CNV: Assignment 2 (project option)

Note: For this assignment, choose either this open-ended project option, or the noise assignment; do not submit both. You are encouraged to work in pairs, in which case one report will be submitted, both partners must contribute equally, and the scope or quality of the project will be expected to be more than for a single project.

For the project, you will implement or modify an existing Topographica simulation to model a novel phenomenon or to add a novel mechanism. The assignment is deliberately open ended, and you should be careful to keep in mind the overall fraction of your course mark that this assignment represents (25%) so that you can spend your time accordingly.

Project

First, you should choose an appropriate topic and research plan. Your plan should consist of novel, concrete computational experiments that you can do to build upon published peer-reviewed work on models of topographic maps, or experimental data relevant to topographic map formation or function. There are three equally important constraints to consider: your research questions must be possible to answer computationally, the results (if successful) should be novel (not replicating existing work), and the project must be feasible in the short time available (which means that the work is likely to be incremental, which is fine). You must discuss the general topic area and research plan with me (Jim) before starting work in earnest, in part so that I can ensure that it is novel and feasible (and give my opinion on how interesting it is :-).

One obvious approach would be to choose a model already implemented in Topographica, and extend or modify it in an interesting and novel way. However, developing a small model from scratch is also fine, as long as you are not overly ambitious.

In either case, some possible questions to consider are described in the Future Work chapter of the CMVC text, but note that many of those have now been done by people here and elsewhere, so it is vital to check with me first. Some that are still open include:

Add feedback from V1 to LGN (proposing and evaluating a specific and interesting computational role); see CMVC 17.2.13.
Add suitable additional laminae in V1 (beyond L4 + L2/3) or additional cell types, with specific circuitry motivated by experimental results.
Implement a basic model of invariant object recognition (using principles from HMAX or VisNet, but in a topographically organized region); see CMVC 17.2.11.
Implement learning in push–pull afferent connections to V1 (where inhibitory feedforward connections should strengthen when the neurons are anti-correlated); see CMVC 17.1.2 (and maybe Kayser and Miller (2002). Ideally, the same model could then be used for both LGN and V1 neurons.
Replicate and explain species differences in topographic maps, such as for joint OR/OD maps; see CMVC 17.2.2 (and published work from other models, e.g. from Wolf and Geisel, and possibly the elastic net).
Develop a more detailed model of the process of OD map development — effects of prenatal retinal waves followed by postnatal visual experience; see CMVC 17.2.3 and Jegelka et al. 2005. May need to implement layer 2/3 on top of layer 4, with timing differences, to explain the data.
Test for the tilt illusion in LISSOM, e.g. by using two eyes and binocular stimuli; see CMVC 17.2.5.
Determine if perceptual learning in an already-organized network can lead to hyperacuity; see CMVC 17.2.7.
Derive and demonstrate an interesting analytical result about topographic map models (e.g. proving aspects of them optimal or suboptimal according to information theory or probability theory); see CMVC 17.3.1.
Adapt the LISSOM LM or LMS simulations to model dichromatic LS color in a non-primate species such as ferret.

Please bear in mind that you are not required to get "good" or even interesting results, as long as you make a good-faith effort to evaluate your hypotheses. Your report (below) will include whatever results you obtained, whether or not they matched your expectations or establish the point you set out to establish.

Be sure to see the noise assignment's Tips for running in batch mode.

Report

Once you have completed the project, you will need to write up a report in the style of a conference paper, such as mine from CNS 2005 or CNS 2004. The report should be about 6-8 pages, and must contain at least the following sections:

Introduction/Background/Literature Review: What you are going to look at, and why that is novel and interesting. This section or sections must include references to specific scholarly, peer-reviewed papers (e.g. from pubmed.gov) on which you based your work, and exactly how your work relates to them.
Methods/Architecture: How your approach differs from a specific earlier paper or papers. If your model is simple, you can report all of it; otherwise you need only report precisely how it differs from previously published work (and why).
Results: Figures and text demonstrating what you found.
Discussion/Analysis/Future Work: Interesting aspects of the results, their significance, how they relate to biological or other prior data, and possibilities for further work.
Conclusion: What the reader should take away from this study.
Bibliography: Any information that you use that is not from the course material or your own experience must be cited in the text, typically using (Author, Year) format. The complete bibliographic info (including everything stated in my writing tips) must then appear in the bibliography. You can use the CMVC book Bibliography database for citation information for any paper cited in the CMVC text.

If your results support the hypothesis you were testing, then the report should convince the reader of the truth of that hypothesis. If If things do not work out as you planned, you just need to convince the reader that you had a reasonable approach, and that you put it into place properly.

Your report will almost certainly improve if you consider my list of writing tips.

Submission

Your work must be submitted by 10am on the deadline, using the submit command on Informatics DICE machines (type man submit for more details). Your work should be in the form of one plain PDF file named report.pdf, plus one or more .ty or .py files with your code. Late submissions will not be accepted without good reason, according to the standard university policy.

Example of submit command:

submit msc cnv 2 report.pdf model.ty components.py

Tips for getting a good mark

For this assignment, you will need to convince me that you can do novel research in computational neuroscience, clearly conveying what you did, why you expected it to be significant or interesting, and what the results were (whether good or bad). Remember that I can only judge what is actually submitted, so you should make sure that the files you submit make it clear that you have done what the assignment requested, and that you have thought about what each part means and represents.

Submissions must be in PDF except by prior approval with the instructor. I can be sure to be able to read PDF; others like .doc or .sxw have a certain probability of working properly, but the probability is far from 1.0. Naming the files as I suggest will make my job a lot easier, because I will be able to see exactly what you are submitting.

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