Perusing the news this morning, I notice that London Metropolitan University has been banned from sponsoring non-EU foreign students because of its rather lax approach to visa enforcement, with 26/101 non-EU foreign students having no valid visa according the UK Border Agency (I also notice I’m a bit behind The Times, as they broke the story on 26th August. Shit, it’s almost September).
Firstly the timing of this, shortly before term starts, is ridiculous. The students that will be affected, legitimate or otherwise, now have a mad rush to find new courses before the academic year starts. Secondly, the general approach is all sorts of wrong. Why on Earth are legitimate, fee-paying students being punished for the failings of their university? Ashiqur Rahman nails it: the Border Agency could easily have banned the university from taking on any international students in the future, and allowed the current crop to finish their courses, thus preventing the massive upset to hundreds of legitimate students and the damage this may cause to UK higher education’s international reputation.
The government is setting up a task force to help deal with affected students, which is all fine and good and hopefully they’ll be able to do some damage limitation, but what a ridiculous waste of effort when the situation could have been avoided entirely by applying the rules with a little leniency and intelligence. Or am I missing something?
Ah yes, I was going to write about data analysis, but I got distracted by more data analysis. Anyway, here’s a bit of information on how we measure what’s going on in the brain, and how we interpret those measurements.
The brain generates and processes information using electrical signals, as far as our current understanding goes. For example, neurons in the eye respond to light by sending electrical signals to the visual cortex (via some other brain areas), where the signals are processed, interpreted, and distributed to other parts of the brain for integration with our other senses, further interpretation etc. These signals are very small, but measurable, even from the scalp – this is called electroencephalography (EEG). Each electrode of an EEG measures contributions from hundreds of thousands, perhaps millions, of neurons, so only provides a very coarse measurement of what’s really going on. At the other end of the scale, the electrical responses of an individual neuron can be measured by using a very small electrode to attach to its cell membrane, revealing the electrical activity inside it (or even to look at the currents flowing through individual channels in the membrane). This gives you information on what a single neuron is doing, but neurons never work in isolation, so you’re missing out on a lot of information about how the rest of the neuronal network is behaving.
Various types of measurement are available to bridge this scale divide. The type that I’m working with is from Utah arrays – 3.6mm square grids of 100 small electrodes that measure electrical activity from the space around neurons. This kind of measurement is similar to an EEG in that each electrode measures activity from many neurons surrounding it, but because the electrodes are placed so close to the neurons, spikes from individual neurons can also be picked up. The smaller scale allows the construction of a detailed picture of the local brain dynamics. Utah arrays are also particularly cool, because they are one of the only types of electrode that provide information on this kind of spatial scale that have also been approved for use in humans. They have already provided previously inaccessible information about epileptic brain activity in humans, and can be used to create brain-machine interfaces.
The faintly terrifying-looking Utah array. Don’t worry, it’s quite small.
The data I get is from less glamorous locations than behaving humans, but in future I may get my grubby paws on recordings from brain tissue that has been removed during epilepsy surgery. Currently I’m looking at how brain activity varies in space over the small scale that the Utah array provides, and trying to match a computer model to the information provided by the recordings. The idea then is to investigate aspects of the model that cannot be changed in an experiment, such as how neurons are connected together, in order to guide future experimental research into unhealthy brain activity.
I have been neglecting my writing duties, for which I apologise. Sorry you two.
I’ve been all over the place of late: Copenhagen visiting friends at the end of July, from whence we drove to the Wacken metal festival in Germany (best shows: Nasum, Kylesa, Volbeat). This proved true and brutal, as usual, but particularly so this year because of the mud baths created by the aggressive precipitation. Still managed to come back with a sun tan. After a fleeting visit to Hamburg, I spent most of the next week recovering, then undid most of that recovery with a visit to my sister for birthday celebrations, followed by a truly wonderful gig by Refused in London. I have a total man crush on the vocalist. The return to Newcastle on Monday took six hours: our train broke down. I managed to read a Viz from cover-to-cover though, so it wasn’t time entirely wasted.
This week I have been mostly analysing electrophysiology data from our experimental collaborators (details to follow, tomorrow maybe…), partly using a nice little toolbox for Matlab called Chronux. Unfortunately, it doesn’t seem to be under active development – the last release was in 2008 – as it has some nice functionality, but needs a little tidying up in places (one of the main authors, Partha Mitra, has co-authored a very interesting and useful book on neural data analysis and some philosophical background, but his web-site seems mysteriously inactive). If anyone knows any more details, please comment!
Do not pass out at Wacken