After a few snags with a dependency that did not want to play nicely, I managed to get the MIIND simulator installed. This is required for the next step of the project.
I now need to demonstrate that as the mu increases (which we can now predict reasonably accurately), the output firing rate steadily increases. This produces a sigmoid shape on a histogram… after a fashion!
Today I ran the one population model [to test, we need to do the same with a two population model], with a variety of different rates, which equates to different mu values. The mu can also be varied by altering the synaptic weight as well, although this would be much less significant changes, and should in theory result in exactly the same curve.
Once I have done this in NEST I will need to replicate it in MIIND
Since last week I managed to track down and resolve the problems I had been having with help from Marc. It turned out to be a quota problem; the data exceed my quota and thus overwrote parts of itself. I thought I had modified my script to do this, but evidently not. Once I had got my script using /tmp/ (and my portable hdd) instead of my home folder, everything works fine again.
I now have a working, one population model. The input and output is consistent as far as I can tell. This is something that is bothering me though, I don’t feel like I am understanding the theory as well as I should. It is certainly making my progress slow, which is frustrating. I am trying to make sense of the Amit & Brunel papers, but they’re big papers and it is all too easy to get lost n the text.
A firm grasp on the background is becoming most apparent now that I have started my final report. I will not be able to write up that section well at all unless I do.
Yesterday was not a good day for my project; I found myself being propelled backwards from the doorstep of phase 2, ending up at the gates of phase 1, right where I started…. well sort of. It certainly felt that way.
The most significant acheivement I have made som far is confirming that I can correctly predict the properties of the output for a neuron population. The next stage is to use models with multiple populations. Before I could move on to this stage, I needed to remind myself of the tools that I had been using before.
The way to do this was to repeat the post-processing of the simulated output for the single population model. As the output files are mammoth in size, I can only store them in /tmp/. This means I had to run the simulation again as well.
Something is not right. While the size of the file is almost identical, the order of magnitude of spikes is correct, my post-processing tool was failing. Given that the output for any given model should be identical no matter how many times it is run, and that the tool worked before, this should not happen. I have modified the tool to cope with the error and produce the meaningful output.
The output I now get still fits the theoretical predictions, and so is correct. However, it is most disconcerting that it is not identical as it should be. I have yet to establish what exactly causes the NEST simulator to behave differently, or if the annomolies are due to the corrupt file. Even if that is the case, I do not know what is causing that to happen.
I will definately be mentioning this to Marc in our next meeting.