Trivial or black-boxing?

I noticed today that I have a particularly unhelpful habit. It might have contributed to hundreds of hours of my time, and I feel they could have been saved with a simple question:

"Is this trivial, or should I black-box it?"

Let me explain.

As part of learning new skills, I will often come across things I don't understand. At this point, I am reaching the limits of my own knowledge and need some assistance in stretching that further to encompass something new. For example, at the moment I am learning how to take data for patch-clamp recordings and use these to estimate parameters for ion channel gating mechanisms using the Hodgkin-Huxley formalism. What I have had difficulty understanding, was how certain protocols were appropriate for parameter estimation. I have sought out further knowledge (patch-clamp recording protocols), consolidated past knowledge (gating mechanisms in Hodgkin-Huxley formalism), and for the most part all is well.

A lot of time could have been saved if I had appropriately interpreted the message my supervisor had given me. She said

"You just fit it"

I interpreted this to mean that it was a trivial exercise. That I should be able to work it out in a back of the envelope style way, or with a simple algorithm. Instead, I should really have interpreted as a black-box. It was not trivial, but I don't really need to concern myself with the details. In this case, once you have the equations, you feed it through a (mostly*) black-boxed optimisation algorithm and run your data through until you get a good fit. It seems obvious in hindsight.

However, my personal tendency is to believe that I must be doing something wrong, or that everyone else finds it easy and therefore I must be an idiot for not getting it. My personal coping mechanisms for dealing with my own ignorance.

This is what I could avoid with that simple question. When something is presented in such a way that it is mostly skimmed over, it could either be trivial or black-boxed. So I should just ask, and save myself the hassle.

* I actually quite like looking over optimisation algorithms. Even while I was wasting time looking up things that didn't help me at all, I was half concocting some Bayesian method for investigating whether there were differences in the uncertainty of parameters for persistent and transient currents with the standard protocols used for collecting these kinds of data.

Red-eared turtles

For the first time since a short lecture series on motor control in my second year of undergraduate (and a few throwaway comments by machine learning enthusiasts on biological substrates for supervised learning), my attention is turning to the cerebellum. For the next four months I'll be modelling tonic inhibition in cerebellar granule cells, but I felt that since I knew so little about the cerebellum as whole I should cast a broad review of general anatomy, network structure, functional roles, paradigms, etc. 

One of the first things that struck me was the high degree of conservation across species, where practically every modern vertebrate has some form of cerebellum. It appears to differentiate in quite an early embryonic stage from the spinal cord, making it perhaps the oldest distinct brain structure that we still have today. Whatever it is, it is certainly a bit heavier than a few nematode ganglia emerging from some barely recognisable proto-spine.

Nevertheless, this high degree of conservation has meant there are a wide array of species that can be used as model organisms for the cerebellum. It was the red-eared turtle that first took my attention though, since a highly influential model of cerebellar granule cells by Grabbiani et al. (1994) comprises a mixture of ion channels whose properties were experimentally derived from the aforementioned turtle species, and from rats. It seemed odd that a model would be based off of multiple species. Little did I know that this was barely the half of it. Ultimately, I found other models to be based off of a mess of data collected from rats, guinea pigs, turtles, frogs, and goldfish, being applied to data gathered from knockout strains of mice. Our basic understanding of the anatomy even comes from Ramon y Cajal's work with pigeons. I have no idea how I would justify this to an even remotely skeptical biologist.

Anyway, this piqued my curiosity and I wanted to look further into why in particular the red-eared turtle had been used as a model organism. Apparently, it was for four reasons:

1. Conservation of anatomy (as already discussed)

2. The shell could easily be clamped to afford high mechanical stability

3. Resilience to hypoxia

4. Lissencephalic brain

The resilience to hypoxia is rather remarkable. At low temperatures, the red-eared turtle can survive for weeks without oxygen, and even at high temperatures can survive for a number of hours. This extremely efficient anaerobic metabolism and protection from low oxygen conditions means the brain would be very well preserved and rather stable after dissection and slicing, making electrophysiological experiments far easier.

Lissencephaly, or 'smooth brain', simplified the anatomy of the cerebellum. In mammals, the cerebellum is highly folded and so localising implanted electrodes for in vivo study was difficult (especially in the 1970s, when it seemed that much of this work with turtles was carried out). This allowed greater confidence in the reliability of experiments being performed, since the researcher could provide more assurance that their recordings were empirically valid.

As such, the red-eared turtle was for a time a highly favoured biological model of the cerebellum. This seems to have died away in the last 20 years or so, especially since the rise of genetically modifiable mouse strains. Even so, I'm still astounded at how such an unrelated species could provide such insight into a whole brain structure. It also begs the question at why we still know so little about the cerebellum, when we have such a diverse set of models to work with.