Mars beckons

It’s now just over a week until I go to Mars – or more accurately, to the Mars Society’s Mars Desert Research Station. Things are looking up – the Station took delivery of three new Kawasaki ATVs recently, and when I get there, the hab will have been in use for a month, meaning that a lot of the small maintanence problems should have been sorted out. Qiagen have kindly donated fifty DNA isolation minikits for the simulation, and the commander of the simulation will be taking along the equipment that will allow us to do some gel electrophoresis – serious biological research, in other words.

I haven’t had nearly as much time as I would have liked to plan a research project for the simulation, so what I’ll be doing is more likely to build upon current projects. I’m hoping to correlate the abundance of life found with other factors – light intensity, temperature, moisture, altitude – and also extend upon a psychological study we’re doing about stress, by monitoring the weather, sleep patterns and so on. It promises to be a great trip, and I’ll admit that riding around on an ATV in a spacesuit should be no small amount of fun.

Fun New Words

New words and terms I’ve heard at my lab:

Fiascotorial, adj.: combinations or permutations of fiasco-like situations. e.g., “And then the squirrel fell into the bowl! Just imagine the fiascotorial possibilites!”

Gene-jockey, n.: derogatory term for a geneticist or molecular biology. e.g., “Those gene-jockeys working on the squirrel genome project, they don’t understand that the real discoveries are to be made in neuroscience.”

Swiss Cheese psychology: derogatory term describing the deductive methods of some psychologists. e.g., “Here’s how psychologists work – they’ll take a squirrel, scoop out its temporal lobe of the brain and then they’ll say that the temporal lobe is responsible for eating nuts, because the squirrel doesn’t eat nuts any more. Typical Swiss Cheese psychologists – it’s the law of the holes.”


During one of our classes today, we talked about the possible causes of Parkinson’s disease. One of the lecturers mentioned that in Kentucky, researchers thought they’d found a possible link between eating squirrel brains and Parkinson’s; 12 out of 42 people they surveyed with Parkinson’s ate squirrel brains, leading them to think that perhaps Parkinson’s was caused by a prion, similar to CJD.

However, when they did a control survey and looked at the general population in the area (rural Kentucky) they found that 27 out of 100 people also ate squirrel brains. So there’s probably no link, but eating squirrel brains? What the hell? Apparently they way it’s done is that they’ll run over a squirrel in the car, and then go and pick it up afterwards. Highly bizarre.


Alas and alack, &c, I haven’t been able to update much recently. I’ve just returned to Cambridge, which has been having unusually glorious weather, and have been unpacking various things. I’ve also been busy getting up to speed with the research project I’m doing this year, on (essentially) information processing in neurones.

What this means is that I’ll be using various information theory methods to try and determine whether the pattern of spike impulses given by neurones actually encodes information, and if so, how does it do it and what kind of information is it. This is pretty interesting stuff that hasn’t been done before, and it’s also quite daunting to me because while I’ve had a very casual interest in cryptography and information theory, I’m never become familiar with the equations involved.

I’ll be mixing traditional neuroscience and ‘wet biology’ practical methods with processor-intensive number crunching during my research, and I have to admit that this was not what I’d been expecting to do this year, not that I’m not looking forward to it.

Before all of that I’ve got to read a hundred-odd pages of background research and start learning a new programming language (MatLab)…


In case you’re interested, it might be worth checking out the BBC2 documentary The Dancer’s Body, on Saturday nights; I’m told it’s pretty good. An added bonus is that you should see Prof. Ramachandran on it either this week or next week, since he was interviewed for the programme while I was in the US. Something to do with the science of art, I recall, and how humans appreciate art from a neuropsychological point of view.

It was pretty fun when the BBC crew came into our lab for a while, waiting for the Prof; we threw a baseball around, chatted with the cameraman about the TV business and so on, and then got told off by their presenter for being too loud while she was on the phone. Ah, great days.


(Warning: Ramble ahead)

Earlier today, I was listening to a guy describe a project I might do next year for neurobiology, trying to figure out some of the characteristics of Golgi neurones in the cerebellum. The way you can identify these neurones, other than looking at them under a microscope, is to insert a super-thin electrode into them and look at their electrical activity. We’ve all seen what heartbeat readouts look like on TV, like a sharp spike. Well, the electrical output from neurones tends to look like that as well. Different types of neurones exhibit different and unique spike properties, such as spike magnitude, length, and interspike intervals.

So you can identify Golgi neurones by looking at their electrical readouts. This can take a bit of time, having to look back and forth all the time. What many researchers do is to hook up the output signal from the electrode to a loudspeaker, so each spike makes a click. I’m told that in time you can become extremely proficient at identifying different types of neurones very quickly by simply listening to their activity.

This kind of process is of course pattern recognition, and it struck me how skilled humans were at doing this and recognising and distinguishing new types of patterns. To do a similar thing on a computer right now would require a fair bit of coding – it wouldn’t be impossible by any means, and it might not be that difficult. But it would probably take longer than learning it yourself. That’s not to say that doing it on a computer is a waste of time, clearly if you want to automate the neurone-finding procedure and link the electrode position controls to the computer it’s worth it.

Even a computer wouldn’t be able to identify the type of a neurone with perfect accuracy though – neurones aren’t perfect things. It could give you probabilities though. And this set me onto a completely different train of thought. Usually probabilities of events or identification are shown in a numerical or percentage quantity, e.g. it’s 80% likely that it will rain tomorrow. Unfortunately, it seems that humans aren’t all too good at assessing probabilities – for example, it’s been shown that we ignore Bayes theorem while calculation probabilities ourselves.

We don’t say to each other, I think there’s an 80% probability of it raining tomorrow. We say, it’s a fairly good chance that it’ll rain tomorrow. And I think that people would respond to this type of framing probabilities better than numerical ways, in various circumstances. It just makes it more familiar.

And then I realised that we aren’t too hot on judging probabilities that way either, since according to human signal detection theories we can alter our criterion for the probability of events depending on, basically, how we’re feeling. And then I started writing this, and unfortunately I don’t have anything more to say at the moment.

GM babies

To Enhance or Not To Enhance – I’m not sure why this guy talks about artificial chromosomes, I can think of several major reasons why this would is far from the best way of genetically altering embryos. Modification of the existing embryonic DNA would work better and in a more predictable manner.