Feedback: BCY on BCI (20160503) - mobeets/nullSpaceControl GitHub Wiki

Poster/presentation only:

  • Ask the viewer: What would you expect the distribution of null space activity to be? (Force an answer, so the results don’t in the end seem “obvious.”)
  • Intuitive mapping activity → “empirical distribution observed during a different experiment.”
  • Start with motivations of hypotheses--not the implementation cartoons.
  • Unconstrained cartoon is not clear. There are easier pictures to motivate this. (Generally, it's the whole "projecting" thing I think that could be made clearer by making the row/null axes the standard cartesian ones, so it's clear you're just looking at the marginal distribution.)
  • New name for “cloud”
  • Null space tuning curves should include error bars, or box-and-whiskers.
  • Errors in mean and cov should include error bars.
  • Show three different colored clouds when you show the true activity distribution (see slide 22 of cartoons slides).
  • It’s confusing that there are two “8”s: number of cursor-target bins, and number of null space dimensions.
  • Units for null space activity are in spikes/timestep (45 msec).
  • In intro, foreshadow that there is one hypothesis that does really well, otherwise it’s too much to take in without knowing that it will pay off.
  • Indirect neurons are all in null space, but this isn’t about that.

Abstract only:

  • Doesn’t read like there’s an actual concrete result--can’t visualize the main figure from reading what we have. [Aaron, Stephen]

General comments:

  • Motivations for hypotheses:
    • Unconstrained: optimal control theory
    • Minimal energy use: muscle movement studies (Fagg)
    • Habitual: minimum intervention
  • “Output potent” instead of “row space activity” [Aaron]
  • Results in terms of a “null space activity tuning curve” will be a more efficient way of explaining how we’ve assessed these hypotheses. [Emily]
  • “Habitual + minimum intervention” instead of “task-dependent” [Will]

Questions:

  • What about dynamics? Timepoints are not independent.
  • Are null and row space actually orthogonal? PPCA doesn’t necessarily do orthogonal axes. [Byron]
  • What if you look at washout period? Does he return to what he was doing before, or still restrict himself to what was working in both previous blocks? [Alan]
  • Fit baseline and minimum hypotheses to predict intuitive block, since they don’t use the actual data. This will provide evidence that these are just bad hypotheses--that it has nothing to do with the two mappings framework that we’re evaluating the others in.
  • Would things change over days? E.g., a long-term WMP.
  • Would these findings hold for an OMP?