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[0] Gage GJ, Ludwig KA, Otto KJ, Ionides EL, Kipke DR, Naive coadaptive cortical control.J Neural Eng 2:2, 52-63 (2005 Jun)

[0] Taylor DM, Tillery SI, Schwartz AB, Direct cortical control of 3D neuroprosthetic devices.Science 296:5574, 1829-32 (2002 Jun 7)

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ref: -0 tags: adaptive optics sensorless retina fluorescence imaging optimization zernicke polynomials date: 11-15-2019 02:51 gmt revision:0 [head]

PMID-26819812 Wavefront sensorless adaptive optics fluorescence biomicroscope for in vivo retinal imaging in mice

  • Idea: use backscattered and fluorescence light to optimize the confocal image through imperfect optics ... and the lens of the mouse eye.
    • Optimization was based on hill-climbing / line search of each Zernicke polynomial term for the deformable mirror. (The mirror had to be characterized beforehand, naturally).
    • No guidestar was needed!
  • Were able to resolve the dendritic processes of EGFP labeled Thy1 ganglion cells and Cx3 glia.

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ref: Gage-2005.06 tags: naive coadaptive control Kalman filter Kipke audio BMI date: 09-13-2019 02:33 gmt revision:2 [1] [0] [head]

PMID-15928412[0] Naive coadaptive Control May 2005. see notes

____References____

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ref: Harris-2011.12 tags: mechanically adaptive electrodes implants case western dissolving flexible histology Harris date: 01-25-2013 01:39 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-22049097[0] Mechanically adaptive intracortical implants improve the proximity of neuronal cell bodies.

  • See also [1]
  • Initial tensile modulus of 5GPa dropped to 12MPa. (almost 500-fold!)
    • Their polymer nanocomposite (NC) still swells 65-70% (with water?)
    • Implant size 100 x 200um.
  • Controlled with tungsten of identical size and coating.
  • Tethered to skull.
  • Interesting:
    • The neuronal nuclei density within 100 µm of the device at four weeks post-implantation was greater for the compliant nanocomposite compared to the stiff wire.
    • At eight weeks post-implantation, the neuronal nuclei density around the nanocomposite was maintained, but the density around the wire recovered to match that of the nanocomposite.
    • Hypothesis, in discussion: softer implants are affecting the time-course of the response rather that final results
  • The glial scar response to the compliant nanocomposite was less vigorous than it was to the stiffer wire
  • Cultured astrocytes have been shown to respond to mechanical stimuli via calcium signaling (Ostrow and Sachs, 2005).
  • Substrate stiffness is also known to shift cell differentiation in mesenchymal stem cells to be neurogenic, myogenic, or osteogenic (Engler et al., 2006).
  • In vivo studies which focus on the effects of electrode tethering have shown that untethered implants reduce the extent of the glial scar (Biran et al., 2007; Kim et al., 2004; Subbaroyan, 2007)
  • Parylene, polymide, and PDMS still each have moduli 6 orders of mangitude larger than that of the brain.
  • In some of their plots, immune response is higher around the nanocomposites!
    • Could be that their implant is still too large / stiff?
  • Note that recent research shows that vitemin may have neuroprotective effects --
    • Research has linked vimentin expression to rapid neurite extension in response to damage (Levin et al., 2009)
    • NG2+ cells that express vimentin have been proposed to support repair of central nervous system (CNS) damage, and stabilize axons in response to dieback from ED1+ cells (Alonso, 2005; Nishiyama, 2007; Busch et al., 2010)
  • Prior work (Frampton et al., 2010 PMID-20336824[2]) hypothesizes that a more compact GFAP response increases the impedance of an electrode which may decrease the quality of electrode recordings.

____References____

[0] Harris JP, Capadona JR, Miller RH, Healy BC, Shanmuganathan K, Rowan SJ, Weder C, Tyler DJ, Mechanically adaptive intracortical implants improve the proximity of neuronal cell bodies.J Neural Eng 8:6, 066011 (2011 Dec)
[1] Harris JP, Hess AE, Rowan SJ, Weder C, Zorman CA, Tyler DJ, Capadona JR, In vivo deployment of mechanically adaptive nanocomposites for intracortical microelectrodes.J Neural Eng 8:4, 046010 (2011 Aug)
[2] Frampton JP, Hynd MR, Shuler ML, Shain W, Effects of glial cells on electrode impedance recorded from neuralprosthetic devices in vitro.Ann Biomed Eng 38:3, 1031-47 (2010 Mar)

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ref: Taylor-2002.06 tags: Taylor Schwartz 3D BMI coadaptive date: 01-08-2012 04:29 gmt revision:7 [6] [5] [4] [3] [2] [1] [head]

PMID-12052948[0] Direct Cortical Control of 3D Neuroprosthetic Devices

  • actually not a bad paper... reasonable and short. they adapted the target size to maintain a 70% hit rate, and one monkey was able to floor this (reach and stay at the minimum)
  • coadaptive algorithm removed noise units based on (effectively) cross-validation.
    • both arms were restrained during performance & co-adaptation. Monkeys initially strained to move the cursor, but eventually relaxed.
  • Changes from hand control to brain control random but apparently somewhat consistent between days.
  • continually increasing performance in brain-control for both monkeys, arguably due to the presence of feedback and learning. They emphasize the difference between open-loop (Wessberg) and closed-loop control. (42 ± 5% versus 12 ± 5% of targets hit)
    • still, the percentage of correct trials is low - ~50% for the 8 target 3D task.
    • monkeys improved target hit rate by 7% from the first to the third block of 8 closed-loop movements each day.
  • claim that they were able to record some units for up to 2 months ?? ! In their other monkey, with teflon/polymide coated stainless electrodes, the neural recordings changed nearly every day, and eventually went away.
  • quote: Cell-tuning functions obtained during normal arm movements were not good predictors of intended movement once both arms were restrained. interesting.
  • coadaptive algorithm:
    • Raw PV yielded poor predictions.
    • first, effectively z-score the firing rate of each neuron.
    • junk / hash neurons were not removed.
    • Two different weights per neuron per axis (hence 6 weights altogether), one if firing rate was above the mean value, another if it was below. corrected for resulting drift. Sum (neuronal firing rates * weights) controlled velocity on each of the axes. (Hence, it is not surprising that the brain-control tuning was significantly different from the hand control - the output model is vastly different).
    • restarted the coadaptive algorithm every day?
    • coadaptive algorithm appears to be something like stochastic gradient descent with a step-size that decreases with increasing performance.
      • From her Case-western website, Dawn Taylor still seems to be on the coadaptive kick. Seems like it's bad to get stuck on one idea all your life ... though perhaps that is the best way to complete something.
    • Their movies in supplementary materials look rather good, better than most of the stuff that we have done. She did not quantify SNR or correlation coefficient.

____References____

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ref: -0 tags: FIR LMS decorrelation adaptive filter matlab myopen date: 01-03-2012 03:35 gmt revision:7 [6] [5] [4] [3] [2] [1] [head]

LMS-based adaptive decorrelator, xn is the noise, xs is the signal, len is the length of the signal, delay is the delay beyond which the autocorrelation function of the signal is zero but the acf of the noise is non-zero. The filter is very simple, and should be easy to implement in a DSP.

function [y,e,h] = lms_test(xn, xs, len, delay)
h = zeros(len, 1); 
x = xn + xs; 
for k = 1:length(x)-len-delay
	y(k) = x(k+delay:k+len-1+delay) * h ; 
	e(k) = x(k) - y(k); 
	h = h + 0.0004 * e(k) * x(k+delay:k+len-1+delay)'; 
end
It works well if the noise source is predictable & stable: (black = sinusoidal noise, red = output, green = error in output)

Now, what if the amplitude of the corrupting sinusoid changes (e.g. due to varying electrode properties during movement), and the changes per cycle are larger than the amplitude of the signal? The signal will be swamped! The solution to this is to adapt the decorrelating filter slowly, by adding an extra (multiplicative, nonlinear) gain term to track the error in terms of the absolute values of the signals (another nonlinearity). So, if the input signal is on average larger than the output, the gain goes up and vice-versa. See the code.

function [y,e,h,g] = lms_test(xn, xs, len, delay)
h = zeros(len, 1); 
x = xn + xs; 
gain = 1;
e = zeros(size(x)); 
e2 = zeros(size(x)); 
for k = 1:length(x)-len-delay
	y(k) = x(k+delay:k+len-1+delay) * h; 
	e(k) = (x(k) - y(k)); 
	h = h + 0.0002 * e(k) * x(k+delay:k+len-1+delay)'; % slow adaptation. 
	y2(k) = y(k) * gain; 
	e2(k) = abs(x(k)) - abs(y2(k)); 
	gain = gain + 1 * e2(k) ; 
	gain = abs(gain);
	if (gain > 3) 
		gain = 3;
	end
	g(k) = gain; 
end

If, like me, you are interested in only the abstract features of the signal, and not an accurate reconstruction of the waveform, then the gain signal (g above) reflects the signal in question (once the predictive filter has adapted). In my experiments with a length 16 filter delayed 16 samples, extracting the gain signal and filtering out out-of-band information yielded about +45db improvement in SNR. This was with a signal 1/100th the size of the disturbing amplitude-modulated noise. This is about twice as good as the human ear/auditory system in my tests.

It doesn't look like much, but it is just perfect for EMG signals corrupted by time-varying 60hz noise.

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ref: Helms-2003.01 tags: Schwartz BMI adaptive control Taylor Tillery 2003 date: 11-26-2011 00:58 gmt revision:1 [0] [head]

PMID-12929922 Training in cortical control of neuroprosthetic devices improves signal extraction from small neuronal ensembles.

  • Lays out the coadaprive algorithm.
  • with supervised / adaptive training, ML estimator is able to get 80% of the targets correct.
  • Reviews in the Neurosciences (conference) Workshop on Neural and Artificial Computation.

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ref: Nakanishi-2005.01 tags: schaal adaptive control function approximation error learning date: 0-0-2007 0:0 revision:0 [head]

PMID-15649663 Composite adaptive control with locally weighted statistical learning.

  • idea: want error-tracking plus locally-weighted peicewise linear function approximation (though , I didn't read it all that much in depth.. it is complicated)