Summary: weekly mtg 20161207 (Byron, Steve, Matt, me) - mobeets/nullSpaceControl GitHub Wiki

Discussed:

histogram overlap

  • just values at first
    • values for particular session in figure
    • and then in text "on any session, no hyp did better than __"
  • then in supp, show bars for first half
    • also in supp show scores for each session (heatmap plot)
    • scores for each session for first part alone too!

make it clear there are multiple datasets

  • for first half, use intuitive
  • for second half, make it super clear that we can now refit the previous hyps now on different data, to make it easy to compare with the new hyps

SSS: try cloud with intuitive row space values to make expansion-contraction values

  • does this make cloud have ratio nearer 0? if so, this explains why normal cloud has value > 0: just due to resampling given different freqs of row space vals

SSS by monkey?

supp figs: fits in reverse

check how often cloud samples different points

  • i.e., does cloud force you to visit every point in the space?

take intuitive data and view in perturbation histograms

  • this will tell us if distributions are really preserved
  • and then emphasize that cloud does predict a slight distribution change, and in the right direction
  • because otherwise, why do we need the simulations?
  • i.e.: view intuitive, observed (perturbation), and cloud predictions

add discussion paragraph on whether distributions change

  • and whether or not this is a version of unconstrained

title: behaviorally equivalent

  • keep this in abstract
  • only mention output-null once we get into text, and make an explicit link to behaviorally equivalent
    • i.e., "the behaviorally equivalent points all lie in the same output-null space"

NDSEG:

  • change phrasing to make stronger claims
    • e.g., "I am going to BE THE GUY who SOLVES THIS PROBLEM"
  • also, make sure there are two sentences of "deep dives" in third para
    • as opposed to just high-level

To discuss:

  • title: Predicting neural activity in [behaviorally OR task] [irrelevant OR equivalent] dimensions
  • text: task-equivalent vs. output-null
  • text: "muscle-inspired" vs. "energy hypotheses"; "hypotheses in multiple tasks" vs. "task-transfer hypotheses"
  • marginal distribution error metric
  • supplemental figures
  • Fig 4: put behavior in supplement; describe IME after showing results?
  • SSS: error bars; why cloud a slight expansion?

Tiny things:

  • author contributions
  • use Nelson data --> Emily as author?
  • highlights (bullet points)
  • in brief (one sentence of < 40 words)