m8ta
use https for features.
text: sort by
tags: modified
type: chronology
[0] Jackson A, Mavoori J, Fetz EE, Correlations between the same motor cortex cells and arm muscles during a trained task, free behavior, and natural sleep in the macaque monkey.J Neurophysiol 97:1, 360-74 (2007 Jan)

[0] 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)

[0] Fetz EE, Operant conditioning of cortical unit activity.Science 163:870, 955-8 (1969 Feb 28)[1] Fetz EE, Finocchio DV, Operant conditioning of specific patterns of neural and muscular activity.Science 174:7, 431-5 (1971 Oct 22)[2] Fetz EE, Finocchio DV, Operant conditioning of isolated activity in specific muscles and precentral cells.Brain Res 40:1, 19-23 (1972 May 12)

[0] Fetz EE, Perlmutter SI, Prut Y, Functions of mammalian spinal interneurons during movement.Curr Opin Neurobiol 10:6, 699-707 (2000 Dec)

[0] Cheney PD, Fetz EE, Functional classes of primate corticomotoneuronal cells and their relation to active force.J Neurophysiol 44:4, 773-91 (1980 Oct)

[0] Jackson A, Mavoori J, Fetz EE, Long-term motor cortex plasticity induced by an electronic neural implant.Nature 444:7115, 56-60 (2006 Nov 2)

[0] Fetz EE, Volitional control of neural activity: implications for brain-computer interfaces.J Physiol 579:Pt 3, 571-9 (2007 Mar 15)

{331}
hide / / print
ref: Jackson-2007.01 tags: Fetz neurochip sleep motor control BMI free behavior EMG date: 09-13-2019 02:21 gmt revision:4 [3] [2] [1] [0] [head]

PMID-17021028[0] Correlations Between the Same Motor Cortex Cells and Arm Muscles During a Trained Task, Free Behavior, and Natural Sleep in the Macaque Monkey

  • used their implanted "neurochip" recorder that recorded both EMG and neural activity. The neurochip buffers data and transmits via IR offline. It doesn't have all that much flash onboard - 16Mb.
    • used teflon-insulated 50um tungsten wires.
  • confirmed that there is a strong causal relationship, constant over the course of weeks, between motor cortex units and EMG activity.
    • some causal relationships between neural firing and EMG varied dependent on the task. Additive / multiplicative encoding?
  • this relationship was different at night, during REM sleep, though (?)
  • point out, as Todorov did, that Stereotyped motion imposes correlation between movement parameters, which could lead to spurrious relationships being mistaken for neural coding.
    • Experiments with naturalistic movement are essential for understanding innate, untrained neural control.
  • references {597} Suner et al 2005 as a previous study of long term cortical recordings. (utah probe)
  • during sleep, M1 cells exhibited a cyclical patter on quiescence followed by periods of elevated activity;
    • the cycle lasted 40-60 minutes;
    • EMG activity was seen at entrance and exit to the elevated activity period.
    • during periods of highest cortical activity, muscle activity was completely suppressed.
    • peak firing rates were above 100hz! (mean: 12-16hz).

____References____

{933}
hide / / print
ref: Moritz-2008.12 tags: FES BMI Fetz Moritz Perlmutter spinal cord date: 01-08-2012 05:18 gmt revision:1 [0] [head]

PMID-18923392[0] Direct control of paralysed muscles by cortical neurons.

  • FES BMI: route signals around a broken spinal cord.
  • Found that "neurons could control functional stimulation equally well regardless of any prior association to movement". interesting. consistent with previous work. Wonder if I can duplicate this result.
  • Another relatively straightforward (?) paper where most of the difficulty is technology (!!). I mean, what new knowledge was needed to do this? Compare this with the technology that was needed. One of these was very challenging. now, as it come in for my stuff: what does technology let you do? Have to motivate.

____References____

[0] Moritz CT, Perlmutter SI, Fetz EE, Direct control of paralysed muscles by cortical neurons.Nature 456:7222, 639-42 (2008 Dec 4)

{341}
hide / / print
ref: Fetz-1973.03 tags: operant conditioning Fetz Baker learning BMI date: 01-07-2012 19:34 gmt revision:2 [1] [0] [head]

PMID-4196269[0] Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles

  • Looked at an operant task through the opposite direction: as a means for looking at reaction time, and muscle responses to trained bursts of activity.
  • recorded from precentral gyrus cells in leg and arm representation.
    • isonel coated tungsten microwires, with great apparent waveform records.
  • also recorded EMG, nylon-insuldated stainless-steel wire, led subcutaneuosly to the head connector.
  • references an even older study concerning the operant conditioning of neural activity in rats by Olds.
  • really simple technology - RC filter to estimate the rate; reward high rate; resets on reward.
    • the evoked operant bursts are undoubtably due to training.
  • looks like it was easy for the monkeys to increase the firing rate of their cortical cells (of course, I'm just skimming the article..)
  • 233 precentral units.
    • which they did some preliminary somatotopic mapping of.
  • neighboring cells mirrored the firing rate changes (logical as they share the local circuitry)
  • in a few sessions the operant bursts were not associated with movements.
  • Could individually condition cells when they happened to record 2 units on the same electrode.

____References____

{303}
hide / / print
ref: Fetz-1969.02 tags: BMI original Fetz operant conditioning date: 01-07-2012 19:04 gmt revision:7 [6] [5] [4] [3] [2] [1] [head]

PMID-4974291[0] Operant conditioning of cortical unit activity

  • (Abstract) The activity of single neurons in precentral cortex of unanesthetized monkeys (Macaca mulatta) was conditioned by reinforcing high rates of neuronal discharge with delivery of a food pellet. Auditory or visual feedback of unit firing rates was usually provided in addition to food reinforcement. After several training sessions, monkeys could increase the activity of newly isolated cells by 50 to 500 percent above rates before reinforcement.
  • Used 'classical' single unit recording.
  • Trepination 5mm circle over hand area.
  • feedback: click for each AP.
  • reinforced on neuron per day.
  • trained neural activity often bursts, usually involved movement such as flexion of the lebow or rotation of the wrist.
  • controlled for sensory positive-feedback loop by performing extinction trials & looking for PETH response to click.
  • I gotta get one of these pellet feeders. monkeys will likely be more motivated, especially if I titrate how frequently they get the food.
  • images/303_1.pdf

PMID-5000088[1] Operant conditioning of specific patterns of neural and muscular activity.

In awake monkeys we recorded activity of single "motor" cortex cells, four contralateral arm muscles, and elbow position, while operantly reinforcing several patterns of motor activity. With the monkey's arm held semiprone in a cast hinged at the elbow, we reinforced active elbow movements and tested cell responses to passive elbow movements. With the cast immobilized we reinforced isometric contraction of each of the four muscles in isolation, and bursts of cortical cell activity with and without simultaneous suppression of muscle activity. Correlations between a precentral cell and specific arm muscles consistently appeared under several behavioral conditions, but could be dissociated by reinforcing cell activity and muscle suppression.

PMID-4624487[2] Operant conditioning of isolated activity in specific muscles and precentral cells

Recorded precentral units in monkeys, trained to contract 4 arm muscles in isolation, under various conditions: passive movements and cutaneous stimulation, active movements and isometric contractions. Some Ss were also reinforced for activity of cortical cells, with no contingency in muscle activity and with simultaneous suppression of all muscular activity. It is concluded that temporal correlations between activity of precentral cells and some other component of the motor response, e.g., muscle activity, force, or position, may depend as strongly on the specific response pattern which is reinforced as on any underlying physiological connection.

____References____

{956}
hide / / print
ref: Fetz-1992 tags: Fetz 1992 Motor control M1 PV date: 01-06-2012 18:13 gmt revision:1 [0] [head]

bibtex: Fetz-1992 Are movement parameters recognizably coded in the activity of single neurons

  • Fetz seems to think that many of the reported correlations or specializations, whether in terms of latency or tuning, are largely a result of observer bias (e.g. to ignore non-obviously tuned cells), and that both simple and complex tuning to motor parameters can be found throughout the motor and premotor cortices.
    • Plus, evidence seems to point to the fact that most things happen simultaneously across multiple areas.
  • Shows that a neural-network model of M1 has complicated and interesting tuning within the hidden network, which he thinks is consistent with the observations.
  • Nice: "This suggests that the search for explicit coding may be diverting us from understanding distributed neural mechanisms that operate without literal representations. "

{1021}
hide / / print
ref: Wyler-1974.02 tags: Wyler Fetz BMI operant conditioning date: 01-05-2012 00:46 gmt revision:3 [2] [1] [0] [head]

PMID-4207598[0] Behavioral control of firing patterns of normal and abnormal neurons in chronic epileptic cortex.

  • Idea: epilepsy treated through biofeedback.
  • Induced epilepsy in monkeys via alumina.
  • Conditioned 198 cells in epileptiform focus; 107 had normal firing patterns.
  • 91 cells had abnormal patterns:
    • Structured bursts with high, invariant burst indices, and could not be conditioned.
    • Cells did not change burstyness based on behavioral state.
    • Lower and more variable burst indices and were as easily conditioned as normal cells.
      • These cells bursted more when the monkey was not paying attention.
  • Operant control: ref 8, 9.
  • Ach, fascinating:
  • Normal precentral cells rarely exhibited interspike intervals less than 10 msec, except during vigorous movements or sleep.
  • Neurons were deemed 'bursty' if they exhibited spontaneous high-frequency firing with interspike intervals less that 5msec.
  • Monkeys obtained proficiency with high-frequency conditioning more quickly and effectively than with low-freq, even with 40% on high and 60% on low.
  • All conditioned cells corresponded to some movement of the contralateral arm (again).
  • Operant conditioning is interesting in this case, as it indicates if cells are still 'functional' in the ensemble.
  • See also: PMID-809116[1]

____References____

[0] Wyler AR, Fetz EE, Behavioral control of firing patterns of normal and abnormal neurons in chronic epileptic cortex.Exp Neurol 42:2, 448-64 (1974 Feb)
[1] Wyler AR, Fetz EE, Ward AA Jr, Firing patterns of epileptic and normal neurons in the chronic alumina focus in undrugged monkeys during different behavioral states.Brain Res 98:1, 1-20 (1975 Nov 7)

{288}
hide / / print
ref: Fetz-2000.12 tags: motor control spinal neurons interneurons movement primitives Fetz review tuning date: 01-03-2012 23:08 gmt revision:4 [3] [2] [1] [0] [head]

PMID-11240278[0] Functions of mammalian spinal interneurons during movement

  • this issue of current opinion in neuro has many reviews of motor control
  • points out that the Bizzi results (they microstimulated & observed a force-field-primitive type organization)
    • others have found that this may be a consequence of decerebration + the structure of the biomechanical groupings of muscles. (see 'update').
  • intraspinal electrodes in the cat provide a secure and reliable method of eliciting forces and movements.
  • CM (corticomotor) cells more often represent synergistic groups of muscles, whereas premotor spinal interneurons are organized to target specific muscles.
    • CMs are therefore more strictly recruited for particular movements.
  • interneurons (IN) are, of course, arrayed in such a way so that antagonist and agonist muscles cross-inhibit eachother (for efficiency)
    • however, we are still able to control the endpoint impedance of the arm - how?
  • spinal interneurons modulate activity during wait period prior to movement!
    • there might be substantial interaction between the cortex and spinal cord.. subjects asked to imagine pressing a foot pedal showed enhanced reflexes in the involved soleus muscle.
      • cognitive priming?
  • spinal reflexes are strongly modulated in movement.

____References____

{327}
hide / / print
ref: Cheney-1980.1 tags: M1 kinematics dynamics tuning STA EMG Fetz date: 01-03-2012 02:30 gmt revision:3 [2] [1] [0] [head]

PMID-6253605[0] Functional classes of primate corticomotoneuronal cells and their relation to active force

  • monkeys made ramp and hold torque wrist movements/contractions.
  • corticomotoneuronal cells identified by clear postspike facilitation of rectified EMG activity.
  • all CM cells or PTNs were related to force - with a mixture/diversity of phasic, tonic, and ramp discharge rate profiles.
  • torque trajectory rather than velocity signal seems to be the primnary determinant of cell firing rate...
  • cells appear to be recruited at low force levels..with increasing rates as the torque increases.
  • high firing rates observed > 100!
    • and really low firing rate when there was no torque.

____References____

{921}
hide / / print
ref: Mavoori-2005.1 tags: Fetz ICMS stim wireless recording flash 2005 date: 12-16-2011 04:21 gmt revision:3 [2] [1] [0] [head]

PMID-16102841[0] An autonomous implantable computer for neural recording and stimulation in unrestrained primates.

  • Pretty basic: AFE + bandpass filter, 11.7ksps ADC, uC spike discriminator, microstimulator, IRDA link, 4Mbit flash (why so small? -- 2005).
  • Device could run for weeks at a time.
  • Used in his Hebbian learning task [1]

____References____

[0] Mavoori J, Jackson A, Diorio C, Fetz E, An autonomous implantable computer for neural recording and stimulation in unrestrained primates.J Neurosci Methods 148:1, 71-7 (2005 Oct 15)
[1] Jackson A, Mavoori J, Fetz EE, Long-term motor cortex plasticity induced by an electronic neural implant.Nature 444:7115, 56-60 (2006 Nov 2)

{69}
hide / / print
ref: Jackson-2006.11 tags: Fetz Andrew Jackson BMI motor learning microstimulation date: 12-16-2011 04:20 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-17057705 Long-term motor cortex plasticity induced by an electronic neural implant.

  • used an implanted neurochip.
  • record from site A in motor cortex (encodes movement A)
  • stimulate site B of motor cortex (encodes movement B)
  • after a few days of learning, stimulate A and generate mixure of AB then B-type movements.
  • changes only occurred when stimuli were delivered within 50ms of recorded spikes.
  • quantified with measurement of (to) radial/ulnar deviation and flexion/extension of the wrist.
  • stimulation in target (site B) was completely sub-threshold (40ua)
  • distance between recording and stimulation site did not matter.
  • they claim this is from Hebb's rule: if one neuron fires just before another (e.g. it contributes to the second's firing), then the connection between the two is strengthened. However, i originally thought this was because site A was controlling the betz cells in B, therefore for consistency A's map was modified to agree with its /function/.
  • repetitive high-frequency stimulation has been shown to expand movement representations in the motor cortex of rats (hmm.. interesting)
  • motor cortex is highly active in REM

____References____

{715}
hide / / print
ref: Legenstein-2008.1 tags: Maass STDP reinforcement learning biofeedback Fetz synapse date: 04-09-2009 17:13 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-18846203[0] A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback

  • (from abstract) The resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP.
    • This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker.
  • STDP is prevalent in the cortex ; however, it requires a second signal:
    • Dopamine seems to gate STDP in corticostriatal synapses
    • ACh does the same or similar in the cortex. -- see references 8-12
  • simple learning rule they use: d/dtW ij(t)=C ij(t)D(t) d/dt W_{ij}(t) = C_{ij}(t) D(t)
  • Their notes on the Fetz/Baker experiments: "Adjacent neurons tended to change their firing rate in the same direction, but also differential changes of directions of firing rates of pairs of neurons are reported in [17] (when these differential changes were rewarded). For example, it was shown in Figure 9 of [17] (see also Figure 1 in [19]) that pairs of neurons that were separated by no more than a few hundred microns could be independently trained to increase or decrease their firing rates."
  • Their result is actually really simple - there is no 'control' or biofeedback - there is no visual or sensory input, no real computation by the network (at least for this simulation). One neuron is simply reinforced, hence it's firing rate increases.
    • Fetz & later Schimdt's work involved feedback and precise control of firing rate; this does not.
    • This also does not address the problem that their rule may allow other synapses to forget during reinforcement.
  • They do show that exact spike times can be rewarded, which is kinda interesting ... kinda.
  • Tried a pattern classification task where all of the information was in the relative spike timings.
    • Had to run the pattern through the network 1000 times. That's a bit unrealistic (?).
      • The problem with all these algorithms is that they require so many presentations for gradient descent (or similar) to work, whereas biological systems can and do learn after one or a few presentations.
  • Next tried to train neurons to classify spoken input
    • Audio stimului was processed through a cochlear model
    • Maass previously has been able to train a network to perform speaker-independent classification.
    • Neuron model does, roughly, seem to discriminate between "one" and "two"... after 2000 trials (each with a presentation of 10 of the same digit utterance). I'm still not all that impressed. Feels like gradient descent / linear regression as per the original LSM.
  • A great many derivations in the Methods section... too much to follow.
  • Should read refs:
    • PMID-16907616[1] Gradient learning in spiking neural networks by dynamic perturbation of conductances.
    • PMID-17220510[2] Solving the distal reward problem through linkage of STDP and dopamine signaling.

____References____

[0] Legenstein R, Pecevski D, Maass W, A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback.PLoS Comput Biol 4:10, e1000180 (2008 Oct)
[1] Fiete IR, Seung HS, Gradient learning in spiking neural networks by dynamic perturbation of conductances.Phys Rev Lett 97:4, 048104 (2006 Jul 28)
[2] Izhikevich EM, Solving the distal reward problem through linkage of STDP and dopamine signaling.Cereb Cortex 17:10, 2443-52 (2007 Oct)

{329}
hide / / print
ref: Fetz-2007.03 tags: hot fetz BMI biofeedback operant training learning date: 09-07-2008 18:56 gmt revision:7 [6] [5] [4] [3] [2] [1] [head]

PMID-17234689[0] Volitional control of neural activity: implications for brain-computer interfaces (part of a symposium)

  • Limits in the degree of accuracy of control in the latter studies can be attributed to several possible factors. Some of these factors, particularly limited practice time, can be addressed with long-term implanted BCIs. YES.
  • Accurate device control under diverse behavioral conditions depends significantly on the degree to which the neural activity can be volitionally modulated. YES again.
  • neurons (50%) in somatosensory (post central) cortex fire prior to volitional movement. interesting.
  • It should also be noted that the monkeys activated some motor cortex cells for operant reward without ever making any observed movements See: Fetz & Finocchio, 1975, PMID-810359.
    • Motor cortex neurons that were reliably associated with EMG activity in particular forelimb muscles could be readily dissociated from EMG when the rewarded pattern involved cell activity and muscle suppression.
    • This may be realated to switching between real and imagined movements.
  • Biofeedback worked well for activating low-threshold motor units in isolation, but not high threshold units; attempts to reverse recruitment order of motor units largely failed to demonstrate violations of the size principle.
  • This (the typical BMI decoding strategy) interposes an intermediate stage that may complicate the relationship between neural activity and the final output control of the device
    • again, in other words: "First, the complex transforms of neural activity to output parameters may complicate the degree to which neural control can be learned."
    • quote: This flexibility of internal representations (e.g. to imagine moving your arm, train the BMI on that, and rapidly directly control the arm rather than gonig through the intermediate/training step) underlies the ability to cognitively incorporate external prosthetic devices in to the body image, and explains the rapid conceptual adaptation to artificial environments, such as virtual reality or video games.
      • There is a high flexibility of input (sensory) and output (motor) for purposes of imagining / simulating movements.
  • adaptive learning algorithms may create a moving target for the robust learning algorithm; does it not make more sense to allow the cortex to work it's magic?
  • Degree of independent control of cells may be inherently contrained by ensemble interactions
    • To the extent that internal representations depend on relationships between the activities of neurons in an ensemble, processing of these representations involves corresponding constraints on the independence of those activities.
  • quote: "These factors suggest that the range and reliability of neural control in BMI might increase significantly when prolonged stable recordings are acheived and the subject can practice under consistent conditions over extended periods of time.
  • Fetz agrees that the limitation is the goddamn technology. need to fix this!
  • there is evidence of favortism in his citations (friends with Miguel??)

humm.. this paper came out a month ago, and despite the fact that he is much older and more experienced than i, we have arrived at the same conclusions by looking at the same set of data/papers. so: that's good, i guess.

____References____