- Continue implementing some of the older models (e.g. Ruppin and Reggia (1995)), and repeating the experiments to compare results.
- Try other lesions, such as adding noise to the connections.
- What symptoms appear?
- What do these symptoms represent? (This could lead to a basic paper).
- Identify weaknesses in the models, and gaps in what they can tell us.
- Be analytical: are the assumptions of ~15 years ago still correct? Is a Hopfield network still the best type of model?
- Identify possible improvements (preferably the ones involving the minimum work and the maximum impact).
- Identify a selection of plausible hypotheses from the medical literature, and investigate how these could be modelled in existing or new model classes.
- Investigate more recent classes of neural model which can be adapted for studying Alzheimer's disease; for example:
John also recommended looking into PDP++ (now called Emergent), a neural network simulator which could save some implementation time, and that for the thesis proposal I shouldn't worry too much about drawing up a specific single hypothesis, but rather focus on the experimental methodology I'll be employing (i.e. applying past work on AD modelling to recent working memory models).