” said Heisenberg wryly, “but I know exactly where I am!”

Do you have any cheesy science jokes?

I have working neurons!

After noticing a typo (hello missing negative sign!) in the paper, and debugging two typos in my implementation, I have what seem to be two working models (VCN bushy cells Types I-c and II). Here I’m applying a constant 100 pA stimulus to them. Already I see significant differences between the two cell types:

Programming + Espresso = Happy Brock

At the moment I’m working on my Models of the Neuron Project #2. In particular, I’m coding a model of ventral cochlear neuron bushy cells, using Octave rather than Matlab.

I’ve also just had another shot of espresso from our new X6, and I’m listening to an all-string-instrument tribute to Radiohead’s OK Computer. It’s so beautiful it’s giving me chills.

This is what I love to do. I feel good. I’m coding something. When it’s done, it will be a useful, functional, scientific model of a biological system. I’m going to use it to examine the effects of input regularity on its output. There are some technical obstacles I need to figure out, but I love doing that. It’s nice to feel like I’m getting somewhere and doing something useful again.

Here’s a bonus plot of excitatory post-synaptic potentials from two different types of bushy cell models! These aren’t anything complicated, they’re just inputs for the cell, but they look like they’re supposed to, so I’m quite happy.

13 days left

I’ve still got:

• my Models of the Neuron Project #2
• my Models of the Neuron Homework #8
• my Models of the Neuron Final Exam
• Assorted Research (300 Cal. per serving)

to do before I drive down to New Orleans and then fly to Seattle for Wintereenmas. Today, despite a marathon 4-hour meeting I managed to:

• Start my Models of the Neuron Project #2
• Get my undergrad going with running jobs on the cluster
• … drink espresso from our kickass FrancisFrancis! X6 (twice)
• have delicious teriyaki salmon bento lunch

and, our new desktops (for some people in the lab) FINALLY arrived today, after a debacle with Monarch Computer from whom I will probably never order again. Not a bad day despite being Ã¼ber-mÃ¼de (mÃ¼de means tired — I know all you geeks know what Ã¼ber means).

Mathematical Atrophy

Practical mathematical ability, like other parts of the body (since it is encoded somewhere in the brain), atrophies over time. By the end of high school, I was able to score a 5 on the AP calculus BC exam, which allowed me to skip Calc I & II at Tulane. Freshman year, I learned more math, although I didn’t really like it and was going through a major adjustment to college life, and I didn’t pick it up very well. By sophomore year, I’d forgotten most of the integration tricks I knew, and some of the derivatives.

From time to time, I’ve re-learned this stuff. Experience (and scientific studies) have shown that it’s normal to re-learn things much more quickly that the initial learning time, which is nice. My research places me in kind of an odd spot though.

If I were doing purely theoretical work, I’d have pencil to paper all of the time, integrating, derivatizing (as we said in HS), solving differential equations, substituting variables, etc, etc. But I don’t do purely theoretical work.

On the other hand, were I an experimentalist or a clinician, my work would be much more hands-on, so my math skill would atrophy a great deal, but it wouldn’t matter as much.

I work somewhere in the middle. Our software is sufficiently advanced that my work is a lot like hands-on experimental work. It’s important for me to understand the underlying theory, and I do, but understanding it and sitting down to work out math problems (especially if you’ve gained the understanding by doing so already in the past) is a different story. I go through long periods without serious math work, punctuated by short bursts where I have to dig all the way back in. Lately it’s been for a class.

I used to feel guilty about this — I probably wouldn’t have even posted it on this blog a couple of years ago. However, after hearing many other academics — several of whom are much more advanced in their careers than me, and well respected — lament a similar problem, I no longer feel guilt. It’s a common problem. For professors that teach university classes, the problem is somewhat alleviated, as they have to teach classes on this stuff and it keeps them fresh.

Not to say that I want to have to teach any classes right now, mind you.

I’d be interested to hear if you have any suggestions for staying sharp when I’m in a mathematical lull.