Tuesday, 27 April 2010

RSMG2 published

Well I've successfully got my first proper report out of the door, which is the 5000 word progress report on what I've been doing so far throughout the year. It was accepted in my Thesis Group meeting with only minor changes to the prospective timetable being required, and it seems that I'm heading along the right track in terms of writing style in my literature reviews etc, which I'm quite pleased about. I've also managed to convince Prof. John Jefferys of the School of Neuroscience to join my Thesis Group unofficially so I can receive feedback from an experienced neuroscience researcher regarding the medical side of my work.

Other discussion points from the meeting were that I'm starting to move away from the original ideas regarding creating a model of routine memory maintenance and trying to evolve schizophrenia and Alzheimer's disease out of this, for two main reasons:
  1. Any model of 'normal working' in the brain would have to be incredibly fine-grained for it to be able to degrade in all possible ways that I'd want to test in order to try to develop the disorders. That's a lot of effort for not very much gain.
  2. Attempting to shoe-horn evolution into the system in this way "for the sake of it" wouldn't necessarily achieve much. It would be better to use the evolutionary algorithm to find ideal starting parameters (such as amount of synaptic plasticity) for the various models, as current modelling techniques tend to just pluck values out of the air with little justification. So this is one area of existing modelling which I can improve.
I was also encouraged to decide on one specific disorder to investigate (most likely AD in my case), and to use the relevant research into other disorders (schizophrenia, epilepsy, etc) to draw ideas from.

Finally, it would be beneficial to create a framework to be able to experiment on different hypotheses and models whilst keeping environmental conditions constant. This is essential for me to be able to argue that my model actually does (dis)prove a given hypothesis, rather than it simply being the case that my model is badly implemented and simply doesn't actually work.

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