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[0] Schmidt EM, Single neuron recording from motor cortex as a possible source of signals for control of external devices.Ann Biomed Eng 8:4-6, 339-49 (1980)[1] Schmidt EM, McIntosh JS, Durelli L, Bak MJ, Fine control of operantly conditioned firing patterns of cortical neurons.Exp Neurol 61:2, 349-69 (1978 Sep 1)[2] Salcman M, Bak MJ, A new chronic recording intracortical microelectrode.Med Biol Eng 14:1, 42-50 (1976 Jan)

[0] Loewenstein Y, Seung HS, Operant matching is a generic outcome of synaptic plasticity based on the covariance between reward and neural activity.Proc Natl Acad Sci U S A 103:41, 15224-9 (2006 Oct 10)

[0] Sabelli HC, Mosnaim AD, Vazquez AJ, Giardina WJ, Borison RL, Pedemonte WA, Biochemical plasticity of synaptic transmission: a critical review of Dale's Principle.Biol Psychiatry 11:4, 481-524 (1976 Aug)[1] Sulzer D, Rayport S, Dale's principle and glutamate corelease from ventral midbrain dopamine neurons.Amino Acids 19:1, 45-52 (2000)[2] Burnstock G, Do some nerve cells release more than one transmitter?Neuroscience 1:4, 239-48 (1976 Aug)

[0] Ojakangas CL, Shaikhouni A, Friehs GM, Caplan AH, Serruya MD, Saleh M, Morris DS, Donoghue JP, Decoding movement intent from human premotor cortex neurons for neural prosthetic applications.J Clin Neurophysiol 23:6, 577-84 (2006 Dec)

[0] Schwartz AB, Cortical neural prosthetics.Annu Rev Neurosci 27no Issue 487-507 (2004)[1] Carmena JM, Lebedev MA, Henriquez CS, Nicolelis MA, Stable ensemble performance with single-neuron variability during reaching movements in primates.J Neurosci 25:46, 10712-6 (2005 Nov 16)

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ref: -0 tags: rutherford journal computational theory neumann complexity wolfram date: 05-05-2020 18:15 gmt revision:0 [head]

The Structures for Computation and the Mathematical Structure of Nature

  • Broad, long, historical.

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ref: -0 tags: rogers thermal oxide barrier neural implants ECoG coating accelerated lifetime test date: 12-28-2017 02:29 gmt revision:0 [head]

PMID-27791052 Ultrathin, transferred layers of thermally grown silicon dioxide as biofluid barriers for biointegrated flexible electronic systems

  • Thermal oxide proved the superior -- by far -- water barrier for encapsulation.
    • What about the edges?
  • Many of the polymer barrier layers look like inward-rectifiers:
  • Extensive simulations showing that the failure mode is from gradual dissolution of the SiO2 -> Si(OH)4.
    • Even then a 100nm layer is expected to last years.
    • Perhaps the same principle could be applied with barrier metals. Anodization or thermal oxidation to create a thick, nonporous passivation layer.
    • Should be possible with Al, Ta...

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ref: -0 tags: polyimide electrodes thermosonic bonding Stieglitz adhesion delamination date: 03-06-2017 21:58 gmt revision:7 [6] [5] [4] [3] [2] [1] [head]

IEEE-6347149 (pdf) Improved polyimide thin-film electrodes for neural implants 2012

  • Tested adhesion to Pt / SiC using accelerated aging in saline solution.
  • Targeted at retinal prostheses.
  • Layer stack:
    • 50nm SiC deposited through PECVD @ 100C using SPS, with low frequency RF modulation.
    • 100nm Pt
    • 100nm Au
    • 100nm Pt
      • These layers will alloy during cure, and hence reduce stress.
    • 30nm SiC
    • 10nm DLC (not needed, imho; PI sticks exceptionally well to clean SiC)
  • Recent studies have concluded that adhesion to PI is through carbon bindings and not through oxide formation.
    • Adhesion of polyimide to amorphous diamond-like carbon and SiC deteriorates at a minimal rate.
  • Delamination is caused by residual stress, which is not only inevetable but a major driving force for cracking in thin films.
    • Different CTE in layer stack -> different contraction when cooling from process temperature.
  • Platinum, which evaporates at 1770C, and is deposited ~100C (photoresists only withstand ~115C) results in a high-stress interface.
    • Pt - Carbon bonds only occur above 1000C
  • After 9 and 13 days of incubation the probes with 400 nm and 300nm of SiC, respectively, which were not tempered, showed complete delamination of the Pt from the SiC.
    • 60C, 0.9 M NaCl, 1 year.
    • The SiC remained attached to the PI.
      • Tempering: repeated treatment at 450C for 15 min in a N2 atmosphere.
    • All other probes remained stable.
  • Notably, used thermosonic bonding to the PI films, using sputtered (seed layer) then 12um electroplated Au.
  • Also: fully cured the base layer PI film.
  • Used oxygen plasma de-scum after patterning with resists to get better SiC adhesion to PI.
    • And better inter-layer adhesion (fully cured the first polyimide layer @ 450C).
  • Conclusion: "The fact that none of the tempered samples delaminated even after ~5 years of lifetime (extrapolated for 37 C) shows a tremendous increase in adhesion.

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ref: -0 tags: myoelectric EMG recording TMR prosthetics date: 02-13-2017 20:43 gmt revision:0 [head]

PMID: Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

  • General idea: deconvolve a grid-recorded EMG signal to infer the spinal motorneron spikes, and use this to more accurately decode user intention.
  • EMG envelope is still fairly good...

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ref: -0 tags: concentation of monoamine dopamine serotonin and norepinephrine in the brain date: 04-28-2016 19:38 gmt revision:3 [2] [1] [0] [head]

What are the concentrations of the monoamines in the brain? (Purpose: estimate the required electrochemical sensing area & efficiency)

  • Dopamine: 100 uM - 1 mM local, extracellular.
    • PMID-17709119 The Yin and Yang of dopamine release: a new perspective.
  • Serotonin (5-HT): 100 ng/g, 0.5 uM, whole brain (not extracellular!).
  • Norepinephrine / noradrenaline: 400 nm/g, 2.4 uM, again whole brain.
    • PMID-11744005 An enriched environment increases noradrenaline concentration in the mouse brain.
    • Also has whole-brain extracts for DA and 5HT, roughly:
      • 1200 ng/g DA
      • 400 ng/g NE
      • 350 ng/g 5-HT
  • So, one could imagine ~100 uM transient concentrations for all 3 monoamines.

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ref: Linsmeier-2011.01 tags: histology lund electrodes immune response fine flexible review Thelin date: 12-08-2015 23:57 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-21867803[0] Can histology solve the riddle of the nonfunctioning electrode? Factors influencing the biocompatibility of brain machine interfaces.

  • We show results from an ultrathin multichannel wire electrode that has been implanted in the rat cerebral cortex for 1 year.
    • 12um Pt-Ir wires in a 200um bundle coated with gelatin. See PMID-20551508[1]
    • Electrode was left in the rat cortex for 354 days
    • no clear GFAP staining or ED1 positive cells at the electrode tips.
  • To improve biocompatibility of implanted electrodes, we would like to suggest that free-floating, very small, flexible, and, in time, wireless electrodes would elicit a diminished cell encapsulation.
  • Suggest standardized methods for the electrode design, the electrode implantation method, and the analyses of cell reactions after implantation
  • somewhat of a review -- Stice, Biran 2005 [2] 2007 [3].
  • 50um is the recording distance Purcell 2009.
  • See also [4]
  • Study of neuronal density and ED1 reactivity / GFAP:
    • Even at 12 weeks the correlation between NeuN density and GFAP / ED1 was small -- r 2=0.12r^2 = 0.12
    • Note that DAPI labels many unknown cells in the vicinity of the electrode.

____References____

[0] Linsmeier CE, Thelin J, Danielsen N, Can histology solve the riddle of the nonfunctioning electrode? Factors influencing the biocompatibility of brain machine interfaces.Prog Brain Res 194no Issue 181-9 (2011)
[1] Lind G, Linsmeier CE, Thelin J, Schouenborg J, Gelatine-embedded electrodes--a novel biocompatible vehicle allowing implantation of highly flexible microelectrodes.J Neural Eng 7:4, 046005 (2010 Aug)
[2] Biran R, Martin DC, Tresco PA, Neuronal cell loss accompanies the brain tissue response to chronically implanted silicon microelectrode arrays.Exp Neurol 195:1, 115-26 (2005 Sep)
[3] Biran R, Martin DC, Tresco PA, The brain tissue response to implanted silicon microelectrode arrays is increased when the device is tethered to the skull.J Biomed Mater Res A 82:1, 169-78 (2007 Jul)
[4] Thelin J, Jörntell H, Psouni E, Garwicz M, Schouenborg J, Danielsen N, Linsmeier CE, Implant size and fixation mode strongly influence tissue reactions in the CNS.PLoS One 6:1, e16267 (2011 Jan 26)

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ref: -0 tags: street fighting mathematics Sanjoy Mahajan date: 10-04-2015 23:09 gmt revision:0 [head]

https://mitpress.mit.edu/sites/default/files/titles/free_download/9780262514293_Street_Fighting_Mathematics.pdf

https://mitpress.mit.edu/sites/default/files/titles/free_download/9780262526548_Art_of_Insight.pdf

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ref: -0 tags: polybenzoxazole PBO synthesis zylon date: 10-10-2014 22:40 gmt revision:0 [head]

Synthesis and thermal properties of polybenzoxazole from soluble precursors with hydroxy-substituted polyenaminonitrile

  • Process:
    1. purified/distilled reagents
    2. made the CCB, a open-ring soluble precursor
    3. eluted the CCB in water / methanol
    4. thermoset the resulting polymer.
  • No control of molecular weight, nor material properties of the cured film.
  • Resultant film was highly temperature resistant, though.

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ref: -0 tags: buszaki watson oscillations review gamma theta hippocampus cortex date: 09-30-2013 18:32 gmt revision:2 [1] [0] [head]

PMID-23393413 Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease.

  • His frequency band standards:
    • delta: 1.5 - 4Hz
    • theta: 4 - 10Hz
    • beta: 10 - 30 Hz
    • gamma: 30 - 80Hz
    • fast: 80 - 200 Hz
    • ultra fast: 200 - 600 Hz.
  • comodugram: power-power correlelogram
  • Reviews current understanding of important rhythms:
    • How gamma is preserved amongs mammals, owing to the same fundamental mechanisms (membrane time constant, GABA transmission, AMPA receptior latency) all around 25ms; suggests that this is a means of tieing neurons into meaningful groups. or symbols; (solves the binding problem?)
    • Theta rhythm, in comparison, varies between species, inversely based on the size of the hippocampus. Larger hippocampus -> greater axonal delay.
    • These and other the critical step is to break neurons into symbols (as part of a 'language' or sequenced computation), not arbitrarily long trains of spikes which are arbitrarily difficult to parse.
  • Reviews the potential role of oscillations in active sensing, though with a rather conjectorial voice: suggests that sensory systems
  • Suggests that neocortical slow-wave oscillations during sleep are critical for transferring information from the hippocampus to the cortex: the cortex become excitable at particular phases of SWS, which biases the fast ripples from the hippocampus. During wakefulness, the direction is reversed -- the hippocampus 'requests' information from the neocortex by gating gamma with theta rhythms.
  • "Typically, when oscillators of different freqencies are coupled, the network oscillation frequency is determined by the fastest one. (??)
  • I actually find figure 3 to be rather weak -- the couplings are not that obvious, espeically if this is the cherry-picked example.
  • Cross phasing-coupling, or n:m coupling: one observes m events associated with the “driven” cycle of one frequency occurring at n different times or phases in the “stimulus” cycle of the other.
    • The mechanism of cross-frequency coupling may for the backbone of neural syntax, which allows for both segmentation and linking of cell assemblies into assemblies (leters) and sequences (words). Hmm. this seems like a stretch, but I am ever cautious.
  • Brain oscillations for quantifiable phenotypes! e.g. you can mono-zygotic twins apart from di-zygotic twins.

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ref: Harris-1998.08 tags: noise wolpert harris motor planning Fitt velocity variance control theory date: 01-27-2013 22:33 gmt revision:1 [0] [head]

PMID-9723616[0] Signal-dependent noise determines motor planning.

  • We present a unifying theory of eye and arm movements based on the single physiological assumption that the neural control signals are corrupted by noise whose variance increases with the size of the control signal
    • Poisson noise? (I have not read the article -- storing here for future reference.)
  • This minimum-variance theory accurately predicts the trajectories of both saccades and arm movements and the speed-accuracy trade-off described by Fitt's law.

____References____

[0] Harris CM, Wolpert DM, Signal-dependent noise determines motor planning.Nature 394:6695, 780-4 (1998 Aug 20)

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ref: Thelin-2011.01 tags: histology MEA tether tissue response malmo lund date: 01-24-2013 22:17 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-21298109[0] Implant size and fixation mode strongly influence tissue reactions in the CNS.

  • Overview: tethering and size both increase immune response, and causes continued GFAP activity.
    • An untethered 50um electrode exhibited very weak inflammatory response after 12 weeks.
      • Suggesting that a small electrode can move with the brain.
  • Tethering in their context means affixed rigidly to the bone.
    • Small-diameter, untethered implants cause the smallest tissue reactions.
    • Likely that this scales.
  • Stice et al 2007 {1111} -- GFAP expression was significantly smaller for 12 um diameter implants than 25um implants @ 4 weeks.
  • They used 50um and 200um stainless steel implants.
    • implants glued to micromanipulator using gelatine
  • 24 rats.
  • Much more GFAP and ED1 actviity in tethered implants; NEuN neural density about the same.
  • 50um implant had a higher NeuN + count.
  • Regarding implantation: not sure. Have to find a reference for stab wounds (where the inserter is retracted).

____References____

[0] Thelin J, Jörntell H, Psouni E, Garwicz M, Schouenborg J, Danielsen N, Linsmeier CE, Implant size and fixation mode strongly influence tissue reactions in the CNS.PLoS One 6:1, e16267 (2011 Jan 26)

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ref: Biran-2007.07 tags: tresco biocompatibility tether skull electrodes Michigan probe recording Tresco date: 01-24-2013 20:11 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-17266019[0] The brain tissue response to implanted silicon microelectrode arrays is increased when the device is tethered to the skull.

  • Good, convincing, figures.

____References____

[0] Biran R, Martin DC, Tresco PA, The brain tissue response to implanted silicon microelectrode arrays is increased when the device is tethered to the skull.J Biomed Mater Res A 82:1, 169-78 (2007 Jul)

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ref: -0 tags: debian linux github persistent ssh authentication date: 07-27-2012 01:40 gmt revision:1 [0] [head]

If you don't want to repeatedly enter in your username/password for github when commiting, you'll want to enable an RSA authetication key.

-- http://www.debian.org/devel/passwordlessssh run

 ssh-keygen 
(with no options).

-- then https://help.github.com/articles/working-with-ssh-key-passphrases

 ssh-keygen -p 
with your github passphrase (I'm not totally sure this is essential).

For me, pull and push aftwerard worked without needing to supply my password. Easy!

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ref: -0 tags: hippocampus theta oscillations memory date: 03-18-2012 18:09 gmt revision:0 [head]

PMID-21696996 The hippocampus: hub of brain network communication for memory

  • Their hypothesis: memory encoding is dominated by theta oscillations 6-10 Hz; during inactivity, hippocampal neurons burst synchronously, creating sharp waves, theoretically supporting memory consolidation.
  • (They claim): to date there is no generally accepted theoretical view on memory consolidation.
  • Generally it seems to shift from hippocampus to neocortex, but still, evidence is equivocal. (Other than HM & other human evidence?)
  • Posit a theory based on excitation ramps of reverse-replay, which seems a bit fishy to me (figure 3).
  • Didn't know this: replay in visual and PFC can be so precise that it preserves detailed features of the crosscorrelograms between neurons. [58, 65, 81].

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ref: -0 tags: hippocampus theta oscillations date: 03-18-2012 17:34 gmt revision:2 [1] [0] [head]

PMID-11832222 Theta Oscillations in the Hippocampus

  • Theta-alpha oscillations have been found in 'all mammals to-date, including humans. (Hence conserved, hence possibly essential).
    • Prevalent in REM sleep.
    • Present in slices bathed in carbachol, too.
    • As well as locomotor activities; but not usually when the animal is resting.
  • Other reviews: Bland 1986, VAnderwolf 1988, Lopes da Silva et al 1990, Buzaki et al 1994 Stewart and Fox 1990, Vinogradova 1995, Vertex and Kocsis 1997.
    • Modeling reviews used passive cable properties; actually, it seems neurons, and their dendrites are have active conductances & active oscillatory features.
  • Theta oscillations most strongly present in CA1
  • Along similar lamina, oscillations are similar.
  • Osc. visible in cortical structures ...
    • subicular complex, entorhinal cortex, perirhinal cortex, cingulate cortex, amygdala -- though none of these structures are capable of generating theta oscillations intrinsically.
  • Also apparent in subcortical structures,
    • Dorsal raphe nucleus, ventral tegmental nucleus, and anterior thalamic nuclei. None of these seem required for oscillation, however:
  • Oscillations may emanate from the medial-septum-diagonal band of Broca (MS-DBB); lesion inactivates theta oscillations in all cortical areas, but the relative role is uncertain, as MS-DBB oscillations may require hippocampal and entorhinal afferents.
    • EPSPs brought about by the MS-DBB cholinergic neurons on hippocampal pyramidal cells cannot be responsible for the atrophine-sensitive form of theta.
    • That said, even though atrophine treatment only modestrly affects theta, it is reduced several-fold after selective neurotoxin elminiation of MS-DBB cholinergic cells -- maybe it's nicotinic synapses?
  • Drugs:
    • Theta can be blocked by GABA antagonist (picrotoxin, induces epilepsy) or agonist (pentoparbital anesthesia).
    • Many other drugs affect oscillations.
    • Broken down into atrophine-sensitive and atrophine-resistant oscillations.
      • (Atrophine blocks muscarinic Ach receptors).
    • Amplitude and frequency of theta does not appreciably change even after large doses of systematic muscarinic blockers.
      • Same drugs abolish theta under anesthesia.
    • The neurotransmitter and receptor causative in theta have never been clearly determined.
  • Theta in CA3 is much smaller than in CA3:
    • Distal dendritic arbor of CA3 pyramidal cells is considerably smaller than that of CA1 pyramidal neurons.
    • CA3 pyramidal neurons receive perisomatic exitation near their somata from the large mossy terminals of granule cells.
      • Regarding this, size of mossy fiber projection correlates well with spatial ability in mice, possibly causative. link (note: used the dryland radial maze, more appropriate for non-swimming mice!)
    • Intrahippocampal oscillator (CA3?) can change its frequency and phase relatively independently from the extrahippocampal (entorhinal) theta inputs.
  • CA1 interneurons discharge on the descenting phase of theta in the pyramidal cell layer, and are assumed to be responsible for the increased gamma of this phase.
  • CA1 pyramidal cells discharge on the negative phase (makes sense) of theta as recorded from the CA1 pyramidal cell layer.
    • Phase fluctuation of spikesis not random and correlates with behavioral varaibles.
      • Stronger excitation = more spikes earlier in the theta negative phase.
    • Firing of place cells varies systematically with animal position and theta phase -- there is a phase precession.
      • Seems as though place is encoded in both which cell is firing as well as when in theta.
      • alternately, this may be an effect of the CA3 oscillator running slightly faster than the extrinsic oscillator.

Original model for theta oscillation creation (figure 2):

  • Note that all oscillations require a dipole which periodically inverts along it's axis, as is required in a conductive solution.
    • And yet there is no 'null' zone in theta oscillation, as dipole would imply. Rather, there is a gradual shift, more like a traveling wave.
  • Dendrites are passive cables, LFP generated by summed activity of IPSP and EPSP on soma and dendrites.
    • Excitation from perforant path,
    • Inhibition from septum to feed-forward inhibitory neuron inputs.
  • That said, the model is not completely consistent with experimental evidence:
    • The highest probability of discharge in the behaving rat occurs around the positive peak of theta recorded at the level of the distal dendrites, corresponding to the negative phase in the pyramidal level. (Remember, spiking corresponds to sodium influx, hence decreased extracellular +)
    • Cells may oscillate by themselves, without input.
    • The cell connections within the hippocampus matter a lot, too.

LTP:

  • Induction is present / optimal when the spacing between pulses is 200ms.
    • Priming can be only one pulse!
    • Not clear how this works - endogenous cannabanoids?
  • Theta oscillation may provide a mechanism for bringing together time afferent inducing depolarization and dendritic invasion of fast spikes.

Conclusions:

  • A theta cycle may be considered an information quantum, allowing the exchange of information among the linked members in a phase-locked manner. ...
  • This discontinuous mode of operation may be a unique solution to temporally segregate and link neuronal assemblies to perform various operations.
  • Notable support of this hypothesis:
    • Theta cycle phase resets upon sensory stimulation
    • Motor activity can become theta locked.

Misc:

  • Ketamine blocks NMDA receptors.
  • Granule cells can be eliminated by neonatal X-ray exposure. (why?)

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ref: -0 tags: DBS parkinsons stem cell therapy date: 02-21-2012 20:56 gmt revision:1 [0] [head]

PMID-21875864 Dopamine cell transplantation in Parkinson's disease: challenge and perspective.

  • Long-term strategy is to graft fetal mesencelphatic tissue into the striatum (putamen) of PD patients.
  • Requires one to four donors per side.
  • Requires immune suppression for graft survival.
  • PET offers a sensitive mechanism for assessing the success of the transplant
  • Earlier review [5].
  • Full double-blind study [2].
    • significant improvement in 39 patients, but only in the younger patients (<60 years).
    • No changes in cognitive function or personality traits.
    • Looks safe at least.
  • Second study [3]
    • Full immune suppressant action, 34 PD patients.
    • No overall clinical effect, even though there was significant FDOPA uptake.
    • significant improvement in less severely affected patients out to 2 years post-surgery.
  • Imaging results not so dramatic?
  • But off-state UPDRS significant
  • 5 of the 33 patients developed graft-induced dyskinesia in addition to therepeutic effects on akinesia -- possibly due to the lack of projecting axons?
    • FDOPA uptake was higher in the dyskinetic graft recipients with dyskinesias.
  • In second trial, 13 of the 23 implanted patients exhibited dyskinesias, but there was no difference in regional or global FDOPA uptake.
    • Might be caused by inflammatory response to the grafts, as per animal studies.
    • Another study showed the worsening if dyskinesias following withdrawal of immunosuppression.
    • Might also be related to DA exposure prior the surgery, or serotonin expression within the grafts (not controlled).
      • Suggest fluorescent cell sorting.
  • Possible solution to the donor problem: human retinal pigment endothelial cells, which produce L-DOPA naturally as an neuromelanin precursor (!!).
    • Open-label study worked, but placebo-controlled did not meet clinical significance.
  • Alternate strategy is to reprogram host somatic cells (e.g. fibroblasts), limiting the need for chronic immune suppressants.
    • Trial in one young patient with PD.

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ref: -0 tags: stem cell therapy parkinsons disease DBS date: 02-21-2012 19:04 gmt revision:1 [0] [head]

PMID-15272269 Stem cell therapy for human neurodegenerative disorders-how to make it work.

  • Before clinical trials are initiated, we need to know how to control stem cell proliferation and differentiation into specific phenotypes, induce integration into existing circuits and optimize functional recovery in animal models. (from abstract)
  • It may seem untralistic, though, to induce functional recovery by replacing cells lost through disease, considering the complexity of human brain structure and function.
    • Animal models have shown at least that it is possible.
  • Intrastriatal transplantation of human fetal mesencephatic tissue have provided a proof of principle that neuronal replacement can work in humans; neurons survive, even as the patients own SN neurons die, for up to 10 years [1,2]. Seems they can become functionally integrated into the brain, and releive symptoms of akinesia [3].
  • Sham-controlled surgieries showed modest benefit, showing that the transplantation techniques are suboptimal.
  • Dyskinesias are a common side-effect in 7-15% of patients, likely due to patchy reinervation or inflammatory response to the grafted cells.
  • Unlikely that this will be a common treatment, due to unavailabiltiy of the fetal tissue.
    • Better bet: culture the cells in vitro.
  • Requirements for graft:
    • Cells should release a regulated amount of DA
    • Cells must reverse PD in animal models
    • at least 1e5 cells must survive in humans
    • grafted cells should establish a dense terminal network throughout the striatum
    • and cells should become functionally integrated into the BG.
      • Debilitating symptoms in PD and related disorders are caused by pathological canges in non-dopaminergic systems (neuroplasticity hypothesis).
      • For more complete reversal of Parkinson's symptoms, it may be necessary to stimulate regrowth of axons from grafts in the SNpc to the striatum, which would require modification of host migration markers / growth inhibitory mechanisms [33].
  • Only embryonic stem cells have been shown to work; stem cells from the adult brain don't.
    • Human ESC may have chromosomal instability.
  • Only 5-10% of cells in fetal mesencelphatic grafts are dopaminergic neurons. It is not yet known whether it is favorable to implant pure DA cells or if the grant should contain other cells, like glia, specifically atrocytes, which control cell fate [18,19].
  • Many different pathways to dopmaninergic ESC.
    • FACS = fluorescence-assisted cell sorting.
  • To date, improvements after fetal grafts have not exceeded those found with deep brain stimulation [4,6,7], and there is no convincing evidence for the reversal of drug-resistant symptoms [4]
    • Even in animals with good reinnervation improvements are only partial [27].
  • Some evidence for the generation of striatal neurons in mice after a stroke -- figure 3.
  • Implantation of mouse ESCs into rat striatum caused teratomas in 20% of the animals [36].
    • ESCs are more likely to generate tumors when implanted in the same species that they were derived from.

Stroke:

  • No notable regeneration int eh cerebral cortex.
  • Targeted apoptosis of neurons in mice, leaving tissue intact, leads to reformation of cortical neurons which extend axons into the thalamus. Therefore restricted self-repair is probably due to lack of cues to trigger neurogenesis from SC.

ALS:

  • Several promising lines of research, but much more basic science needs to be done regarding differentiation and delivery before treatment can be attempted.
  • Protecting existing neurons from degeneration seems like a better strategy.

Synthesis:

  • Much more work is required, especially the basic science of differentiation / cell survival, but it's undoubtedly worth it.

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ref: -0 tags: active filter design Netherlands Gerrit Groenewold date: 02-17-2012 20:27 gmt revision:0 [head]

IEEE-04268406 (pdf) Noise and Group Delay in Actvie Filters

  • relevant conclusion: the output noise spectrum is exactly proportinoal to the group delay.
  • Poschenrieder established a relationship between group delay and energy stored in a passive filter.
  • Fettweis proved from this that the noise generation of an active filter which is based on a passive filter is appoximately proportional to the group delay. (!!!)

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ref: -0 tags: AAV2 parkinsons gene therapy date: 02-15-2012 17:45 gmt revision:4 [3] [2] [1] [0] [head]

PMID-20970382 Gene delivery of AAV2-neurturin for Parkinson's disease: a double-blind, randomised, controlled trial.

  • Dennis Turner on the list.
  • glial-cell derived neurotrophic factor (GDNF) protects dopamine neurons in in-vitro and animal models of Parkinson's disease.
  • Neurturin is a naturally occuring structural and functional analog of GDNF that improved dopaminergic activity in aged monkeys and also protected dopamine neurons in animal models of PD.
  • Results from open-label trial have shown benefits of continuous infusion of GDNF in the putamen in advanced PD patients.
  • Gene therapy of neuturin and GDNF provides long-term histological and behavioral benefits in primate models of PD. (5,9,16,17)
    • 5: PMID-17443702 Striatal delivery of CERE-120, an AAV2 vector encoding human neurturin, enhances activity of the dopaminergic nigrostriatal system in aged monkeys.
      • CERE-120 is an AAV2 vector.
      • Unilateral injection of vector; each monkey served as its own control.
      • Stronger hydroxylase immunoreactivity on injected side.
      • Therapeutic effects?
    • 9: PMID-17192932 Delivery of neurturin by AAV2 (CERE-120)-mediated gene transfer provides structural and functional neuroprotection and neurorestoration in MPTP-treated monkeys.
      • AAV2-NTN injected 4 days after MPTP rendered the monkeys hemiparkinsonian.
      • AAV2-NTN significantly improved MPTP-induced motor impairments by 80 to 90% starting at approximately month 4 and lasting until the end of the experiment (month 10).
    • 16: PMID-11052933 Neurodegeneration prevented by lentiviral vector delivery of GDNF in primate models of Parkinson's disease.
      • treatment with lentiviral delivery of glial cell line-derived neurotrophic factor (lenti-GDNF).
      • treatment applied 1 week after MPTP.
      • Reversed motor deficits in a hand-reach task.
      • Persistent gene expression out to 8 months.
      • Results look really good.
    • 17: PMID-15673656 Continuous low-level glial cell line-derived neurotrophic factor delivery using recombinant adeno-associated viral vectors provides neuroprotection and induces behavioral recovery in a primate model of Parkinson's disease.
      • GDNF treatment at low levels (0.04 ng/mg tissue).
      • 6-OHDA model.
      • The anatomical protection was accompanied by a complete attenuation of sensorimotor neglect, head position bias, and amphetamine-induced rotation. We conclude that when delivered continuously, a low level of GDNF in the striatum (approximately threefold above baseline) is sufficient to provide optimal functional outcome.
  • Open-label 12 month phase 1 trial of AAV2-neurturin in patients with advanced PD was safe, well tolerated, and associated with therapeutic benefits (19)
  • Histology was performed for two patients. Neurturin was strongly expressed around the injection site, but tyrosene hydroxlase staining was sparse.
  • Span of trial December 2006 - November 2008.
  • 58 patients.

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ref: -0 tags: Seymour thesis electrode lithography fabrication date: 02-05-2012 17:35 gmt revision:4 [3] [2] [1] [0] [head]

Advanced polymer-based microfabricated neural probes using biologically driven designs.

  • References {1109}
  • Thermal noise from 280 um^2 or 170 um^2 gold recording sites much higher than PEDOT coated sites.
  • Used an interdigitated contact-free probe for measuring insulation impedance change. Very smart!
    • Water molecules will diffuse 15 um / minute in parylene (Yasuda, Yu et. al 2010).
    • In the frequency range critical for neural recording and stimulation, 500-5k, impedance moculus decline was small.
    • 1 hr soak at 60C.
  • Chapter 3 details 60-day soak of Parylene-C + reactive parylene insulation performance testing.
    • Regular parylene seems to work perfectly fine, no better than the PPX heat-treated devices.
    • Heat treatment does improve quality -- 200C in a vacuum oven for 2 days. (Li, Rodger et al 2005)
      • However -- this increases the brittleness.

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ref: Parush-2011.01 tags: basal ganglia reinforcement learning hypothesis frontiers israel date: 01-24-2012 04:05 gmt revision:2 [1] [0] [head]

PMID-21603228[0] Dopaminergic Balance between Reward Maximization and Policy Complexity.

  • model complexity discounting is an implicit thing.
    • the basal ganglia aim at optimization of independent gain and cost functions. Unlike previously suggested single-variable maximization processes, this multi-dimensional optimization process leads naturally to a softmax-like behavioral policy
  • In order for this to work:
    • dopamine directly affects striatal excitability and thus provides a pseudo-temperature signal that modulates the tradeoff between gain and cost.

____References____

[0] Parush N, Tishby N, Bergman H, Dopaminergic Balance between Reward Maximization and Policy Complexity.Front Syst Neurosci 5no Issue 22 (2011)

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ref: Ganguly-2009.07 tags: Ganguly Carmena 2009 stable neuroprosthetic BMI control learning kinarm date: 01-14-2012 21:07 gmt revision:4 [3] [2] [1] [0] [head]

PMID-19621062 Emergence of a stable cortical map for neuroprosthetic control.

  • Question: Are the neuronal adaptations evident in BMI control stable and stored like with skilled motor learning?
    • There is mixed evidence for stationary neuron -> behavior maps in motor cortex.
      • It remains unclear if the tuning relationship for M1 neurons are stable across time; if they are not stable, rather advanced adaptive algorithms will be required.
  • A stable representation did occur.
    • Small perturbations to the size of the neuronal ensemble or to the decoder could disrupt function.
    • Compare with {291} -- opposite result?
    • A second map could be learned after primary map was consolidated.
  • Used a Kinarm + Plexon, as usual.
    • Regressed linear decoder (Wiener filter) to shoulder and elbow angle.
  • Assessed waveform stability with PCA (+ amplitude) and ISI distribution (KS test).
  • Learning occurred over the course of 19 days; after about 8 days performance reached an asymptote.
    • Brain control trajectory to target became stereotyped over the course of training.
      • Stereotyped and curved -- they propose a balance of time to reach target and effort to enforce certain firing rate profiles.
    • Performance was good even at the beginning of a day -- hence motor maps could be recalled.
  • By analyzing neuron firing wrt idealized movement to target, the relationship between neuron & movement proved to be stable.
  • Tested to see if all neurons were required for accurate control by generating an online neuron dropping curve, in which a random # of units were omitted from the decoder.
    • Removal of 3 neurons (of 10 - 15) resulted in > 50% drop in accuracy.
  • Tried a shuffled decoder as well: this too could be learned in 3-8 days.
    • Shuffling was applied by permuting the neurons-to-lags mapping. Eg. the timecourse of the lags was not changed.
  • Also tried retraining the decoder (using manual control on a new day) -- performance dropped, then rapidly recovered when the original fixed decoder was reinstated.
    • This suggests that small but significant changes in the model weights (they do not analyze what) are sufficient for preventing an established cortical map from being transformed to a reliable control signal.
  • A fair bit of effort was put into making & correcting tuning curves, which is problematic as these are mostly determined by the decoder
    • Better idea would be to analyze the variance / noise properties wrt cursor trajectory?
  • Performance was about the same for smaller (10-15) and larger (41) unit ensembles.

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ref: neuro notes-0 tags: clementine thesis electrophysiology fit predictions tlh24 date: 01-06-2012 03:07 gmt revision:4 [3] [2] [1] [0] [head]

ok, so i fit all timestamps from clem022007001 & timarm_log_070220_173947_k.mat to clementine's behavior, and got relatively low SNR for almost everything - despite the fact that I am most likely overfitting. (bin size = 7802 x 1491) the offset is calibrated @ 2587 ms + 50 to center the juice artifact in the first bin. There are 10 lags. There are 21 sorted units.

same thing, but with only the sorted units. juice prediction is, of course, worse.

now, for file clem022007002 & timarm_log_070220_175636_k.mat. first the unsorted:

and the sorted:

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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: Schmidt-1980.01 tags: BMI 1980 SUA M1 prosthetics Schmidt MEA date: 01-04-2012 22:59 gmt revision:14 [13] [12] [11] [10] [9] [8] [head]

PMID-6794389[0] Single neuron recording from motor cortex as a possible source of signals for control of external devices

  • also [1]
  • I guess this was the first published article claiming that motorneurons could be used to drive a prosthesis, and first clear attempt at long-term array recording (?)
  • recorded via arrays for up to 37 months!
    • only 2 of the 11 eelctrodes were recording at the time of sacrifice.
  • trained the monkey to perform an 8 target tracking task
    • with cortical neurons: 2.45 bits/second
    • with wrist flexion/extension: 4.48 bits/second
  • electrodes: {946} A new chronic recording intracortical microelectrode (1976!)
    • 25um iridium wires electropolished to a 1um tip; 1.5mm long.
    • electrodes float on the cortex; signals transmitted through 25um gold wire, which is in turn connected to a head-mounted connector.
    • iridium and gold are insulated with vapor-deposited parylene-C
    • electrode tips are exposed with a HV arc. (does this dull them? from the electromicrograph, it seems that it just makes them rougher.)
    • arrays of 12.
    • 1M impedance (average)
  • interesting: neural activity was recorded from at least 8 different neurons with this electrode during the course of the implant, indicating that it was migrating through cortical tissue.
    • the average recording time from the same electrode was 8 days; max 23 days.
  • second implant was more successful: maximium time recording from the same neuron was 108 days.
  • failure is associated with cracks in the parylene insulation (which apparently occurred on the grain boundaries of the iridium). "still only marginally reliable" (and still.. and still..)
  • they have operantly trained cortical units in another, earlier study.
  • have, effectively, 8 levels of activity, with feedback monkey has to match the proscribed firing rate.
  • > 50% rewarded trials = success for them; 26/28 of the neurons tested were eventually conditioned successfully.
  • looks like the monkey can track the target firing rate rather accurately. "the output of cortical cells can provide information output rates moderately less precise than the intact motor system. "
  • Monkey can also activated sequences of neurons: A, then AB, then B.
  • people have also tried conditioning individual EMG units; it is sometimes possible to control 2 different motor units in the same muscle independently, but in general only a single channel of information can be obtained from one muscle, and gross EMGs are fine for this.
    • Thus surface EMG is preferred.
    • you can get ~ 2.73 bits/sec with gross EMG on a human; 2.99 bits/sec (max) with a monkey.
  • they remind us, of course, that an enormous amount of work remains to be done.

____References____

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ref: bookmarks-0 tags: EMG schematic amplifier prosthetic myopen date: 01-03-2012 23:08 gmt revision:3 [2] [1] [0] [head]

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ref: bookmark-0 tags: machine_learning research_blog parallel_computing bayes active_learning information_theory reinforcement_learning date: 12-31-2011 19:30 gmt revision:3 [2] [1] [0] [head]

hunch.net interesting posts:

  • debugging your brain - how to discover what you don't understand. a very intelligent viewpoint, worth rereading + the comments. look at the data, stupid
    • quote: how to represent the problem is perhaps even more important in research since human brains are not as adept as computers at shifting and using representations. Significant initial thought on how to represent a research problem is helpful. And when it’s not going well, changing representations can make a problem radically simpler.
  • automated labeling - great way to use a human 'oracle' to bootstrap us into good performance, esp. if the predictor can output a certainty value and hence ask the oracle all the 'tricky questions'.
  • The design of an optimal research environment
    • Quote: Machine learning is a victim of it’s common success. It’s hard to develop a learning algorithm which is substantially better than others. This means that anyone wanting to implement spam filtering can do so. Patents are useless here—you can’t patent an entire field (and even if you could it wouldn’t work).
  • More recently: http://hunch.net/?p=2016
    • Problem is that online course only imperfectly emulate the social environment of a college, which IMHO are useflu for cultivating diligence.
  • The unrealized potential of the research lab Quote: Muthu Muthukrishnan says “it’s the incentives”. In particular, people who invent something within a research lab have little personal incentive in seeing it’s potential realized so they fail to pursue it as vigorously as they might in a startup setting.
    • The motivation (money!) is just not there.

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ref: Dagnelie-2008.01 tags: visual BMI prosthesis review Dagnelie date: 12-17-2011 02:25 gmt revision:0 [head]

PMID-18429703 Psychophysical evaluation for visual prosthesis.

  • Visual prostheses are clinical and preclinical trials!
  • cochlear implants function with 16-20 electrodes; retina is 120e6 photoreceptors and 1.2 optic nerve fibers.
  • Argus 2 retinal implant has 60 electrodes. visual information impoverished.
  • In the heyday of prewar German scientific discovery, Foerster (3) established that electrical stimulation of the visual cortex in an awake patient during a neurosurgical intervention produced the percept of dots of light, called phosphenes, and that the location of a phosphene changed with that of the electrical stimulus.
  • people originally thought that loss of the photoreceptors would lead to degradation of the RGCs; this appears not to be true.
  • There is broad consensus that functional vision restoration is predicated on prior visual experience; this is different than cochlera prostheses, which work on congenitally deaf people.
    • Visual development depends on nearly a decade of high-resolution perception, and cannot be emulated later in life through a low-bw prosthesis.
  • There are at the present time at least 20 distinct research groups in at least 8 countries actively engaged in visual prosthesis development.
  • discuss a lot of pre-clinical testing & all the nitty-grity details, e.g. how to make a low res prosthesis work for reading.

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ref: Guenther-2009.12 tags: Guenther Kennedy 2009 neurotrophic electrode speech synthesize formant BMI date: 12-17-2011 02:12 gmt revision:2 [1] [0] [head]

PMID-20011034[0] A Wireless Brain-Machine Interface for Real-Time Speech Synthesis

  • Neurites grow into the glass electrode over the course of 3-4 months; the signals and neurons are henceforth stable, at least for the period prior publication (>4 years).
  • Used an FM modulator to send out the broadband neural signal; powered the implanted electronics inductively.
  • Sorted 56 spike clusters (!!)
    • quote: "We chose to err on the side of overestimating the number of clusters in our BMI since our Kalman filter decoding technique is somewhat robust to noisy inputs, whereas a stricter criterion for cluster definition might leave out information-carrying spike clusters."
    • 27 units on one wire and 29 on the other.
  • Quote: "neurons in the implanted region of left ventral premotor cortex represent intended speech sounds in terms of formant frequency trajectories, and projections from these neurons to primary motor cortex transform the intended formant trajectories into motor commands to the speech articulators."
    • Thus speech can be represented as a trajectory through formant space.
    • plus there are many simple low-load formant-based sw synthesizers
  • Used supervised methods (ridge regression), where the user was asked to imagine making vowel sounds mimicking what he heard.
    • only used the first 2 vowel formants; hence 2D task.
    • Supervised from 8 ~1-minute recording sessions.
  • 25 real-time feedback sessions over 5 months -- not much training time, why?
  • Video looks alright.

____References____

[0] Guenther FH, Brumberg JS, Wright EJ, Nieto-Castanon A, Tourville JA, Panko M, Law R, Siebert SA, Bartels JL, Andreasen DS, Ehirim P, Mao H, Kennedy PR, A wireless brain-machine interface for real-time speech synthesis.PLoS One 4:12, e8218 (2009 Dec 9)

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ref: Loewenstein-2006.1 tags: reinforcement learning operant conditioning neural networks theory date: 12-07-2011 03:36 gmt revision:4 [3] [2] [1] [0] [head]

PMID-17008410[0] Operant matching is a generic outcome of synaptic plasticity based on the covariance between reward and neural activity

  • The probability of choosing an alternative in a long sequence of repeated choices is proportional to the total reward derived from that alternative, a phenomenon known as Herrnstein's matching law.
  • We hypothesize that there are forms of synaptic plasticity driven by the covariance between reward and neural activity and prove mathematically that matching (alternative to reward) is a generic outcome of such plasticity
    • models for learning that are based on the covariance between reward and choice are common in economics and are used phenomologically to explain human behavior.
  • this model can be tested experimentally by making reward contingent not on the choices, but rather on the activity of neural activity.
  • Maximization is shown to be a generic outcome of synaptic plasticity driven by the sum of the covariances between reward and all past neural activities.

____References____

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ref: BuzsAki-1996.04 tags: hippocampus neocortex theta gamma consolidation sleep Buzsaki review learning memory date: 12-07-2011 02:31 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-8670641[0] The hippocampo-neocortical dialogue.

  • the entorhinal ctx is bidirectionally conneted to nearly all areas of the neocortical mantle.
  • Buzsaki correctly predicts that information gathered during exploration is played back at a faster scale during synchronous population busts during (comnsummatory) behaviors.
  • looks like a good review of the hippocampus, but don't have time to read it now.
  • excellent explanation of the anatomy (with some omissions, click through to read the caption):
  • SPW = sharp waves, 40-120ms in duration. caused by synchronous firing in much of the cortex ; occur 0.02 - 3 times/sec in daily activity & during slow wave sleep.
    • BUzsaki thinks that this may be related to memory consolidation.
  • check the cited-by articles : http://cercor.oxfordjournals.org/cgi/content/abstract/6/2/8
____References____
[0] Buzsaiki G, The hippocampo-neocortical dialogue.Cereb Cortex 6:2, 81-92 (1996 Mar-Apr)

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ref: notes-0 tags: thesis timetable contingency plan hahaha date: 12-06-2011 07:15 gmt revision:5 [4] [3] [2] [1] [0] [head]

Timetable / Plan:

  1. Get recording technology finished & assembled.
    1. Hardware
      1. Clean up prototype 2. Test in-chair with Clementine.
      2. Decide upon a good microelectrode-to-headstage connector with Gary.
      3. Fit headstage PCB into head-mounted chamber. Select battery and fit that too.
      4. Assemble one; contract Protronics to assemble 3 more.
      5. Contract Protronics to assemble 4 receiver boards.
    2. Software
      1. Headstage firmware basically complete; need to add in code for LFP measurement & transmission.
      2. Need some simple sort-client; use existing "Neurocaml" source as a basis. Alternately, use Rppl, inc's open-source "Trellis" electrophysiology suite.
      3. Integrate UDP reception into the BMI suite.
      4. Get an rugged all-in-one computer for display of BMI task - at tablet PC in a plexiglas box would be perfect.
    3. Due: June 30 2009
  2. Monkeys.
    1. Test in-cage recording with Clementine. He's a bit long in the tooth now, and does not have enough cells in M1/premotor cortices to do BMI control.
    2. Select two monkeys, train them on 2D target acquisition with a joystick using Joey's chair and setup. Make sure the monkeys can learn the 2D task in a reasonable amount of time; we don't want to waste time on dumb monkeys.
    3. Arrange for implantation surgeries this summer, depending on the availability of neurosurgeon.
    4. Work with Gary Lehew to assemble microelectrodes & head-mounted chamber.
    5. Get an ethernet drop in the vivarium for transmission of data.
    6. Due: August 30 2009
  3. Experiments
    1. Test & refine task 1 with both monkeys. Allow a maximum of 1 month to learn task 1. Neuron class (x/y/z) selected based on correlational structure (PCA of firing rate).
      1. Will have to get them to turn off Wifi (in same wireless band as the headstages) in the vivarium.
      2. Batteries will need to be replaced daily.
      3. Data will be inspected daily, to eliminate possible confounds / fix bugs / optimize the probability that the monkey learns.
      4. Expected data rate per headstage, given mean firing rate of 40Hz, full waveform storage, one LFP channel sampled at 1Khz = 3.5Gb / day. 1.5Tb drive, $120, will take 100 days to fill with data from 4 headstages.
      5. Very occasionally interleave 4-target test trials after the first week of learning, with both 'y' and 'z' neurons used to control the y-axis.
    2. Test & refine task 2 with both monkeys, in position control; here, record for a minimum of 1 month.
      1. Adjust cursor and target sizes to maintain task difficulty; measure asymptotic performance in bits/sec.
      2. Interleave randomly positioned target acquisition with stereotyped target sequences to measure neuronal tuning curves.
      3. Occasionally perturb cursor to see if there is an internal expectation of cursor motion.
    3. Switch task 2 to velocity control. Measure performance and learning effects of the switch. Train the monkey on this for at least 2 weeks, or until performance asymptotes.
    4. Shuffle the neuron class to make it non-topological, and re-train on position control in task 2 (this to test if topology matters). Train monkey for at least 3 weeks.
    5. Continue recording for as long as it seems worthwhile to do so.
    6. Due: February 1 2010
  4. Writing
    1. Write the DBS paper. This can be done in parallel with many other things, and should take about a month off and on.
    2. Keep good notes during experiments, write everything up within 1-2 months of finishing the proposed experiments.
    3. Write thesis.
    4. Due : June 2010

Contingency Plan:

  1. Recording technology does not work / cannot be made workable in a reasonable amount of time (Reasonable = 4 months.)
    1. Use Plexon, record for as long as possible (or permissible given our protocol - 4 hours) while monkey is in chair. If monkeys will not go into REM/SWS in a chair, as seems likely given what I've tried, scratch the sleep specific aim.
    2. Focus instead on making the simplified BMI work. Will have to assume that neuron identity does not change between sessions.
  2. Monkey surgery fails.
    1. Unlikely. If it does happen, we should just get another monkey. As Joey's travails in publishing his paper show, it is best to have two monkeys that learn and perform the same task.
    2. Even if the implants don't last as long as all the others, the core experiments can be completed within 2 months. Recording quality from even our worst monkey has lasted much longer than this.
  3. Monkey does not learn the BMI
    1. Focus on figuring out why the monkeys cannot learn it - start by re-implementing Dawn Taylor's kludgy autoadaptive algorithm, and go from there.
    2. Focus on sleep. Put a joystick into the cage, and train the monkey on relatively complex sequences of movement to see if there is replay.
    3. Use the experiment as a springboard to test more complicated decoding algorithms with the help of Zheng.
  4. There are no signs of replay.
    1. Try different mathematical methods of looking for replay.
    2. If still nothing, report that.

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ref: -0 tags: automatic programming synthesis loops examples date: 10-03-2011 22:28 gmt revision:1 [0] [head]

Open letter proposing some ideas on how to automate programming: simulate a human! Rather from a neuro background, and rather sketchy (as in vague, not as in the present slang usage).

images/892_1.pdf

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ref: -0 tags: the edge ideas future prediction date: 01-03-2011 19:26 gmt revision:2 [1] [0] [head]

Interesting ideas from __This Will Change Everything__

  • Daniel Dennett suggests that what is changing everything is the act of looking at what is changing everything: "When we look closely at looking closely, when we increase our investment in techniques for investing in techniques, this is what amplify uncertainties, what will change everything. We figure out how to game the system, and this initiates an arm race to control or prevent gaming of the system, which leads to new levels of gamesmanship, and so on."
    • Well said. I think this is an essential part of any creative economy.
  • The internet is humanity's growing global hindbrain: it attends itself with rote memory, managing commerce and markets, and doling out attention. This implies that eventually it will be a global forebrain
    • W. Danniel Hillis argues that it will do this through recursive hierarchical organization. But, that said, there is still no good way for making decisions with higher intelligence than each of the actors/voters. (really? are you sure this is not just an artifact of perception?)
  • Paul Saffo: "But there is one development that would fundamentally change everything: the discovert of nonhuman intelligences equal or superior to our own species. It would change everything because our crowded, quarreling species is lonely. Vastly, achingly, existentially lonely"
    • [If we do find someone/thing else:] "And despite the distance, of course we will try to talk to them. A third of us will try to conquer them, a third of us will seek to convert them, and the rest of us will try to sell them something". hah!
  • Mentioned: focus fusion technology and http://focusfusion.org/ -- looks excellent, the argument seems convincing. Why doesn't somebody throw some money at them, get it done and tested?
  • John Gottman paraphrases Peggy Sanday: "Military - or any hierarchical - social structure cannot last without external threat" Unfortunately, hierarchical structures (human and otherwise) also seem to be the best way for getting things done.

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ref: -0 tags: artificial intelligence Hutters theorem date: 08-05-2010 05:06 gmt revision:0 [head]

Hutter's Theorem: for all problems asymptotically large enough, there exists one algorithm that is within a factor of 5 as fast as the fastest algorithm for a particular problem. http://www.hutter1.net/ai/pfastprg.htm

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ref: Friston-2010.02 tags: free energy minimization life learning large theories date: 06-08-2010 13:59 gmt revision:2 [1] [0] [head]

My letter to a friend regarding images/817_1.pdf The free-energy principle: a unified brain theory? PMID-20068583 -- like all critics, i feel the world will benefit from my criticism ;-) Hey , I did read that paper on the plane, and wrote down some comments, but haven't had a chance to actually send them until now. err..anyway.. might as well send them since I did bother writing stuff down: I thought the paper was interesting, but rather specious, especially the way the author makes 'surprise' something to be minimized. This is blatantly false! Humans and other mammals (at least) like being surprised (in the normal meaning of the word). He says things like: "This is where free energy comes in: free energy is an upper bound on surprise, which means that if agents minimize free energy, they implicity minimize surprise -- a huge logical jump, and not one that I'm willing to accept. I feel like this author is trying to capitalize on some recent developments, like variational bayes and ensemble learning, without fully understanding them or having the mathematical chops (like Hayen) to flesh it out. So far as I understand, large theories (as this proposes to be) are useful in that they permit derivation of particular update equations; Variational Bayes for example takes the Kullbeck-Leibler divergence & a factorization of the posterior to create EM update equations. So, even if the free energy idea is valid, the author uses it at such a level to make no useful, mathy predictions. One area where I agree with him is that the nervous system create a model of the internal world, for the purpose of prediction. Yes, maybe this allows 'surprise' to be minimized. But animals minimize surprise not because of free energy, but rather for the much more quotidian reason that surprise can be dangerous. Finally, i wholly reject the idea that value and surprise can be equated or even similar. They seem orthogonal to me! Value is assigned to things that help an animal survive and multiply, surprise is things it's nervous system does not expect. All these things make sense when cast against the theories of evolurion and selection. Perhaps, perhaps selection is a consequence of decreasing free energy - this intuitively and somewhat amorphously/mystically makes sense (the aggregate consequence of life on earth is somehow order, harmony and other 'goodstuff' (but this is an anthropocentric view)) - but if so the author should be able to make more coherent / mathematical prediction of observed phenomena. Eg. why animals locally violate the second law of thermodynamics. Despite my critique, thanks for sending the article, made me think. Maybe you don't want to read it now and I saved you some time ;-)

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ref: work-0 tags: sine wave synthesis integrator date: 02-03-2010 05:52 gmt revision:1 [0] [head]

I learned this in college, but have forgotten all the details - Microcontroller provides an alternative to DDS

freq=|F|2πτ freq = \frac{\sqrt{|F|}}{2 \pi \tau} where τ\tau is the sampling frequency. F ranges from -0.2 to 0.

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ref: work-0 tags: software development theory date: 10-26-2009 04:29 gmt revision:2 [1] [0] [head]

http://weblog.raganwald.com/2007/06/which-theory-first-evidence.html

  • Very good article, clearly the author has hard-earned experience..
    • I appreciate his (journalistic, correctful, maybe overbearing) tone, but personally think it much better to be a bit playful with the silly arbitrariness, imperfect-but-honestly-attempted decisions, that humans are.
  • One thing that I particularly liked was the idea of 'learning area' - the more competent people that you have working on a project and learning along the way, the more area is exposed to learning, facilitating progress. Compare to the top-down approach, which allocates a few very good people at the beginning of a project to plan it out, but then does not allow the implementors to modify the plan, and furthermore suggests mediocre implementors will do - all which minimizes the 'learning area'.

also from that site - http://weblog.raganwald.com/2007/05/not-so-big-software-application.html

  • The market for lemons, or "the bad driving out the good" - linked in the blog - brilliant!
  • Quote: "Adding detail makes a design more specific, but it only makes it specific for a client if the choices expressed address the most important needs of the client."

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ref: work-0 tags: Ng computational leaning theory machine date: 10-25-2009 19:14 gmt revision:0 [head]

Andrew Ng's notes on learning theory

  • goes over the bias / variance tradeoff.
    • variance = when the model has a large testing error; large generalization error.
    • bias = the expected generalization error even if the model is fit to a very large training set.
  • proves that, with a sufficiently large training set, the training error will be the same as the fitting error.
    • also gives an upper bound on the generalization error in terms of fitting error in terms of the number of models available (discrete number)
    • this bound is only logarithmic in k, the number of hypotheses.
  • the training size m that a certain method or algorithm requires in order to achieve a certain level of performance is the algorithm's sample complexity.
  • shows that with infinite hypothesis space, the number of training examples needed is at most linear in the parameters of the model.
  • goes over the Vapnik-Chervonenkis dimension = the size of the largest set that is shattered by a hypothesis space. = VC(H)
    • A hypothesis space can shatter a set if it can realize any labeling (binary, i think) on the set of points in S. see his diagram.
    • In oder to prove that VC(H) is at least D, only need to show that there's at least one set of size d that H can shatter.
  • There are more notes in the containing directory - http://www.stanford.edu/class/cs229/notes/

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ref: work-0 tags: Cohen Singer SLIPPER machine learning hypothesis generation date: 10-25-2009 18:42 gmt revision:2 [1] [0] [head]

http://www.cs.cmu.edu/~wcohen/slipper/

  • "One disadvantage of boosting is that improvements in accuracy are often obtained at the expense of comprehensibility.
  • SLIPPER = simple learner with iterative pruning to produce error reduction.
  • Inner loop: the weak lerner splits the training data, grows a single rule using one subset of the data, and then prunes the rule using the other subset.
  • They use a confidence-rated prediction based boosting algorithm, which allows the algorithm to abstain from examples not covered by the rule.
    • the sign of h(x) - the weak learner's hyposthesis - is interpreted as the predited label and the magnitude |h(x)| is the confidence in the prediction.
  • SLIPPER only handles two-class problems now, but can be extended..
  • Is better than, though not dramatically so, than c5rules (a commercial version of Quinlan's decision tree algorithms).
  • see also the excellent overview at http://www.cs.princeton.edu/~schapire/uncompress-papers.cgi/msri.ps

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ref: -0 tags: Vanity Fair American dream control theory in politics and society date: 05-03-2009 17:11 gmt revision:3 [2] [1] [0] [head]

Rethinking the American Dream by David Kamp

  • check out the lights in the frame at the bottom, and the kid taking a picture center-right (image courtesy of Kodak, hence.)

  • (quote:) "Still, we need to challenge some of the middle-class orthodoxies that have brought us to this point—not least the notion, widely promulgated throughout popular culture, that the middle class itself is a soul-suffocating dead end."
    • Perhaps they should teach expectations management in school? Sure, middle class should never die - I hope it will grow.
  • And yet, this is still rather depressive - we all want things to continuously, exponentially get better. I actually think this is almost possible, we just need to reason carefully about how this could happen: what changes in manufacturing, consumption, energy generation, transportation, and social organization would gradually effect widespread improvement.
    • Some time in individual lives (my own included!) is squandered in pursuit of the small pleasures which would be better used for purposeful endeavor. Seems we need to resurrect the idea of sacrifice towards the future (and it seems this meme itself is increasingly popular).
  • Realistically: nothing is for free; we are probably only enjoying this more recent economic boom because energy (and i mean oil, gas, coal, hydro, nuclear etc), which drives almost everything in society, is really cheap. If we can keep it this cheap, or make it cheaper through judicious investment in new technologies (and perhaps serendipity), then our standard of living can increase. That is not to say that it will - we need to put the caloric input to the economy to good use.
    • Currently our best system for enacting a general goal of efficiency is market-based capitalism. Now, the problem is that this is an inherently unstable system: there will be cheaters e.g. people who repackage crap mortgages as safe securities, companies who put lead paint on children's toys, companies who make unsafe products - and the capitalistic system, in and of itself, is imperfect at regulating these cheaters (*). Bureaucracy may not be the most efficient use of money or people's lives, but again it seems to be the best system for regulating/auditing cheaters. Examined from a control feedback point-of-view, bureaucracy 'tries' to control axes which pure capitalism does not directly address.
    • (*) Or is it? The largest problem with using consumer (or, more generally, individual) choice as the path to audit & evaluate production is that there is a large information gradient or knowledge difference between producers and consumers. It is the great (white?) hope of the internet generation that we can reduce this gradient, democratize information, and have everyone making better choices.
      • In this way, I'm very optimistic that things will get continuously better. (But recall that optimality-seeking requires time/money/energy - it ain't going to be free, and it certainly is not going to be 'natural'. Alternately, unstable-equilibrium-maintaining (servoing! auditing!) requires energy; democracy's big trick is that it takes advantage of a normal human behavior, bitching, as the feedstock. )
  • Finally (quote:) "I’m no champion of downward mobility, but the time has come to consider the idea of simple continuity: the perpetuation of a contented, sustainable middle-class way of life, where the standard of living remains happily constant from one generation to the next. "
    • Uh, you've had this coming: stick it. You can enjoy 'simple continuity'. My life is going to get better (or at least my life is going to change and be interesting/fun), and I expect the same for everybody else that I know. See logic above, and homoiconic's optimism

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ref: Graves-2001.04 tags: sleep memory REM protein synthesis review date: 03-25-2009 15:23 gmt revision:1 [0] [head]

PMID-11250009[0] Sleep and memory: a molecular perspective.

  • inhibition of protein synthesis is most effective if it occurs at a time post-training when rapid eye movement (REM) sleep is required for memory consolidation
  • The neurochemical changes that occur across sleep/wake states, especially the cholinergic changes that occur in the hippocampus during REM sleep, might provide a mechanism by which sleep modulates specific cellular signaling pathways involved in hippocampus-dependent memory storage.
    • REM sleep could influence the consolidation of hippocampus-dependent long-term memory if it occurs during windows that are sensitive to cholinergic or serotonergic signaling.
    • PKA activation seems important to hippocampal long-term memory
    • NMDA affects PKA through Ca2+ to adenyl cyclase
    • 5-HT_1A receptor negatively coupled to adenyl cyclase (AC)
    • 5-HT concentrations go down in hippocampus during sleep ?

____References____

[0] Graves L, Pack A, Abel T, Sleep and memory: a molecular perspective.Trends Neurosci 24:4, 237-43 (2001 Apr)

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ref: Wagner-2004.01 tags: sleep insight mental restructure integration synthesis consolidation date: 03-20-2009 21:31 gmt revision:1 [0] [head]

PMID-14737168[0] Sleep Inspires Insight.

  • Subjects performed a cognitive task requiring the learning of stimulus–response sequences, in which they improved gradually by increasing response speed across task blocks. However, they could also improve abruptly after gaining insight into a hidden abstract rule underlying all sequences.
    • number reduction task - three numbers 1, 4, 9, in short sequence, with a simple comparison rule to generate a derivative number sequence; task was to determine the last number in sequence; this number was always the same as the second number.
  • This abstract rule was more likely to be learned after 8 hours of sleep as compared to 8 hours of wakefulness.
  • My thoughts: replay during sleep allows synchronous replay of cortical activity seen during the day (presumably from the hippocampus to the neocortex), replay which is critical for linking the second number with the last (response) number. This is a process of integration: merging present memories with existing memories / structure. The difference in time here is not as long as it could be .. presumably it goes back to anything in your cortex that is activated buy the hippocampal memories. In this way we build up semi-consistent integrated maps of the world. Possibly these things occur during dreams, and the weird events/thoughts/sensations are your brain trying to smooth and merge/infer things about the world.

____References____

[0] Wagner U, Gais S, Haider H, Verleger R, Born J, Sleep inspires insight.Nature 427:6972, 352-5 (2004 Jan 22)

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ref: notes-0 tags: software debian xpaint maxima math mathematica date: 07-02-2008 14:37 gmt revision:3 [2] [1] [0] [head]

oldies but goodies:

  • Maxima a computer algebra system, almost like a free version of Mathematica!
    • be sure to install maxima-emacs to get LaTeX prettyprinting.
  • [xpaint] Has a cool spring-mass-friction system where the length of the spring (the distance between cursor and paint brush) controls the width of the paint brush. see below!

Both are in Debian of course :)

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ref: Walker-2005.12 tags: algae transfection transformation protein synthesis bioreactor date: 03-21-2008 17:22 gmt revision:1 [0] [head]

Microalgae as bioreactors PMID-16136314

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ref: notes-0 tags: CRC32 ethernet blackfin date: 10-10-2007 03:57 gmt revision:1 [0] [head]

good explanation of 32-bit CRC (from the blackfin BF537 hardware ref):

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ref: notes-0 tags: RF telemetry differential phase shift key prosthesis power transmission TETS PSK date: 05-12-2007 23:13 gmt revision:0 [head]

transcutaneous data telemetry system tolerant to power telemetry interference

  • details optimum operation of class-E amplifier.
  • plus 1-2 MBaud data link, dual band to minimize interference.

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ref: Sabelli-1976.08 tags: anatomy of the spinal cord interneurons pyramidal tract commissure reflexes date: 04-23-2007 05:12 gmt revision:1 [0] [head]

Anatomy of the spinal cord

  • wow! detailed!!
  • the spinal cord is remarkably complex (of course, considering how old it is and how important it is for structuring movement and locomotion..well..most animals)
  • there is a lot of well-organized circuitry in the spinal cord mediating adaptive phenomena and reflexes like the clasp knife reflex (upper motoneuron disease where the resistance to flexion abruptly melts away when the joint is fully flexed)

____References____

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ref: Ojakangas-2006.12 tags: BMI Donoghue prosthetic DBS prefrontal cortex planning date: 04-09-2007 22:32 gmt revision:3 [2] [1] [0] [head]

PMID-17143147[0] Decoding movement intent from human premotor cortex neurons for neural prosthetic applications

  • they suggest using additional frontal areas beyond M1 to provide signal sources for human neuromotor prosthesis.
    • did recording in prefrontal cortex during DBS surgeries.
    • these neurons were able to provide information about movement planning production, and decision-making.
  • unusual for BMI studies, their significance levels are near 0.02 - they show distros of % correct based on a ML decoding scheme.

____References____

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ref: Schwartz-2004.01 tags: Schwartz BMI prosthetics M1 review 2004 date: 04-05-2007 16:12 gmt revision:1 [0] [head]

PMID-15217341[0] Cortical Neuro Prosthetics

  • closed-loop control improves performance. see [1]
    • adaptive learning tech, when coupled to the adaptability of the cortex, suggests that these devices can function as control signals for motor prostheses.

____References____

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ref: math-0 tags: partial least squares PLS regression thesis italy date: 03-26-2007 16:48 gmt revision:2 [1] [0] [head]

http://www.fedoa.unina.it/593/

  • pdf does not seem to open in linux? no, doesn't open on windows either - the Pdf is screwed up!
  • here is a published version of his work.

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ref: Dzirasa-2006.1 tags: DAT-KO Kafui Nicolelis sleep wake dopamine tyrosine synthesis date: 03-12-2007 01:50 gmt revision:1 [0] [head]

PMID-17035544 Dopaminergic control of sleep-wake states.

  • dopmergic activity is high in REM sleep!! perhaps this is involved in learning?
  • they have a good description of the DAT-KO model, and why it is good for both exessive levels of synaptic dopamine as well as depressed/parkinsonian levels...
  • also at http://hardm.ath.cx:88/pdf/Kafui2006.pdf

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ref: bookmark-0 tags: book information_theory machine_learning bayes probability neural_networks mackay date: 0-0-2007 0:0 revision:0 [head]

http://www.inference.phy.cam.ac.uk/mackay/itila/book.html -- free! (but i liked the book, so I bought it :)

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ref: notes-0 tags: thesis thoughts date: 0-0-2007 0:0 revision:0 [head]

properties of the brain: 1 .cerebellum in a supervised learner, I agree with the evidence: it learns to predict future outcomes given present states very efficiently. Appears to have a structure that is conducive for learning spatio-temporal structures, with the parallel fibers and purkinje cells. climbing fibers fire on error and cause LTP. Purkinje cells have inhibitor output -> hence error to LTP to less inhibition = movement in the positive direction. Mossy fibers have collaterals to DCN neurons and purkinje cells, i think.

this whole structure seems rather strange to me - why the multiple levels of inversion? it is the same as the basal ganglia - striatal output is inhibitory upon the globus pallidus, globus pallidus output is inhibitory on the thalamus. {and, at least in the monkey though probably also in the human, the thalamus is very large and very well organized}. actually, the whole brain seems exceedingly well organized, the problem is that we don't really understand this organization quite yet. E.G the putamen seems to have a somatotopic organization & has units which fire according to motion in the distal joints. (those old papers are great!) . caudate seems to have some sort of cognitive role?

blaaa. so, what does the brain do? it learns to live, more or less; it is adaptive. humans seem to be thte most adaptive; we stay in the adaptive phase for the longest part of our life, whereas rhesus seem to grow up rather quickly. learning! as kawato's student explains, learning modifying a function to minimize (or maximize) some evaluative function. In the case the fitness function is some function of the match between desired output and training output, that learning is supervised. We have neural networks to do this, and undoubtably the human mind can do this too. In the case the fitness function is some weighted-sum of a scalar reward, then you have reinforcement learning. Generally, the animal will learn the value of certain states, actions, or state-action pairs, and has to choose which is the best based on either the direct perceived value or the integrated expected future value. Humans think in this way all the time, and use a high-level model of the world, learned basically by example, trial and error, and even book-learning, to 'do the integral' and evaluate which of several paths are best. Once we 'decide', things then become habits. We, and especially monkeys, are exceptionally subject to choosing arbitrarily when the reward is unknown - we explore all of our lives, in order to expand the quality of our models of the world, and improve the reward-evaluation of states and actions. Is this dichotomy between models and evaluations artificial? Is there any reason to believe that they are represented in separate structures/pathways/molecules in the brain? perhaps. take dopamine for example. blocking its reuptake via cocaine is very rewarding, and induces a habit in mammals that are administered the drug. but perhaps it it not so much involved in reward so much as desire. {drug addicts who have their DA1 receptor blocked end up taking /more/ drugs, apparently in the desire to feel something}. DA depletion in parkinsons makes the stick larger in carrot-stick learning: these patients learn worse with reward than controls. {hence, error must not require DA}. _{system function is hard to intuit from such nonspecific effectors like drugs because the system is adaptive; i actually think leasions are better, or at least seem better, due to the precise organization fo the brain.

anyway, learning. "the controller learns the inverse model of it's own reflexes" - this is brilliant. only through hebbian learning! I like this a lot. In general, i agree with Kawato (actually, so far everything he has put out seems to be high-quality, well thought out and easy to understand) - the proof is incontrovertible that there are inverse models in the brain, probably at least in the cerebellum.

todo: review what is required to make an inverse model.

ok time to put the monkey away.

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ref: notes-0 tags: architecture aesthetic light date: 0-0-2006 0:0 revision:0 [head]

http://www.thomaslockehobbs.com/ -- interetsing photoblog of a globetrotter & laconic harvard intellectual

http://www.uni-weimar.de/architektur/InfAR/lehre/Entwurf/Patterns/107/ca_107.html Modern buildings are often shaped with no concern for natural light - they depend almost antirely on artificial light. But buildings which displace natural light as the major source of illumination are not fit places to spend the day.

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ref: abstract-0 tags: tlh24 error signals in the cortex and basal ganglia reinforcement_learning gradient_descent motor_learning date: 0-0-2006 0:0 revision:0 [head]

Title: Error signals in the cortex and basal ganglia.

Abstract: Numerous studies have found correlations between measures of neural activity, from single unit recordings to aggregate measures such as EEG, to motor behavior. Two general themes have emerged from this research: neurons are generally broadly tuned and are often arrayed in spatial maps. It is hypothesized that these are two features of a larger hierarchal structure of spatial and temporal transforms that allow mappings to procure complex behaviors from abstract goals, or similarly, complex sensory information to produce simple percepts. Much theoretical work has proved the suitability of this organization to both generate behavior and extract relevant information from the world. It is generally agreed that most transforms enacted by the cortex and basal ganglia are learned rather than genetically encoded. Therefore, it is the characterization of the learning process that describes the computational nature of the brain; the descriptions of the basis functions themselves are more descriptive of the brain’s environment. Here we hypothesize that learning in the mammalian brain is a stochastic maximization of reward and transform predictability, and a minimization of transform complexity and latency. It is probable that the optimizations employed in learning include both components of gradient descent and competitive elimination, which are two large classes of algorithms explored extensively in the field of machine learning. The former method requires the existence of a vectoral error signal, while the latter is less restrictive, and requires at least a scalar evaluator. We will look for the existence of candidate error or evaluator signals in the cortex and basal ganglia during force-field learning where the motor error is task-relevant and explicitly provided to the subject. By simultaneously recording large populations of neurons from multiple brain areas we can probe the existence of error or evaluator signals by measuring the stochastic relationship and predictive ability of neural activity to the provided error signal. From this data we will also be able to track dependence of neural tuning trajectory on trial-by-trial success; if the cortex operates under minimization principles, then tuning change will have a temporal relationship to reward. The overarching goal of this research is to look for one aspect of motor learning – the error signal – with the hope of using this data to better understand the normal function of the cortex and basal ganglia, and how this normal function is related to the symptoms caused by disease and lesions of the brain.