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[0] Fu QG, Flament D, Coltz JD, Ebner TJ, Temporal encoding of movement kinematics in the discharge of primate primary motor and premotor neurons.J Neurophysiol 73:2, 836-54 (1995 Feb)

[0] Li CS, Padoa-Schioppa C, Bizzi E, Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field.Neuron 30:2, 593-607 (2001 May)[1] Caminiti R, Johnson PB, Urbano A, Making arm movements within different parts of space: dynamic aspects in the primate motor cortex.J Neurosci 10:7, 2039-58 (1990 Jul)

[0] Kalaska JF, Cohen DA, Hyde ML, Prud'homme M, A comparison of movement direction-related versus load direction-related activity in primate motor cortex, using a two-dimensional reaching task.J Neurosci 9:6, 2080-102 (1989 Jun)

[0] Wetts R, Kalaska JF, Smith AM, Cerebellar nuclear cell activity during antagonist cocontraction and reciprocal inhibition of forearm muscles.J Neurophysiol 54:2, 231-44 (1985 Aug)

[0] Sergio LE, Kalaska JF, Systematic changes in motor cortex cell activity with arm posture during directional isometric force generation.J Neurophysiol 89:1, 212-28 (2003 Jan)

[0] Taira M, Boline J, Smyrnis N, Georgopoulos AP, Ashe J, On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional static isometric force.Exp Brain Res 109:3, 367-76 (1996 Jun)

[0] Ashe J, Force and the motor cortex.Behav Brain Res 87:2, 255-69 (1997 Sep)

[0] Amirikian B, Georgopoulos AP, Directional tuning profiles of motor cortical cells.Neurosci Res 36:1, 73-9 (2000 Jan)

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ref: -2018 tags: biologically inspired deep learning feedback alignment direct difference target propagation date: 03-15-2019 05:51 gmt revision:5 [4] [3] [2] [1] [0] [head]

Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures

  • Sergey Bartunov, Adam Santoro, Blake A. Richards, Luke Marris, Geoffrey E. Hinton, Timothy Lillicrap
  • As is known, many algorithms work well on MNIST, but fail on more complicated tasks, like CIFAR and ImageNet.
  • In their experiments, backprop still fares better than any of the biologically inspired / biologically plausible learning rules. This includes:
    • Feedback alignment {1432} {1423}
    • Vanilla target propagation
      • Problem: with convergent networks, layer inverses (top-down) will map all items of the same class to one target vector in each layer, which is very limiting.
      • Hence this algorithm was not directly investigated.
    • Difference target propagation (2015)
      • Uses the per-layer target as h^ l=g(h^ l+1;λ l+1)+[h lg(h l+1;λ l+1)]\hat{h}_l = g(\hat{h}_{l+1}; \lambda_{l+1}) + [h_l - g(h_{l+1};\lambda_{l+1})]
      • Or: h^ l=h l+g(h^ l+1;λ l+1)g(h l+1;λ l+1)\hat{h}_l = h_l + g(\hat{h}_{l+1}; \lambda_{l+1}) - g(h_{l+1};\lambda_{l+1}) where λ l\lambda_{l} are the parameters for the inverse model; g()g() is the sum and nonlinearity.
      • That is, the target is modified ala delta rule by the difference between inverse-propagated higher layer target and inverse-propagated higher level activity.
        • Why? h lh_{l} should approach h^ l\hat{h}_{l} as h l+1h_{l+1} approaches h^ l+1\hat{h}_{l+1} .
        • Otherwise, the parameters in lower layers continue to be updated even when low loss is reached in the upper layers. (from original paper).
      • The last to penultimate layer weights is trained via backprop to prevent template impoverishment as noted above.
    • Simplified difference target propagation
      • The substitute a biologically plausible learning rule for the penultimate layer,
      • h^ L1=h L1+g(h^ L;λ L)g(h L;λ L)\hat{h}_{L-1} = h_{L-1} + g(\hat{h}_L;\lambda_L) - g(h_L;\lambda_L) where there are LL layers.
      • It's the same rule as the other layers.
      • Hence subject to impoverishment problem with low-entropy labels.
    • Auxiliary output simplified difference target propagation
      • Add a vector zz to the last layer activation, which carries information about the input vector.
      • zz is just a set of random features from the activation h L1h_{L-1} .
  • Used both fully connected and locally-connected (e.g. convolution without weight sharing) MLP.
  • It's not so great:
  • Target propagation seems like a weak learner, worse than feedback alignment; not only is the feedback limited, but it does not take advantage of the statistics of the input.
    • Hence, some of these schemes may work better when combined with unsupervised learning rules.
    • Still, in the original paper they use difference-target propagation with autoencoders, and get reasonable stroke features..
  • Their general result that networks and learning rules need to be tested on more difficult tasks rings true, and might well be the main point of this otherwise meh paper.

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ref: -0 tags: direct electrical stimulation neural mapping review date: 01-26-2017 02:28 gmt revision:0 [head]

PMID-22127300 Direct electrical stimulation of human cortex -- the gold standard for mapping brain functions?

  • Fairly straightforward review, shows the strengths and weaknesses / caveats of cortical surface stimulation.
  • Axon initial segment and nodes of Ranvier (which has a high concentration of Na channels) are the most excitable.
  • Stimulation of a site in the LGN of the thalamus increased the BOLD signal in the regions of V1 that received input from that site, but strongly suppressed it in the retinotopicaly matched regions of extrastriate cortex.
  • To test the hypothesis that the deactivation of extrastriate cortex might be due to synaptic inhibition of V1 projection neurons, GABA antagonists were microinjected into V1 in monkeys in experiments that combined fMRI, ephys, and microstim.
    • Ref 25. PMID-20818384
    • These findings suggest that the stimulation of cortical neurons disrupts the propagation of cortico-cortico signals after the first synapse.
    • Likely due to feedforward and recurrent inhibition.
  • Revisit the hypothesis of tight control of excitation and inhibition (e.g. in-vivo patch clamping + drugs). "The interactions between excitation and inhibition within cortical microcircuits as well as between inter-regional connections haper the predicability of stimulation."
  • The average size of a fMRI voxel:
    • 55ul, 55mm^2
    • 5.5e6 neurons,
    • 22 - 55e9 billion synapses,
    • 22km dendrites (??)
    • 220km axons.
  • In the 1970s, Daniel Pollen conducted a series of studies stimulating the visual cortex of cats and humans.
    • Observed long intra-stim responses, and post-stim afterdischarges.
    • Importantly, he also observed inhibitory effects of DES on cortical responses at the stimulation site.
      • The inhibitory effect depended on the state of the neuron before stimulation.
      • High spontaneous activity + low stim strengths = inhibition;
      • low spontaneous activity + high stim strengths = excitation.
  • In the author's opinion, there is an equal or greater number of inhibitory responses to electrical microstimulation as excitatory. Only, there is a reporting bias toward the positive.
  • Many locations for paresthesias:
    • postcentral sulcus (duh)
    • opercular area inferior postcentral gyrus (e.g. superior to and facing the temporal lobe)[60]
    • posterior cingulate gyrus
    • supramarginal gyrus
    • temporal lobe, limbic and isocortical structures.

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ref: -0 tags: perl directory descent script remove date: 01-10-2014 06:12 gmt revision:0 [head]

Simple perl scrip for removing duplicate files within sub-directories of a known depth:

#!/usr/bin/perl -w

@files = <*>;
foreach $file (@files) {
	@files2 = <$file/*>;
	foreach $file2 (@files2) {
		print $file2 . "\n";
		`rm -rf $file2/*_1.jpg`; 
		`rm -rf $file2/*_2.jpg`; 
	}
}

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ref: Ganguly-2011.05 tags: Carmena 2011 reversible cortical networks learning indirect BMI date: 01-23-2013 18:54 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-21499255[0] Reversible large-scale modification of cortical networks during neuroprosthetic control.

  • Split the group of recorded motor neurons into direct (decoded and controls the BMI) and indirect (passive) neurons.
  • Both groups showed changes in neuronal tuning / PD.
    • More PD. Is there no better metric?
  • Monkeys performed manual control before (MC1) and after (MC2) BMI training.
    • The majority of neurons reverted back to original tuning after BC; c.f. [1]
  • Monkeys were trained to rapidly switch between manual and brain control; still showed substantial changes in PD.
  • 'Near' (on same electrode as direct neurons) and 'far' neurons (different electrode) showed similar changes in PD.
    • Modulation Depth in indirect neurons was less in BC than manual control.
  • Prove (pretty well) that motor cortex neuronal spiking can be dissociated from movement.
  • Indirect neurons showed decreased modulation depth (MD) -> perhaps this is to decrease interference with direct neurons.
  • Quote "Studies of operant conditioning of single neurons found that conconditioned adjacent neurons were largely correlated with the conditioned neurons".
    • Well, also: Fetz and Baker showed that you can condition neurons recorded on the same electrode to covary or inversely vary.
  • Contrast with studies of motor learning in different force fields, where there is a dramatic memory trace.
    • Possibly this is from proprioception activating the cerebellum?

Other notes:

  • Scale bars on the waveforms are incorrect for figure 1.
  • Same monkeys as [2]

____References____

[0] Ganguly K, Dimitrov DF, Wallis JD, Carmena JM, Reversible large-scale modification of cortical networks during neuroprosthetic control.Nat Neurosci 14:5, 662-7 (2011 May)
[1] Gandolfo F, Li C, Benda BJ, Schioppa CP, Bizzi E, Cortical correlates of learning in monkeys adapting to a new dynamical environment.Proc Natl Acad Sci U S A 97:5, 2259-63 (2000 Feb 29)
[2] Ganguly K, Carmena JM, Emergence of a stable cortical map for neuroprosthetic control.PLoS Biol 7:7, e1000153 (2009 Jul)

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ref: Rouse-2011.06 tags: BMI chronic DBS bidirectional stimulator Washington Medtronic ASIC translational date: 03-05-2012 23:56 gmt revision:3 [2] [1] [0] [head]

PMID-21543839[0] A chronic generalized bi-directional brain-machine interface.

  • Using a commercial neurostimulator package & battery etc.
  • "A key goal of this research prototype is to help broaden the clinical scope and acceptance of NI techniques, particularly real-time brain state detection" Good purpose! good work!
  • Augments the stimulator with 4 channels of ECoG/LFP + accelerometer + wireless telemetry.
    • Can be used to detect parkinsons state or pre-epileptiform behavior.
      • Much of this has been though of before, it just took the technology to catch up & a group to make it.
    • Chronic data is needed from humans -- animal models are often inadequate.
  • Tested in a primate for brain control of a cursor: 1D control using ECoG.
    • Good Left/right ROC, actually.
    • A large cost is simply the clinical testing; hence they piggy-back on an existing design.
    • There should be more research-industry collaborations like this.
  • impressive specs.
  • SVM classification algorithm (only consumed 10uW!) for data compression.
  • short-time Fourier transform for extracting the power over a given band. This using a modified chopper-amplification scheme. Output data has a bandwidth of less than 5Hz, which greatly reduces processing requirements.
  • Lots of processing on the BASIC chip, much like here.
  • Also see the press release

____References____

[0] Rouse AG, Stanslaski SR, Cong P, Jensen RM, Afshar P, Ullestad D, Gupta R, Molnar GF, Moran DW, Denison TJ, A chronic generalized bi-directional brain-machine interface.J Neural Eng 8:3, 036018 (2011 Jun)

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ref: Fu-1995.02 tags: M1 motor tuning kinematics dynamic direction date: 01-03-2012 02:21 gmt revision:1 [0] [head]

PMID-7760138[0] Temporal encoding of movement kinematics in the discharge of primate primary motor and premotor neurons

  • 48 target 2D center out task
  • wanted to disambiguate temporal aspects of tuning vs. parallel (e.g. across a neuronal population) aspects of tuning.
  • On average we found a clear temporal segregation and ordering in the onset of the parameter-related partial R2 values: direction-related discharge occurred first (115 ms before movement onset), followed sequentially by target position (57 ms after movement onset) and movement distance (248 ms after movement onset).
  • therefore, the motor cortex seems to have strong temporal processing aspects. duh.
    • Probably explained by Todorov ...

____References____

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ref: work-0 tags: fur openGL directX shell hull algorithm date: 11-03-2010 15:47 gmt revision:0 [head]

http://www.xbdev.net/directx3dx/specialX/Fur/index.php -- for future reference. Simple algorithm that seems to work quite well. Can be done almost entirely in vertex shader...

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ref: Li-2001.05 tags: Bizzi motor learning force field MIT M1 plasticity memory direction tuning transform date: 09-24-2008 22:49 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-11395017[0] Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field

  • this is concerned with memory cells, cells that 'remember' or remain permanently changed after learning the force-field.
  • In the above figure, the blue lines (or rather vertices of the blue lines) indicate the firing rate during the movement period (and 200ms before); angular position indicates the target of the movement. The force-field in this case was a curl field where force was proportional to velocity.
  • Preferred direction of the motor cortical units changed when the preferred driection of the EMGs changed
  • evidence of encoding of an internal model in the changes in tuning properties of the cells.
    • this can suppor both online performance and motor learning.
    • but what mechanisms allow the motor cortex to change in this way???
  • also see [1]

____References____

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ref: notes-0 tags: leadership dilbert redirection politics trolls date: 09-28-2007 18:11 gmt revision:0 [head]

a very nice synopsis of how leadership works:

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ref: Kalaska-1989.06 tags: motor control direction tuning force Kalaska date: 04-09-2007 19:59 gmt revision:2 [1] [0] [head]

PMID-2723767[0] A comparison of movement direction-related versus load direction-related activity in primate motor cortex, using a two-dimensional reaching task.

  • comparison to georoplous task:
    • "We demonstrate here that many of these cells show similar large continuously graded changes in discharge when the monkey compensates for inertial loads which pull the arm in 8 different directions"
  • the mean activity of the sample population under any condition of movement direction and load direction can be described reasonably well by a simple linear summation of the movement-related discharge without any loads, and the change in tonic activity of the population caused by the load, measured prior to movement
  • their data support the dual kinematics/dynamics encoding in the motor cortex.
    • but, to me, the data also supports direct control of the muscles.

____References____

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ref: Wetts-1985.08 tags: Kalaska isometric motor control dentate cerebellum purkinje M1 pyramidal tract direction tuning date: 04-09-2007 19:54 gmt revision:0 [head]

PMID-3928831[0] Cerebellar nuclear cell activity during antagonist cocontraction and reciprocal inhibition of forearm muscles. by kalaska concering the interpositus dentate & isometric task.

  • the dentate nucleus sends afferents to the premotor areas. GABAergic inhibition from purkinje cells.
  • not so much tuning in the dentate nucleus as M1, but positive correlation was found.
  • Purkinje cells had a general low-order negative tuning to muscle activations.

____References____

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ref: Sergio-2003.01 tags: M1 isometric force posture direction SUA Kalaska date: 04-09-2007 15:22 gmt revision:1 [0] [head]

PMID-12522173[0] Systematic changes in motor cortex cell activity with arm posture during directional isometric force generation.

  • isometric joystick was positioned at 5-9 different locations in a plane in the monkey's workspace.
  • discharge of all cells varied with position and force.
    • Cell directional tuning tended to shift systematically with hand location even though the direction of static force output at the hand remained constant
      • would this be true if the forces were directed in muscle coordinates?
  • "provides further evidence that MI contributes to the transformation between extrinsic and intrinsic representations of motor output during isometric force production."

____References____

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ref: Taira-1996.06 tags: 3D Georgopoulos SUA M1 force motor control direction tuning date: 04-09-2007 15:16 gmt revision:1 [0] [head]

PMID-8817266[0] On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional static isometric force.

  • 3D isometric joystick.
  • stepwise multiple linear regression.
  • direction of force is a signal especially prominent in the motor cortex.
    • the pure directional effect was 1.8 times more prevalent in the cells than in the muscles studied (!)

____References____

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ref: Ashe-1997.09 tags: motor control force direction magnitude M1 cortex date: 04-09-2007 01:10 gmt revision:0 [head]

PMID-9331494[0] Force and the motor cortex.

  • most M1 cells seem to be related to the direction of static force; fewer related to direction and magnitude; fewer yet to only magnitude.
  • dynamic forces: there is a stron correlation between the rate of change of force and the motor cortex firing
    • dynamic force seems to determine firing rate moreso than static force (e.g. resisting gravity)
    • I have definantly seen evidence of this with the kinarm experiments.

____References____

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ref: Amirikian-2000.01 tags: Georgopulos directional tuning motor cortex SUA electrophysiology date: 04-05-2007 16:34 gmt revision:2 [1] [0] [head]

PMID-10678534[0] Directional tuning profiles of motor cortical cells

  • trained the monkeys to move to 20 targets in a horizontal plane
    • the larger number of targets allowed a more accurate estimation of the tuning properties of the cells
    • measured tuning based on the spike count during movement.
  • typical r^2 = 0.7 for a modified cosine fit

____References____