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 ( ) is the Mann-Whitney test statistic, which non-parametrically estimates the difference between two distributions.
- 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 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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