CNV: Assignment 2

In this assignment, you will make modifications to a Topographica model to get experience in building and testing models. You will also write a short research proposal to get experience with locating and understanding scholarly neuroscientific and computational literature, and selecting research topics. The goal is to end the course in a good position to start constructing novel and feasible visual cortex models using Topographica.

You are encouraged to work in pairs of your choosing. Just submit one copy of the assignment, listing your partner, and the mark will be allocated equally to each. It is ok to work together just on Part 1 if you prefer; in that case, one of the pair should submit Part 1, listing both partners, and the other should submit only Part 2 plus a list of the two partners.

Part 1: The role of noise in map development

For this assignment you should again use the copy of Topographica installed in /group/teaching/cnv/topographica.

First, tell Topographica where you want your output files to go by default, by setting up a ~/.topographicarc file:
```
  cp /group/teaching/cnv/topographica/sample_.topographicarc ~/.topographicarc
```
You can edit ~/.topographicarc if you want your output files put into some directory other than ~/cnv; if so, be sure that directory exists.
Then copy /group/teaching/cnv/topographica/asst2.ty to your own directory, e.g. ~/cnv, and launch it:
```
  mkdir ~/cnv
  cd ~/cnv
  cp /group/teaching/cnv/topographica/asst2.ty .
  ln -s /group/teaching/cnv/topographica/topographica .
  ./topographica -g asst2.ty
```
The asst2.ty file is a slightly modified copy of examples/lissom_oo_or.ty, with a lower V1 density, four input patterns per iteration instead of two, and no noise on the initial weight values (to avoid covering up the effects of other noise). You should run it for a few iterations in the GUI and look at the various plots to verify that it works ok. You can ignore any "unused variable" or "ignoring #pragma" warnings you see on the terminal.
Then run asst2.ty in batch mode to establish a baseline for how the network behaves, before you make any changes. Sample batch mode command for running to simulation time 2500 and analysing the map at times 100, 1000, and 2500:
```
  ./topographica -a -c "run_batch('asst2.ty',times=[100,1000,2500])" -c "save_snapshot()"
```
This command takes 4.5 minutes on my 3GHz Pentium 4 machine, but could be more if your machine is slower or heavily loaded. The output will be a set of .png images in ~/cnv/Output/200803131631_asst2, where 200803131631 is the year, month, hour, and minute when the command was started (to provide a unique tag to keep track of results).
You can use your favorite image viewer to see the results, e.g. gthumb *.png. For gthumb, it works better if you go to the Preferences and set the Viewer options to have a Zoom quality of Low (to avoid smoothing) and After loading an image to Fit image to window (so that it's large enough to see).
You can also load the saved snapshot if you want, to explore the final map:
```
  ./topographica -g -c "load_snapshot('$HOME/cnv/Output/200803131631_asst2/200803131631_asst2_002500.00.typ')"
```
(using the appropriate path to your copy of the simulation output).
Always be sure to examine the .out file in the batch output directory, so that you can detect any warnings that might be important (such as parameters that you thought you changed but the code warns you were not actually changed due to typos).
This model is idealized in many ways, and the goal of this assignment will be to determine how adding various types of noise or other variability will affect the results. Topographica allows noise to be added easily to nearly every major computation, as described in Adding noise to Topographica.
For this assignment, choose four different types or locations of noise or variability, and rigorously evaluate how these affect the function and the development of the map.
For each type of noise:
1. Describe (i) what type of noise you are adding, (ii) where you are adding it, (iii) what such noise might represent biologically, (iv) why such noise might be an interesting or important case to consider, and (v) list the changes to the code that you had to make to model such noise.
  For instance, if you decide to look at the effect of having noisy input patterns, a very brief sketch of an answer might state that you will investigate zero-mean Gaussian noise added to photoreceptor activations, representing variability of photoreceptor activations, and that that you assume it to be additive and Gaussian in nature (and explain why). Doing so might be important because it can help establish how much of the variability in cortical responses could be due to variability in activations in the periphery. You would then list the change that you made to the code, e.g. changing output_fn=IdentityOF() in the definition of topo.sim['Retina'] to:
```
      output_fn=PatternCombine(generator=topo.patterns.random.\
      GaussianRandom(scale=0.1,offset=-0.05),operator=numpy.add)
```
2. Systematically adjust the amount of noise, until the overall behavior of the network is affected clearly enough that the results are easily visible (if that is possible). Considering this level of noise and also an intermediate level with less obvious effects, demonstrate and explain:
  1. What is the effect of this type of noise on the overall response of the network to an input pattern (i.e., disregarding development, how does the noise change what happens in V1)?
  2. What is the effect of this type of noise on the development of the map, such as the emergence of selectivity for orientation and the overall arrangement into a map?
  3. How sensitive is the network to this type of noise? Phrase your response in terms of whatever you are adding noise to. E.g. if adding noise to the input pattern, how strong does the noise have to be compared to the input pattern itself before you see significant effects?
  4. Explain why the network is or is not very sensitive to this type of noise, preferably by referring to specific equations (by number) in chapter 4 of the CMVC text, but at least at a qualitative level.
For each type of noise, please submit the answers to these questions (preferably as a PDF file including appropriate figures), plus a .ty file for each variant set up so that when run it demonstrates a visible impact on behavior from that particular type of noise. (That is, submit something ready for me to run, where the effect is obvious!)

Part 2: Research Proposal

Prepare a short (1-2 page) essay discussing recently published peer-reviewed work on models of topographic maps or experimental data relevant to topographic map formation or function, and proposing concrete computational experiments that you could do to build upon that work and answer an important outstanding question. It's important to propose a question that can be answered computationally (to demonstrate that you are aware of how models are used), and also to propose something novel and feasible. Models from any source can be discussed, not simply those mentioned in this course. Suggestions on how to write such a proposal are in our Proposal guidelines from the DTC Neuroinformatics Research course, although the marking criteria will be significantly less strict in this course because not all of the students are enrolled in a PhD program.

Submission

Your work must be submitted by 10am on the deadline, Thursday, 3 April, 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 per problem, plus one .ty file where appropriate, each named as listed below. Late submissions will not be accepted without good reason, and will be penalized according to the standard university policy of 5% penalty per working day or part of a day.

Example of submit command:

submit msc cnv 2 1_1.pdf 1_1.ty 1_2.pdf 1_2.ty 1_3.pdf 1_3.ty 1_4.pdf 1_4.ty 2.pdf

Tips for selecting types of noise to model

Essentially any computation performed by a physical system will involve noise, so in some sense any type of noise is fine to investigate. However, it's especially interesting to consider noise that might actually be serving a functional role, or is surprising in some way. For instance, Finn, Priebe and Ferster (2007) suggest that noise on the input to a V1 neuron can help explain contrast-invariant tuning, while Geisler and Albrecht (1997) argue that the output of V1 neurons includes a surprising amount of multiplicative noise. It would be worthwhile to do a short literature search to see if other specific roles for noise have been proposed, and if so whether they can be tested in the network used for this assignment.

Since adding the noise is easy to try, you could also simply considering trying out lots of different options, and then focusing on the four that seem most interesting or informative.

Note that because this network uses firing-rate neurons, we cannot investigate spike-timing dependent variability directly, only the corresponding variation in average firing rate that results from variable spike times.

Tips for running in batch mode

You can use batch mode to automate various tedious tasks and keep track of things. For instance, the command shown above is equivalent to:

  /group/teaching/cnv/topographica/topographica -a -c "run_batch('asst2.ty',analysis_fn=default_analysis_function,times=[100,1000,2500],default_density=30)" -c "save_snapshot()"

This runs the default_analysis_function (defined in topo/commands/basic.py) at each of the specified simulation times. You can copy that function to your own .ty file, change the function's name, and add any code inside it that you want to run repeatedly for the simulation.

Any parameter that you add at the end of the run_batch command, like default_density=30, will be set before running your .ty file, and will be put into the filename of the output directory. You can use this mechanism to keep track of the various configurations that you run. For each parameter you want to control like this, just put in code like locals().get('some_parameter',0.25) wherever the value 0.25 appeared in your .ty file. That way Python will first check if some_parameter has been defined, and if not it will use the value 0.25. See the code for default_density in asst2.ty for an example.

Tips for getting a good mark

Be sure that you provide evidence that you did each part of this assignment. 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 everything, and thought about everything.

Be sure to cite any information that you use that is not from the course material or your own experience. Including such information is encouraged, but it must be properly cited. You can use the CMVC book Bibliography database for citation information for any paper cited in the CMVC text.

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 for each problem.

Read and follow my list of writing tips.

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