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[0] Wahnoun R, Helms Tillery S, He J, Neuron selection and visual training for population vector based cortical control.Conf Proc IEEE Eng Med Biol Soc 6no Issue 4607-10 (2004)[1] Wahnoun R, He J, Helms Tillery SI, Selection and parameterization of cortical neurons for neuroprosthetic control.J Neural Eng 3:2, 162-71 (2006 Jun)[2] Fetz EE, Operant conditioning of cortical unit activity.Science 163:870, 955-8 (1969 Feb 28)[3] Fetz EE, Finocchio DV, Operant conditioning of specific patterns of neural and muscular activity.Science 174:7, 431-5 (1971 Oct 22)[4] Fetz EE, Finocchio DV, Operant conditioning of isolated activity in specific muscles and precentral cells.Brain Res 40:1, 19-23 (1972 May 12)[5] Fetz EE, Baker MA, Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles.J Neurophysiol 36:2, 179-204 (1973 Mar)[6] Humphrey DR, Schmidt EM, Thompson WD, Predicting measures of motor performance from multiple cortical spike trains.Science 170:959, 758-62 (1970 Nov 13)

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ref: Prescott-2006.01 tags: basal_ganglia action selection motor control robot date: 03-01-2012 17:56 gmt revision:4 [3] [2] [1] [0] [head]

PMID-16153803[0] The robot basal ganglia: action selection by an embedded model of the basal ganglia

  • they implemented a model of the basal ganglia in a robot. The model switches between competing (hypothetical) actions based on input salience. There are only a possible actions in their robot.
  • they reiterate the common conception that the basal ganglia are implicated in action selection: what to do next ( also mentioned are other functions - perception and cognition working memory and many other aspects of motor function. )
  • huh, interesting : cognitive psychologists have discovered that when an observable system has more than three interacting parts, it becomes very difficult for human minds to predict accurately how that system will change over time. (!!!) I dig disclaimers like this.
    • therefore, very limited understanding can be gleaned from informal, box and arrow style models.
      • I think the same is true of many biological analysis - including analysis of the immune and nervous systems - it needs to be at a much higher level of quantification
    • they also say that a model must be validated by placing it within the entire behavioral system.
  • the basal ganglia seem to be suitable for switching between competing channels & providing the required clean selection of a winner.
    • (1) striatal cells have up and down states, and can only switch between them with heavy coincident inputs.
    • (2) selective local inhibition between channels.
    • (3) dopamine innervation D1 = exitation; D2 = inhibition. I never really got how this enters their model; figure 1 seems like it would describe it, but it needs more math :)
    • (4) feedforward off-center, on surround network. they ref some other work..
      • I still don't feel like their explanation is the best (they use kinda wishy-washy terms) - though it is a step in the right direction.
  • people with schizophrenia sometimes switch cognitive focus rapidly; schizo is though to be due to a dopamine imbalance. Same problem with ADD.
    • treatment for ADD: amphetamine (blocks monoamine transporter, increases extracellular concentration of DA), ritalin. Both allow for heightened concentration: once you select a task, you stick with 'it' (the thought / prediction pathway) for longer. Dopamine is definintely involved in action selection, duhh.
    • their model supports this behavior: If the tonic dopamine level is very low, the robot has difficulty initiating actions; if the DA level is high, then it tends to select more than one action at the same time. (wait.. this implies that DA is too high in people with ADD? what? perhaps this is a consequence of the two different types of DA receptors? )
  • (...) basal ganglia - thalamo-cotrical loops my act to provide a positive feedback pathway that can maintain appropriate level of salience to selected behavior.
  • much of the input to the basal ganglia comprises collateral fibers from motor regions that project to the spinal cord and brainstem structures.
    • activity changes in the BG occur slightly after the beginning of EMG activity (good evidence!) Such signals may be important for controlling the maintenance and termination of selected behavior.

My thoughts:

  • what if the STN is involved in controlling the stability of neuronal activity - that is, preventing motor feedback instability by knocking down the gain. (whereas the cerebellum is involved in the balance and coordination interpretations of stability)
    • Normally, the human motor system is very stable, but when you lack dopamine innervation, you both cannot move (become very rigid) & have tremor (an inability to control cyclical oscillations).
      • That is, perhaps oscillation is due to a intrinsic inability to modulate gain.
      • more likely it is a manifestation/symptom of pathological activity in the control loop.


[0] Prescott TJ, Montes González FM, Gurney K, Humphries MD, Redgrave P, A robot model of the basal ganglia: behavior and intrinsic processing.Neural Netw 19:1, 31-61 (2006 Jan)

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ref: work-0 tags: emg_dsp design part selection stage6 date: 09-22-2010 20:09 gmt revision:9 [8] [7] [6] [5] [4] [3] [head]

"Stage 6" part selection:

  • B527 to replace the BF537 -- big difference are more pins + USB OTG high-speed port. The previous deign used Maxim's MAX3421E, which seems to drop packets / have limited bandwidth (or perhaps my USB profile is incorrect?)
    • available in both 0.8mm and 0.5mm BGA. which? both are available from Digi-key. Coarser one is fine, will be easier to route.
    • Does not support mobile SDRAM nor DDR SDRAM; just the vanilla variety.
  • Continue to use the BF532 on the wireless devices (emg, neuro)
  • LAN8710 to replace the LAN83C185. Both can use the MII interface; the LAN83 is not recommended for new designs, though it is in the easier-to-debug TQFP package. Blackfin EZ-KIT for BF527 uses the LAN8710.
    • comes in 0.5mm pitch QFN-32 package.
    • 3.3V and 1.2V supply - can supply 1.2V externally.
  • SDRAM: MT48LC16M16A2BG-7E:D, digikey 557-1220-1-ND 16M x16, or 4M x 16 bit X 4 banks.
    • VFBGA-54 package.
    • 3.3v supply.
  • converter: AD7689 8 channel, 16-bit SAR ADC. has a built-in sequencer, which is sweet. (as well as a temperature sensor??!)
    • Package: 20LFCSP.
    • Seems we can run it at 4.0V, as in stage4.
  • Inst amp: MCP4208, available MSOP-8 (they call it 8-muMax). can use the same circuitry as in stage2 - just check the bandwidth; want 2khz maybe?
  • M25P16 flash, same as on the dev board.
    • Digikey M25P16-VMN6P-ND : 150mil width SOIC-8
  • USB: use the on-board high-speed controller. No need for OTG functionality; FCI USB connector is fine. Digikey 609-1039-ND.

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ref: Wahnoun-2004.01 tags: BMI population_vector neural selection Brown 3D arizona ASU date: 04-06-2007 23:28 gmt revision:3 [2] [1] [0] [head]

PMID-17271333[0] Neuron selection and visual training for population vector based cortical control.

  • M1 and Pmd (not visual areas), bilateral.
  • a series of experiments designed to parameterize a cortical control algorithm without an animal having to move its arm.
  • a highly motivated animal observes as the computer drives a cursor move towards a set of targets once each in a center-out task.
    • how motivated? how did they do this? (primate working for its daily water rations)
  • I do not think this is the way to go. it is better to stimulate in the proper afferents and let the brain learn the control algorithm, the same as when a baby learns to crawl.
    • however, the method described here may be a good way to bootstrap., definitely.
  • want to generate an algorithm that 'tunes-up' control with a few tens of neurons, not hundreds as Miguel estimates.
  • estimate the tuning from 12 seconds of visual following (1.5 seconds per each of the 8 corners of a cube)
  • optimize over the subset of neurons (by dropping them) & computing the individual residual error.
  • their paper seems to be more of an analysis of this neuron-removal method.
  • neurons seem to maintain their tuning between visual following and brain-control.
  • they never actually did brain control

PMID-16705272[1] Selection and parameterization of cortical neurons for neuroprosthetic control

  • here they actually did neuroprosthetic control.
  • most units add noise to the control signal, a few actually improve it -> they emphasize cautious unit selection leaning to simpler computational/electrical systems.
  • point out that the idea of using chronically recorded neural signals has a very long history.. [2,3,4,5] [6] etc.
  • look like it took the monkeys about 1.6-1.8 seconds to reach the target.
    • minimum summed path length / distance to target = 3.5. is that good?