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ref: Olson-2005 tags: Arizona rats BMI motor control training SVM single-unit left right closed-loop learning Olson Arizona date: 01-03-2012 23:06 gmt revision:1 [0] [head]

bibtex:Olson-2005 Evidence of a mechanism of neural adaptation in the closed loop control of directions

  • from abstract:
    • Trained rats to press left/right paddles to center a LED. e.g. paddles were arrow keys, LED was the cursor, which had to be centered. Smart rats.
      • Experiment & data from Olson 2005
    • Then trained a SVM to discriminate left/right from 2-10 motor units.
    • Once closed-loop BMI was established, monitored changes in the firing properties of the recorded neurons, specifically wrt the continually(?) re-adapted decoding SVM.
    • "but expect that the patients who use the devices will adapt to the devices using single neuron modulation changes. " --v. interesting!
  • First page of article has an excellent review back to Fetz and Schmidt. e.g. {303}
  • Excellent review of history altogether.
    • Notable is their interpretation of Sanchez 2004 {259}, who showed that most of the significant modulations are from a small group of neurons, not the large (up to 320 electrodes) populations that were actually recorded. Carmena 2003 showed that the population as a whole tended to group tuning, although this was imperfectly controlled.
  • Also reviewed: Zacksenhouse 2007 {901}
  • SVM is particularly interesting as a decoding algorithm as it weights the input vectors in projecting onto a decision boundary; these weights are experimentally informative.
  • Figure 7: The brain seems to modulate individual firing rate changes to move away from the decision boundary, or at least to minimize overlap.
  • For non-overt movements, the distance from decision function was greater than for overt movements.
  • Rho ( ρ\rho ) is the Mann-Whitney test statistic, which non-parametrically estimates the difference between two distributions.
  • δf(X t)\delta f(X_t) is the gradient wrt the p input dimensions o9f the NAV, as defined with their gaussian kernel SVM.
  • They show (i guess) that changes in ρ\rho are correlated with the gradient -- e.g. the brain focuses on neurons that increase fidelity of control?
    • But how does the brain figure this out??
  • Not sure if i fully understand their argument / support.
  • Conclusion comes early in the paper
    • figure 5 weakly supports the single-neuron modulation result.

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ref: -0 tags: caltech right date: 01-11-2011 05:15 gmt revision:0 [head]

Commentary on Why Caltech Is in a Class by Itself (I do not go to Caltech, but this is not the reason the article rubbed me):

This is a good argument, especially given the real need to educate students in STEM subjects. However I would like to make four counterpoints:

1. Only accepting students based on test scores - as your article suggests - strikes me as a rather narrow criteria. There are many forms of intelligence, and the variety of problems is much broader than those posed within a SAT - surely there should be some leeway in accepting individuals? Any single criteria would seem to impoverish the student body.

2. Maybe affirmative action will not heal wounds; I have not read your book (obviously). But what would happen if all universities stopped accepting minority students? Our society is already stratified, and this would make it worse. (The counterargument would be that by making the criteria equal, minorities would be forced to rise up - yes, eventually, but slow enough to not set off a positive feedback loop)

3. I don't watch sports myself, but a lot of my friends do. Sure, there may be an excess now of section-9 type stuff, but is there anything wrong with cultivating athletic excellence? Is it not inspiring to be on campus with these people as well as to watch them? More importantly, people *really enjoy* athletics, which seems a good enough reason to me.

4. Where's your soul, man? My criteria would be: "Caltech is great because the students there are motivated and happy, and when they graduate they go on to contribute to and greatly enjoy the f- out of life." which is true, btw.