Neuroinformatics: scary stuff.

At the University there are always talks and lectures happening across campus and this year I have successfully managed to sneak into some quite intellectual (and generally confusing!) talks explaining new research.

brian1Recently I attended a lecture on ‘Neuroinformatics’ by Dan Goodman, a researcher at the Harvard Medical School and creator of a computer program that imitates neural behaviour called Brian.

Neuroscience, just like modern technology, is getting awfully fiddly. Long gone are the days when relatively simple discoveries such as Galvani’s seminal observation that a frog’s muscle twitches when a current is passed through it, were viewed as ground­breaking (Galvani, 1700’s). This discovery opened the door for further work exploring the role of electricity in nerve cell communication – a field which took neuroscience research to a new level of complexity.

Thanks to work by Neher and Sackmann in the 1970’s developing the patch clamp technique, we can now study electrical activity of single brain cells (neurons). The patch clamp technique allowed researchers to probe the electrophysiological (think electrical and living) properties of single neurons.

We now know that neurons communicate with one another through a special cellular language involving brief fluctuations in electrophysiological activity (known as spikes). (which look something like the image below)

Approximate plot of a typical action potential shows its various phases as the action potential passes a point on a cell membrane. The membrane potential starts out at -70 mV at time zero. A stimulus is applied at time=1 ms, which raises the membrane potential above -55 mV (the threshold potential). After the stimulus is applied, the membrane potential rapidly rises to a peak potential of +40 mV at time=2 ms. Just as quickly, the potential then drops and overshoots to -90 mV at time=3 ms, and finally the resting potential of -70 mV is reestablished at time=5 ms. Schematic of an action potential (Wikipedia)
Approximate plot of a typical action potential shows its various phases as the action potential passes a point on a cell membrane. The membrane potential starts out at -70 mV at time zero. A stimulus is applied at time = 1 ms, which raises the membrane potential above -55 mV (the threshold potential). After the stimulus is applied, the membrane potential rapidly rises to a peak potential of +40 mV at time = 2 ms. Just as quickly, the potential then drops and overshoots to -90 mV at time = 3 ms, and finally the resting potential of -70 mV is reestablished at time = 5 ms.
Schematic of an action potential (Wikipedia)

However, the brain is much more than single cells and their associated spikes, it is actually made up of many neurons, all interacting with and influencing each other. Therefore, studying the behaviour of singular neurons is a rather long, laborious process that,

Typical neuroscientist at the end of a long week
Typical neuroscientist at the end of a long week

although informative, cannot tell us about how the brain functions as a whole. This problem could be solved if we could study the human brain directly, but as you can imagine it is a little hard to get willing human volunteers. (see left)

Luckily, scientists such as Alan Turing toyed with the relationship between computers, mathematics and the brain, developing the idea of the ‘human computer’. Advances in technology mean that scientists can now study many neural interactions simultaneously. But, as the experiments grow in complexity, so does the amount of data to anaylse….(Leading to many, many sleep deprived  scientists – also see left )…

So, computer scientists and mathematicians, armed with knowledge of models and computational methods, rose to the neuroscientist’s aid. Thus, the interdisciplinary age began.

Dan argued that complicated algorithms brought some neuroscientists out in cold sweats, so he tried to create easy, user friendly, software – hence Brian was born. I could feel the itchy feet of one researcher behind me, dying to challenge the speaker to an intellectual dual (scientists are quite territorial over their fields of expertise and occasionally I have to resist the urge to stand up with a bell and shout “round 1”). The antagonist, in this case, 2268845904_e0ddae5fec_owas somewhat skeptical about Brian and its benefits over SpinNaker, another computer based simulation designed to model brain circuits. Words that I would have needed to google beforehand such as ‘GPU’, ‘jinja’  and ‘Numpy’ were thrown around the room ­ and I realised that I agreed with Dan – after a 3 year Neuroscience degree, the only word I understood was ‘Andriod’ and that is because of my phone!  At the end of the talk, he ran a demo of Brian to show how it imitates neuronal behaviors at both the network and single cell level. This is important because if we can  create a human brain online, we can manipulate it to see how diseases such as Dementia occur, giving us a sneaky bit more insight into how to tackle these problems.

Afterwards, several things dawned on me; specifically, the complexity of neuroscience analysis and how important it is to be a Jack­-of­-all-­trades in the research industry, as well as having an open mind and being a little nosy when it comes to areas of research outside your own comfort zone ­ you never know what you might need to know these days..

Post by: Clare McCullagh

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