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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] Wood F, Fellows M, Donoghue J, Black M, Automatic spike sorting for neural decoding.Conf Proc IEEE Eng Med Biol Soc 6no Issue 4009-12 (2004)

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ref: -0 tags: hahnloser zebrafinch LMAN HVC song learning internal model date: 10-12-2018 00:33 gmt revision:1 [0] [head]

PMID-24711417 Evidence for a causal inverse model in an avian cortico-basal ganglia circuit

  • Recorded an stimulated the LMAN (upstream, modulatory) region of the zebrafinch song-production & learning pathway.
  • Found evidence, albeit weak, for a mirror arrangement or 'causal inverse' there: neurons fire bursts prior syllable production with some motor delay, ~30ms, and also fire single spikes with a delay ~10 ms to the same syllables.
    • This leads to an overall 'mirroring offset' of about 40 ms, which is sufficiently supported by the data.
    • The mirroring offset is quantified by looking at the cross-covariance of audio-synchronized motor and sensory firing rates.
  • Causal inverse: a sensory target input generates a motor activity pattern required to cause, or generate that same sensory target.
    • Similar to the idea of temporal inversion via memory.
  • Data is interesting, but not super strong; per the discussion, the authors were going for a much broader theory:
    • Normal Hebbian learning says that if a presynaptic neuron fires before a postsynaptic neuron, then the synapse is potentiated.
    • However, there is another side of the coin: if the presynaptic neuron fires after the postsynaptic neuron, the synapse can be similarly strengthened, permitting the learning of inverse models.
      • "This order allows sensory feedback arriving at motor neurons to be associated with past postsynaptic patterns of motor activity that could have caused this sensory feedback. " So: stimulate the sensory neuron (here hypothetically in LMAN) to get motor output; motor output is indexed in the sensory space.
      • In mammals, a similar rule has been found to describe synaptic connections from the cortex to the basal ganglia [37].
      • ... or, based on anatomy, a causal inverse could be connected to a dopaminergic VTA, thereby linking with reinforcement learning theories.
      • Simple reinforcement learning strategies can be enhanced with inverse models as a means to solve the structural credit assignment problem [49].
  • Need to review literature here, see how well these theories of cortical-> BG synapse match the data.

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ref: Li-2009.07 tags: Zheng Odoherty Nicolelis unscented kalman wiener filter date: 01-07-2012 23:57 gmt revision:1 [0] [head]

PMID-19603074[0] Unscented Kalman filter for brain-machine interfaces.

  • Includes quadratic neuron tuning curves.
  • Includes n-1 past states for augmented state prediction.
  • Population vector .. has < 0 SNR.
  • Works best with only 1 future tap .. ?
  • Pursuit and center-out tasks.

____References____

[0] Li Z, O'Doherty JE, Hanson TL, Lebedev MA, Henriquez CS, Nicolelis MA, Unscented Kalman filter for brain-machine interfaces.PLoS One 4:7, e6243 (2009 Jul 15)

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ref: thesis-0 tags: clementine Kalman wiener tlh24 date: 01-06-2012 03:08 gmt revision:3 [2] [1] [0] [head]

040507. wiener pred. same deal as {262}

kalman fit/pred.

per-unit and channel aggregate SNR summary

    unit     chan       lag       snr       behav var
    1.0000   69.0000    1.0000    1.1159    2.0000
    1.0000   58.0000    1.0000    1.1074    6.0000
    2.0000   44.0000    2.0000    1.1040    2.0000
    2.0000   44.0000    1.0000    1.0953    2.0000
    2.0000   93.0000    1.0000    1.0868    3.0000
    2.0000   64.0000         0    1.0728    3.0000
    1.0000   69.0000    2.0000    1.0698    2.0000
    1.0000   32.0000         0    1.0684    3.0000
    2.0000   44.0000         0    1.0634    8.0000
    1.0000   58.0000         0    1.0613    6.0000
    1.0000   33.0000    1.0000    1.0594    1.0000
    2.0000   93.0000    3.0000    1.0523    3.0000
    1.0000   63.0000         0    1.0507    3.0000
    1.0000   67.0000    1.0000    1.0490    5.0000
    1.0000   47.0000         0    1.0489    3.0000
    1.0000   12.0000    4.0000    1.0472    3.0000
    2.0000   93.0000    2.0000    1.0460    3.0000
    1.0000   24.0000         0    1.0459    3.0000
    1.0000   42.0000    1.0000    1.0447    6.0000
    1.0000   24.0000    1.0000    1.0440    3.0000
    1.0000   69.0000    3.0000    1.0431    2.0000
    2.0000   60.0000         0    1.0429    5.0000
    1.0000   61.0000         0    1.0410    4.0000
    1.0000   12.0000    1.0000    1.0400    1.0000
    1.0000   32.0000    3.0000    1.0395    3.0000
    1.0000    8.0000    1.0000    1.0387    1.0000
    1.0000   33.0000         0    1.0386   11.0000
    1.0000         0    1.0000    1.0383    4.0000
    2.0000   77.0000    2.0000    1.0383    1.0000
    1.0000   47.0000    1.0000    1.0382    3.0000
    2.0000   60.0000    1.0000    1.0376   10.0000
    2.0000   77.0000    1.0000    1.0375    1.0000
    1.0000   28.0000    1.0000    1.0374    1.0000
    1.0000   69.0000    5.0000    1.0359    3.0000
    1.0000   42.0000         0    1.0358    3.0000
    1.0000    8.0000         0    1.0357    3.0000
    1.0000   63.0000    3.0000    1.0357    3.0000
    2.0000   68.0000    1.0000    1.0348    1.0000
    1.0000   51.0000         0    1.0343    3.0000
    1.0000   30.0000    1.0000    1.0341    1.0000
    1.0000   24.0000    2.0000    1.0341    3.0000
    2.0000   93.0000    5.0000    1.0340    3.0000
    1.0000   63.0000    4.0000    1.0338    3.0000
    1.0000   63.0000    2.0000    1.0337    3.0000
    1.0000   12.0000    2.0000    1.0329    1.0000
    2.0000   23.0000    1.0000    1.0325    1.0000
    1.0000   46.0000    1.0000    1.0324    2.0000
    1.0000   28.0000         0    1.0323    1.0000
    2.0000   93.0000    4.0000    1.0321    3.0000
    1.0000   58.0000    3.0000    1.0316    6.0000
    1.0000   47.0000    2.0000    1.0314    6.0000
    1.0000   48.0000         0    1.0311    4.0000
    1.0000   12.0000    3.0000    1.0310    3.0000
    1.0000   12.0000         0    1.0309    3.0000
    1.0000   48.0000    1.0000    1.0303   11.0000
    1.0000   28.0000    2.0000    1.0300    1.0000
    2.0000   60.0000    2.0000    1.0294   10.0000
    1.0000   46.0000         0    1.0293    8.0000
    1.0000   49.0000         0    1.0291    3.0000
    1.0000   24.0000    3.0000    1.0286    1.0000
    2.0000   77.0000    3.0000    1.0282    3.0000
    1.0000    8.0000    2.0000    1.0282    1.0000
    2.0000   15.0000    1.0000    1.0281    3.0000
    2.0000   68.0000    2.0000    1.0278    1.0000
    2.0000   23.0000         0    1.0273    1.0000
    1.0000  112.0000    1.0000    1.0261    7.0000
    1.0000   69.0000    4.0000    1.0258    3.0000
    2.0000   92.0000    3.0000    1.0244    3.0000
    2.0000   42.0000    1.0000    1.0244   11.0000
    1.0000   58.0000    2.0000    1.0238    3.0000
    1.0000   61.0000    1.0000    1.0234    7.0000
    1.0000   32.0000    4.0000    1.0232    3.0000
    1.0000   33.0000    2.0000    1.0231    1.0000
    1.0000   30.0000    4.0000    1.0231    3.0000
    1.0000   46.0000    2.0000    1.0227    2.0000
    1.0000   30.0000    3.0000    1.0226    3.0000
    1.0000   45.0000         0    1.0225    3.0000
    1.0000   60.0000         0    1.0225    3.0000
    2.0000   84.0000    5.0000    1.0222    3.0000
    1.0000   32.0000    1.0000    1.0221    1.0000
    1.0000   24.0000    4.0000    1.0220    1.0000
    1.0000   28.0000    3.0000    1.0219    1.0000
    1.0000   64.0000    1.0000    1.0216    4.0000
    2.0000   84.0000    1.0000    1.0215    3.0000
    1.0000   30.0000         0    1.0212    3.0000
    2.0000   77.0000    5.0000    1.0211    3.0000
    1.0000   63.0000    1.0000    1.0210    3.0000
    1.0000   33.0000    4.0000    1.0209    1.0000
    1.0000    7.0000    1.0000    1.0209    3.0000
    2.0000   35.0000         0    1.0202    3.0000

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ref: research-0 tags: clementine tlh24 Kalman thesis date: 01-06-2012 03:07 gmt revision:3 [2] [1] [0] [head]

clementine, 040207, Miguel's sorting. top 200 lags selected via bmisql.m , decent SNR on all channels but I had to z-score the state and measurement matricies.

-- standard wiener

-- linear kalman.

-- associated behavior

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ref: Dethier-2011.28 tags: BMI decoder spiking neural network Kalman date: 01-06-2012 00:20 gmt revision:1 [0] [head]

IEEE-5910570 (pdf) Spiking neural network decoder for brain-machine interfaces

  • Golden standard: kalman filter.
  • Spiking neural network got within 1% of this standard.
  • THe 'neuromorphic' approach.
  • Used Nengo, freely available neural simulator.

____References____

Dethier, J. and Gilja, V. and Nuyujukian, P. and Elassaad, S.A. and Shenoy, K.V. and Boahen, K. Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on 396 -399 (2011)

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ref: Brown-2007 tags: Kalman filter BMI Black spike_sorting Donoghue date: 01-06-2012 00:07 gmt revision:1 [0] [head]

From Uncertain Spikes to Prosthetic Control a powerpoint presentation w/ good overview of all that the Brown group has done

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ref: ODoherty-2011.1 tags: nicolelis odoherty nicolelis 2011 active tactile BMBI stimulation ICMS unscented Kalman filter date: 01-01-2012 18:27 gmt revision:3 [2] [1] [0] [head]

PMID-21976021[0] Active tactile exploration using a brain-machine-brain interface.

  • Tricky part was the temporal interleaving. 50ms stim / 50ms record.
    • No proof a priori as S1 stim could affect M1 processing.
  • Real perception, as the stimulation resulted from motor commands (through a BMI).
  • RAT = rewarded ICMS (200Hs pulses)
  • UAT = unrewarded ICMS, 400Hs, skip every 100ms.
  • NAT = no ICMS.
  • So short. damn you, nature.

____References____

[0] O'Doherty JE, Lebedev MA, Ifft PJ, Zhuang KZ, Shokur S, Bleuler H, Nicolelis MA, Active tactile exploration using a brain-machine-brain interface.Nature 479:7372, 228-31 (2011 Oct 5)

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ref: Gage-2005.06 tags: naive coadaptive control Kalman filter Kipke date: 10-03-2008 16:34 gmt revision:1 [0] [head]

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

____References____

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ref: Wood-2004.01 tags: spikes sorting BMI Black Donoghue prediction kalman date: 04-06-2007 21:57 gmt revision:2 [1] [0] [head]

PMID-17271178[0] automatic spike sorting for neural decoding

  • idea: select the number of units (and, indeed, clustering) based on the ability to predict a given variable. makes sense!
  • results:
    • human sorting: 13,5 cm^2 MSE
    • automatic spike sorting: 11.4 cm^2 MSE
      • yes, I know, the increase is totally dramatic.
  • they do not say if this could be implemented in realtime or not. hence, probably not.

____References____

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ref: bookmark-0 tags: Unscented sigma_pint kalman filter speech processing machine_learning SDRE control UKF date: 0-0-2007 0:0 revision:0 [head]

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ref: bookmark-0 tags: unscented kalman filter square-root Merwe date: 0-0-2007 0:0 revision:0 [head]

http://hardm.ath.cx/pdf/unscentedKalmanFilter.pdf -- the square root transform. contains a nice tabulation of the original algorithm, which i what I use.

http://hardm.ath.cx/pdf/unscentedKalmanFilter2000.pdf -- the original, with examples of state, parameter, and dual estimation

http://en.wikipedia.org/wiki/Kalman_filter -- wikipedia page, also has the unscented kalman filter

http://www.cs.unc.edu/~welch/kalman/media/pdf/Julier1997_SPIE_KF.pdf - Julier and Ulhmann's original paper. a bit breif.

http://www.cs.ubc.ca/~murphyk/Papers/Julier_Uhlmann_mar04.pdf -- Julier and Ulhmann's invited paper, quite excellent.

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ref: bookmark-0 tags: Bayes Baysian_networks probability probabalistic_networks Kalman ICA PCA HMM Dynamic_programming inference learning date: 0-0-2006 0:0 revision:0 [head]

http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html very, very good! many references, well explained too.

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ref: bookmark-0 tags: training neural_networks with kalman filters date: 0-0-2006 0:0 revision:0 [head]

with the extended kalman filter, from '92: http://ftp.ccs.neu.edu/pub/people/rjw/kalman-ijcnn-92.ps

with the unscented kalman filter : http://hardm.ath.cx/pdf/NNTrainingwithUnscentedKalmanFilter.pdf

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ref: Wu-2004.06 tags: Switching Kalman Filter BMU Wei Wu Donoghue date: 0-0-2006 0:0 revision:0 [head]

PMID-15188861 Modeling and Decoding Motor Cortical Activity Using a Switching Kalman Filter

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ref: bookmark-0 tags: kalman filter introduction Maybeck 1979 date: 0-0-2006 0:0 revision:0 [head]

http://www.cs.unc.edu/~welch/media/pdf/maybeck_ch1.pdf -- great explanation!! really sensible!