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[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] Isoda M, Hikosaka O, Role for subthalamic nucleus neurons in switching from automatic to controlled eye movement.J Neurosci 28:28, 7209-18 (2008 Jul 9)

[0] Evarts EV, Relation of pyramidal tract activity to force exerted during voluntary movement.J Neurophysiol 31:1, 14-27 (1968 Jan)

[0] Ashe J, Force and the motor cortex.Behav Brain Res 87:2, 255-69 (1997 Sep)[1] Cabel DW, Cisek P, Scott SH, Neural activity in primary motor cortex related to mechanical loads applied to the shoulder and elbow during a postural task.J Neurophysiol 86:4, 2102-8 (2001 Oct)[2] Cheney PD, Fetz EE, Functional classes of primate corticomotoneuronal cells and their relation to active force.J Neurophysiol 44:4, 773-91 (1980 Oct)[3] Evarts EV, Relation of pyramidal tract activity to force exerted during voluntary movement.J Neurophysiol 31:1, 14-27 (1968 Jan)[4] Evarts EV, Activity of pyramidal tract neurons during postural fixation.J Neurophysiol 32:3, 375-85 (1969 May)[5] Humphrey DR, Schmidt EM, Thompson WD, Predicting measures of motor performance from multiple cortical spike trains.Science 170:959, 758-62 (1970 Nov 13)[6] Thach WT, Correlation of neural discharge with pattern and force of muscular activity, joint position, and direction of intended next movement in motor cortex and cerebellum.J Neurophysiol 41:3, 654-76 (1978 May)[7] Wetts R, Kalaska JF, Smith AM, Cerebellar nuclear cell activity during antagonist cocontraction and reciprocal inhibition of forearm muscles.J Neurophysiol 54:2, 231-44 (1985 Aug)[8] Georgopoulos AP, Ashe J, Smyrnis N, Taira M, The motor cortex and the coding of force.Science 256:5064, 1692-5 (1992 Jun 19)[9] Kalaska JF, Cohen DA, Hyde ML, Prud'homme M, A comparison of movement direction-related versus load direction-related activity in primate motor cortex, using a two-dimensional reaching task.J Neurosci 9:6, 2080-102 (1989 Jun)[10] Li CS, Padoa-Schioppa C, Bizzi E, Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field.Neuron 30:2, 593-607 (2001 May)[11] Sergio LE, Kalaska JF, Systematic changes in directional tuning of motor cortex cell activity with hand location in the workspace during generation of static isometric forces in constant spatial directions.J Neurophysiol 78:2, 1170-4 (1997 Aug)[12] Sergio LE, Kalaska JF, Systematic changes in motor cortex cell activity with arm posture during directional isometric force generation.J Neurophysiol 89:1, 212-28 (2003 Jan)[13] Taira M, Boline J, Smyrnis N, Georgopoulos AP, Ashe J, On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional static isometric force.Exp Brain Res 109:3, 367-76 (1996 Jun)[14] Maier MA, Bennett KM, Hepp-Reymond MC, Lemon RN, Contribution of the monkey corticomotoneuronal system to the control of force in precision grip.J Neurophysiol 69:3, 772-85 (1993 Mar)[15] Hepp-Reymond M, Kirkpatrick-Tanner M, Gabernet L, Qi HX, Weber B, Context-dependent force coding in motor and premotor cortical areas.Exp Brain Res 128:1-2, 123-33 (1999 Sep)[16] Smith AM, Hepp-Reymond MC, Wyss UR, Relation of activity in precentral cortical neurons to force and rate of force change during isometric contractions of finger muscles.Exp Brain Res 23:3, 315-32 (1975 Sep 29)[17] Cooke JD, Brown SH, Movement-related phasic muscle activation. II. Generation and functional role of the triphasic pattern.J Neurophysiol 63:3, 465-72 (1990 Mar)[18] Almeida GL, Hong DA, Corcos D, Gottlieb GL, Organizing principles for voluntary movement: extending single-joint rules.J Neurophysiol 74:4, 1374-81 (1995 Oct)[19] Gottlieb GL, Latash ML, Corcos DM, Liubinskas TJ, Agarwal GC, Organizing principles for single joint movements: V. Agonist-antagonist interactions.J Neurophysiol 67:6, 1417-27 (1992 Jun)[20] Corcos DM, Agarwal GC, Flaherty BP, Gottlieb GL, Organizing principles for single-joint movements. IV. Implications for isometric contractions.J Neurophysiol 64:3, 1033-42 (1990 Sep)[21] Gottlieb GL, Corcos DM, Agarwal GC, Latash ML, Organizing principles for single joint movements. III. Speed-insensitive strategy as a default.J Neurophysiol 63:3, 625-36 (1990 Mar)[22] Corcos DM, Gottlieb GL, Agarwal GC, Organizing principles for single-joint movements. II. A speed-sensitive strategy.J Neurophysiol 62:2, 358-68 (1989 Aug)[23] Gottlieb GL, Corcos DM, Agarwal GC, Organizing principles for single-joint movements. I. A speed-insensitive strategy.J Neurophysiol 62:2, 342-57 (1989 Aug)[24] Ghez C, Gordon J, Trajectory control in targeted force impulses. I. Role of opposing muscles.Exp Brain Res 67:2, 225-40 (1987)[25] Sainburg RL, Ghez C, Kalakanis D, Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms.J Neurophysiol 81:3, 1045-56 (1999 Mar)[26] Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT, On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex.J Neurosci 2:11, 1527-37 (1982 Nov)[27] Ashe J, Georgopoulos AP, Movement parameters and neural activity in motor cortex and area 5.Cereb Cortex 4:6, 590-600 (1994 Nov-Dec)[28] Caminiti R, Johnson PB, Urbano A, Making arm movements within different parts of space: dynamic aspects in the primate motor cortex.J Neurosci 10:7, 2039-58 (1990 Jul)[29] Caminiti R, Johnson PB, Galli C, Ferraina S, Burnod Y, Making arm movements within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets.J Neurosci 11:5, 1182-97 (1991 May)[30] Matsuzaka Y, Picard N, Strick PL, Skill representation in the primary motor cortex after long-term practice.J Neurophysiol 97:2, 1819-32 (2007 Feb)[31] Fu QG, Suarez JI, Ebner TJ, Neuronal specification of direction and distance during reaching movements in the superior precentral premotor area and primary motor cortex of monkeys.J Neurophysiol 70:5, 2097-116 (1993 Nov)

[0] Evarts EV, Activity of pyramidal tract neurons during postural fixation.J Neurophysiol 32:3, 375-85 (1969 May)[1] Evarts EV, Relation of pyramidal tract activity to force exerted during voluntary movement.J Neurophysiol 31:1, 14-27 (1968 Jan)

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

[0] Aflalo TN, Graziano MS, Relationship between unconstrained arm movements and single-neuron firing in the macaque motor cortex.J Neurosci 27:11, 2760-80 (2007 Mar 14)

[0] Moran DW, Schwartz AB, Motor cortical representation of speed and direction during reaching.J Neurophysiol 82:5, 2676-92 (1999 Nov)

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

[0] Atallah HE, Lopez-Paniagua D, Rudy JW, O'Reilly RC, Separate neural substrates for skill learning and performance in the ventral and dorsal striatum.Nat Neurosci 10:1, 126-31 (2007 Jan)

[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] Matsuzaka Y, Picard N, Strick PL, Skill representation in the primary motor cortex after long-term practice.J Neurophysiol 97:2, 1819-32 (2007 Feb)

[0] Diedrichsen J, Hashambhoy Y, Rane T, Shadmehr R, Neural correlates of reach errors.J Neurosci 25:43, 9919-31 (2005 Oct 26)

[0] Tamaki M, Matsuoka T, Nittono H, Hori T, Fast sleep spindle (13-15 hz) activity correlates with sleep-dependent improvement in visuomotor performance.Sleep 31:2, 204-11 (2008 Feb 1)

[0] Morin A, Doyon J, Dostie V, Barakat M, Hadj Tahar A, Korman M, Benali H, Karni A, Ungerleider LG, Carrier J, Motor sequence learning increases sleep spindles and fast frequencies in post-training sleep.Sleep 31:8, 1149-56 (2008 Aug 1)

[0] Song S, Consciousness and the consolidation of motor learning.Behav Brain Res 196:2, 180-6 (2009 Jan 23)

[0] Rasch B, Gais S, Born J, Impaired Off-Line Consolidation of Motor Memories After Combined Blockade of Cholinergic Receptors During REM Sleep-Rich Sleep.Neuropsychopharmacology no Volume no Issue no Pages (2009 Feb 4)

[0] Peters J, Schaal S, Reinforcement learning of motor skills with policy gradients.Neural Netw 21:4, 682-97 (2008 May)

[0] Churchland MM, Afshar A, Shenoy KV, A central source of movement variability.Neuron 52:6, 1085-96 (2006 Dec 21)

[0] Karni A, Meyer G, Rey-Hipolito C, Jezzard P, Adams MM, Turner R, Ungerleider LG, The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex.Proc Natl Acad Sci U S A 95:3, 861-8 (1998 Feb 3)

[0] Nakahara H, Doya K, Hikosaka O, Parallel cortico-basal ganglia mechanisms for acquisition and execution of visuomotor sequences - a computational approach.J Cogn Neurosci 13:5, 626-47 (2001 Jul 1)

[0] Hikosaka O, Nakamura K, Sakai K, Nakahara H, Central mechanisms of motor skill learning.Curr Opin Neurobiol 12:2, 217-22 (2002 Apr)

[0] Graybiel AM, Aosaki T, Flaherty AW, Kimura M, The basal ganglia and adaptive motor control.Science 265:5180, 1826-31 (1994 Sep 23)

[0] Graybiel AM, The basal ganglia: learning new tricks and loving it.Curr Opin Neurobiol 15:6, 638-44 (2005 Dec)

[0] Radhakrishnan SM, Baker SN, Jackson A, Learning a novel myoelectric-controlled interface task.J Neurophysiol no Volume no Issue no Pages (2008 Jul 30)

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

[0] Narayanan NS, Kimchi EY, Laubach M, Redundancy and synergy of neuronal ensembles in motor cortex.J Neurosci 25:17, 4207-16 (2005 Apr 27)

[0] Francis JT, Influence of the inter-reach-interval on motor learning.Exp Brain Res 167:1, 128-31 (2005 Nov)

[0] Kawato M, Internal models for motor control and trajectory planning.Curr Opin Neurobiol 9:6, 718-27 (1999 Dec)

[0] Scott SH, Optimal feedback control and the neural basis of volitional motor control.Nat Rev Neurosci 5:7, 532-46 (2004 Jul)

[0] Boline J, Ashe J, On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional dynamic isometric force.Exp Brain Res 167:2, 148-59 (2005 Nov)

[0] Chan SS, Moran DW, Computational model of a primate arm: from hand position to joint angles, joint torques and muscle forces.J Neural Eng 3:4, 327-37 (2006 Dec)

[0] Sergio LE, Hamel-Paquet C, Kalaska JF, Motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks.J Neurophysiol 94:4, 2353-78 (2005 Oct)[1] Hatsopoulos NG, Encoding in the motor cortex: was evarts right after all? Focus on "motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks".J Neurophysiol 94:4, 2261-2 (2005 Oct)[2] Cooke JD, Brown SH, Movement-related phasic muscle activation. II. Generation and functional role of the triphasic pattern.J Neurophysiol 63:3, 465-72 (1990 Mar)[3] Almeida GL, Hong DA, Corcos D, Gottlieb GL, Organizing principles for voluntary movement: extending single-joint rules.J Neurophysiol 74:4, 1374-81 (1995 Oct)[4] Gottlieb GL, Latash ML, Corcos DM, Liubinskas TJ, Agarwal GC, Organizing principles for single joint movements: V. Agonist-antagonist interactions.J Neurophysiol 67:6, 1417-27 (1992 Jun)[5] Corcos DM, Agarwal GC, Flaherty BP, Gottlieb GL, Organizing principles for single-joint movements. IV. Implications for isometric contractions.J Neurophysiol 64:3, 1033-42 (1990 Sep)[6] Gottlieb GL, Corcos DM, Agarwal GC, Latash ML, Organizing principles for single joint movements. III. Speed-insensitive strategy as a default.J Neurophysiol 63:3, 625-36 (1990 Mar)[7] Corcos DM, Gottlieb GL, Agarwal GC, Organizing principles for single-joint movements. II. A speed-sensitive strategy.J Neurophysiol 62:2, 358-68 (1989 Aug)[8] Gottlieb GL, Corcos DM, Agarwal GC, Organizing principles for single-joint movements. I. A speed-insensitive strategy.J Neurophysiol 62:2, 342-57 (1989 Aug)[9] Ghez C, Gordon J, Trajectory control in targeted force impulses. I. Role of opposing muscles.Exp Brain Res 67:2, 225-40 (1987)[10] Sainburg RL, Ghez C, Kalakanis D, Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms.J Neurophysiol 81:3, 1045-56 (1999 Mar)

[0] Hatsopoulos NG, Encoding in the motor cortex: was evarts right after all? Focus on "motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks".J Neurophysiol 94:4, 2261-2 (2005 Oct)

[0] Ashe J, Georgopoulos AP, Movement parameters and neural activity in motor cortex and area 5.Cereb Cortex 4:6, 590-600 (1994 Nov-Dec)

[0] Maier MA, Bennett KM, Hepp-Reymond MC, Lemon RN, Contribution of the monkey corticomotoneuronal system to the control of force in precision grip.J Neurophysiol 69:3, 772-85 (1993 Mar)[1] Smith AM, Hepp-Reymond MC, Wyss UR, Relation of activity in precentral cortical neurons to force and rate of force change during isometric contractions of finger muscles.Exp Brain Res 23:3, 315-32 (1975 Sep 29)

[0] Hepp-Reymond M, Kirkpatrick-Tanner M, Gabernet L, Qi HX, Weber B, Context-dependent force coding in motor and premotor cortical areas.Exp Brain Res 128:1-2, 123-33 (1999 Sep)

[0] Caminiti R, Johnson PB, Galli C, Ferraina S, Burnod Y, Making arm movements within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets.J Neurosci 11:5, 1182-97 (1991 May)

[0] Caminiti R, Johnson PB, Urbano A, Making arm movements within different parts of space: dynamic aspects in the primate motor cortex.J Neurosci 10:7, 2039-58 (1990 Jul)[1] Caminiti R, Johnson PB, Galli C, Ferraina S, Burnod Y, Making arm movements within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets.J Neurosci 11:5, 1182-97 (1991 May)

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

[0] Georgopoulos AP, Ashe J, Smyrnis N, Taira M, The motor cortex and the coding of force.Science 256:5064, 1692-5 (1992 Jun 19)

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

[0] Thach WT, Correlation of neural discharge with pattern and force of muscular activity, joint position, and direction of intended next movement in motor cortex and cerebellum.J Neurophysiol 41:3, 654-76 (1978 May)

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

[0] Brashers-Krug T, Shadmehr R, Bizzi E, Consolidation in human motor memory.Nature 382:6588, 252-5 (1996 Jul 18)

[0] DeLong MR, Strick PL, Relation of basal ganglia, cerebellum, and motor cortex units to ramp and ballistic limb movements.Brain Res 71:2-3, 327-35 (1974 May 17)

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

[0] Fu QG, Suarez JI, Ebner TJ, Neuronal specification of direction and distance during reaching movements in the superior precentral premotor area and primary motor cortex of monkeys.J Neurophysiol 70:5, 2097-116 (1993 Nov)

[0] Kettner RE, Schwartz AB, Georgopoulos AP, Primate motor cortex and free arm movements to visual targets in three-dimensional space. III. Positional gradients and population coding of movement direction from various movement origins.J Neurosci 8:8, 2938-47 (1988 Aug)[1] Georgopoulos AP, Kettner RE, Schwartz AB, Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population.J Neurosci 8:8, 2928-37 (1988 Aug)[2] Schwartz AB, Kettner RE, Georgopoulos AP, Primate motor cortex and free arm movements to visual targets in three-dimensional space. I. Relations between single cell discharge and direction of movement.J Neurosci 8:8, 2913-27 (1988 Aug)[3] Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT, On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex.J Neurosci 2:11, 1527-37 (1982 Nov)

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

[0] Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT, On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex.J Neurosci 2:11, 1527-37 (1982 Nov)

[0] Ostry DJ, Feldman AG, A critical evaluation of the force control hypothesis in motor control.Exp Brain Res 153:3, 275-88 (2003 Dec)

[0] Cabel DW, Cisek P, Scott SH, Neural activity in primary motor cortex related to mechanical loads applied to the shoulder and elbow during a postural task.J Neurophysiol 86:4, 2102-8 (2001 Oct)

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

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ref: -2015 tags: PaRAC1 photoactivatable Rac1 synapse memory optogenetics 2p imaging mouse motor skill learning date: 10-30-2019 20:35 gmt revision:1 [0] [head]

PMID-26352471 Labelling and optical erasure of synaptic memory traces in the motor cortex

  • Idea: use Rac1, which has been shown to induce spine shrinkage, coupled to a light-activated domain to allow for optogenetic manipulation of active synapses.
  • PaRac1 was coupled to a deletion mutant of PSD95, PSD delta 1.2, which concentrates at the postsynaptic site, but cannot bind to postsynaptic proteins, thus minimizing the undesirable effects of PSD-95 overexpression.
    • PSD-95 is rapidly degraded by proteosomes
    • This gives spatial selectivity.
  • They then exploited the dendritic targeting element (DTE) of Arc mRNA which is selectively targeted and translated in activiated dendritic segments in response to synaptic activation in an an NMDA receptor dependent manner.
    • Thereby giving temporal selectivity.
  • Construct is then PSD-PaRac1-DTE; this was tested on hippocampal slice cultures.
  • Improved sparsity and labelling further by driving it with the Arc promoter.
  • Motor learning is impaired in Arc KO mice; hence inferred that the induction of AS-PaRac1 by the Arc promoter would enhance labeling during learning-induced potentiation.
  • Delivered construct via in-utero electroporation.
  • Observed rotarod-induced learning; the PaRac signal decayed after two days, but the spine volume persisted in spines that showed Arc / DTE hence PA labeled activity.
  • Now, since they had a good label, performed rotarod training followed by (at variable delay) light pulses to activate Rac, thereby suppressing recently-active synapses.
    • Observed both a depression of behavioral performance.
    • Controlled with a second task; could selectively impair performance on one of the tasks based on ordering/timing of light activation.
  • The localized probe also allowed them to image the synapse populations active for each task, which were largely non-overlapping.

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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____

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ref: -2012 tags: Emo Todorov contact invariant animation optimization complex motor behavior date: 05-04-2016 17:34 gmt revision:3 [2] [1] [0] [head]

* Watch the [http://homes.cs.washington.edu/~todorov/index.php?video=MordatchSIGGRAPH12&paper=Mordatch,%20SIGGRAPH%202012 movies! Discovery of complex behaviors through contact-invariant optimization]

  • Complex movements tend to have phases within which the set of active contacts (hands, feet) remains invariant (hence can exert forces on the objects they are contacting, or vice versa).
  • Discovering suitable contact sets is the central goal of optimization in our approach.
    • Once this is done, optimizing the remaining aspects of the movement tends to be relatively straightforward.
    • They do this through axillary scalar variables which indicate whether the a contact is active or not, hence whether to enable contact forces.
      • Allows the optimizer to 'realize' that movements should have phases.
      • Also "shapes the energy landscape to be smoother and better behaved"
  • Initial attempts to make these contact axillary variables discrete -- when and where -- which was easy for humans to specify, but made optimization intractable.
    • Motion between contacts was modeled as a continuous feedback system.
  • Instead, the contact variables c ic_i have to be continuous.
  • Contact forces are active only when c ic_i is 'large'.
    • Hence all potential contacts have to be enumerated in advance.
  • Then, parameterize the end effector (position) and use inverse kinematics to figure out joint angles.
  • Optimization:
    • Break the movement up into a predefined number of phases, equal duration.
    • Interpolate end-effector with splines
    • Physics constraints are 'soft' -- helps the optimizer : 'powerful continuation methods'
      • That is, weight different terms differently in phases of the optimization process.
      • Likewise, appendages are allowed to stretch and intersect, with a smooth cost.
    • Contact-invariant cost penalizes distortion and slip (difference between endpoint and surface, measured normal, and velocity relative to contact point)
      • Contact point is also 'soft' and smooth via distance-normalized weighting.
    • All contact forces are merged into a f 6f \in \mathbb{R}^6 vector, which includes both forces and torques. Hence contact force origin can move within the contact patch, which again makes the optimization smoother.
    • Set τ(q,q˙,q¨)=J(q) Tf+Bu\tau(q, \dot{q}, \ddot{q}) = J(q)^T f + B u where J(q) T J(q)^T maps generalize (endpoint) velocities to contact-point velocities, and f above are the contact-forces. BB is to map control forces uu to the full space.
    • τ(q,q˙,q¨)=M(q)q˙+C(q,q˙)q˙+G(q)\tau(q, \dot{q}, \ddot{q}) = M(q)\dot{q} + C(q, \dot{q})\dot{q} + G(q) -- M is inertia matrix, C is Coriolis matrix, g is gravity.
      • This means: forces need to add to zero. (friction ff + control uu = inertia + coriolis + gravity)
    • Hence need to optimize ff and uu .
      • Use friction-cone approximation for non-grab (feet) contact forces.
    • These are optimized within a quadratic programming framework.
      • LBFGS algo.
      • Squared terms for friction and control, squared penalization for penetrating and slipping on a surface.
    • Phases of optimization (continuation method):
      • L(s)=L CI(s)+L physics(s)+L task(s)+L hint(s)L(s) = L_{CI}(s) + L_{physics}(s) + L_{task}(s) + L_{hint}(s)
      • task term only: wishful thinking.
      • all 4 terms, physcics lessened -- gradually add constraints.
      • all terms, no hint, full physics.
  • Total time to simulate 2-10 minutes per clip (only!)
  • The equations of the paper seem incomplete -- not clear how QP eq fits in with the L(s)L(s) , and how c ic_i fits in with J(q) Tf+BuJ(q)^T f + B u

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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: -0 tags: implicit motor sequence learning basal ganglia parkinson's disease date: 03-06-2012 22:47 gmt revision:2 [1] [0] [head]

PMID-19744484 What can man do without basal ganglia motor output? The effect of combined unilateral subthalamotomy and pallidotomy in a patient with Parkinson's disease.

  • Unilateral lesion of both STN and GPi in one patient. Hence, the patient was his own control.
    • DRastically reduced the need for medication, indicating that it had a profound effect on BG output.
  • Arm contralateral lesion showed faster reaction times and normal movement speeds; ipsilateral arm parkinsonian.
  • Implicit sequence learning in a task was absent.
  • In a go / no-go task when the percent of no-go trials increased, the RT speriority of contralateral hand was lost.
  • " THe risk of persistent dyskinesias need not be viewed as a contraindication to subthalamotomy in PD patients since they can be eliminated if necessary by a subsequent pallidotomy without producting deficits that impair daily life.
  • Subthalamotomy incurs persistent hemiballismus / chorea in 8% of patients; in many others chorea spontaneously disappears.
    • This can be treated by a subsequent pallidotomy.
  • Patient had Parkinsonian symptoms largely restricted to right side.
  • Measured TMS ability to stimulate motor cortex -- which appears to be a common treatment. STN / GPi lesion appears to have limited effect on motor cortex exitability 9other things redulate it?).
  • conclusion: interrupting BG output removes such abnormal signaling and allows the motor system to operate more normally.
    • Bath DA presumably calms hyperactive SNr neurons.
    • Yuo cannot distrupt output of the BG with compete imuntiy; the associated abnormalities may be too subtle to be detected in normal behaviors, explaniing the overall clinical improbement seen in PD patients after surgery and the scarcity fo clinical manifestations in people with focal BG lesions (Bhatia and Marsden, 1994; Marsden and Obeso 1994).
      • Our results support the prediction that surgical lesions of the BG in PD would be associated with inflexibility or reduced capability for motor learning. (Marsden and Obeso, 1994).
  • It is better to dispense with faulty BG output than to have a faulty one.

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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.

____References____

[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: Isoda-2008.07 tags: STN switching motor control scaccades monkeys electrophysiology DBS date: 02-22-2012 15:02 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-18614691[0] Role for subthalamic nucleus neurons in switching from automatic to controlled eye movement.

  • we found neurons that showed a phasic change in activity specifically before volitionally controlled saccades which were switched from automatic saccades
  • A majority of switch-related neurons were considered to inhibit no-longer-valid automatic processes, and the inhibition started early enough to enable the animal to switch.
  • We suggest that the STN mediates the control signal originated from the medial frontal cortex and implements the behavioral switching function using its connections with other basal ganglia nuclei and the superior colliculus.
  • neurons have a really high rate of spiking - what we observe in DBS surgeries.
  • nice. There may be alternate explanations, but this one is plausible.

____References____

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ref: Turner-2010.12 tags: STN DBS basal ganglia motor learning vigor scaling review date: 02-16-2012 21:27 gmt revision:3 [2] [1] [0] [head]

PMID-20850966[0] Basal ganglia contributions to motor control: a vigorous tutor.

  • Using single-cell recording and inactivation protocols these studies provide consistent support for two hypotheses: the BG modulates movement performance ('vigor') according to motivational factors (i.e. context-specific cost/reward functions) and the BG contributes to motor learning.
  • Most BG associated clinical conditions involve some form of striatal dysfunction -- clincal sings occur when the prinicpal input nucleus of the BG network is affected.
    • Lesions of the output nuclei are typically subtle, consistent that pallidotomy is an effective treatment for PD and dystonia.
    • It is better to block BG output completely than pervert the normal operations of motor areas that receive BG output.
    • Pathological firing patters degrade the ability of thalamic neurons to transmit information reliably.
      • Bad BG activity may block cortico-thalamic-cortico communication.
      • Hence BG treatment does not reflect negative images of normal function.
  • Years of debate have been resolved by a confirmation that the direct and indirect pathways originate from biochamically distinct and morphologically disctinct types of projection neurons [97, 105].
    • Direct: D1; indirect = D2, GPe.
  • CMPf projects back to the striatuim.
  • Movement representation in the BG: ref [36]
  • Results of GPi inactivation:
    • RT are not lengthened. These results are not consistent with the idea that the BG contributes to the selection or initiation of movement.
    • GPi inactivation does not perturb on-line error correction process or the generation of discrete corrective submovements.
      • Rapid and-path corrections are preserved in PD.
      • Challenges the idea that the BG mediates on-line correction of motor error.
    • GPi inactivation does not affect the execution of overlearned or externally cued sequences of movements.
      • contradicts claims, based on neuroimaging and clinical evidence, that the BG is involved in the long term storage of overlearned motor sequences or the ability to string together successive motor acts.
    • GPi inactivation reduces movement velocity and acceleration.
      • Very consistent finding.
      • Mirrors the bradykinesia observed in PD.
      • Common side-effect of DBS of the GPi for dystonia.
    • GPI inactivation produces marked hypometria -- unsershooting of the desired movement extent.
      • Un accompanied by changes in movement linearity or directional accuracy.
  • Conclusion: impaired gain.
    • Movement: bradykinesia and hypometria
    • hand-writing: micrographia
    • speech: hyophonia [65].
    • There is a line of evidence suggesting that movement gain is controlled independently of movement direction.
    • Motor cost terms, which scale with velocity, may link and animals' previous experience with the cost/benefit contingencies of a task [75] to its current allocation of energy to meet the demands of a specific task.
      • This is consistent with monkey rapid fatiguing following BG lesion.
      • Schmidt et al [5] showed that patients with lilateral esions of the putamen or pallidum are able to control grip forces normally in response to explicit sensory instructions, but do not increase grip force spontaneously despite full understanding that higher forces will earn more money.
    • Sensory cuse and curgent conditions increase movement speed equally in healthy subjects and PD patients.
  • BG and learning:
    • role in dopamine-mediated learning is uncontroversial and supported by a vast literature [10,14,87].
    • Seems to be involved in reward-driven acquisition, but not long-term retention or recall of well-learned motor skills.
    • Single unit recording studies have demonstrated major changes in the BG of animals as they learn procedural tasks. [88-90]
      • Learning occurs earlier in the striatum than cortex [89,90].
    • One of the sequelae associated with pallidotomy is an impaired ability to learn new motor sequences [22 92] and arbitrary stimulus-response associations [93].
    • BG is the tutor, cortex is the storage.

____References____

[0] Turner RS, Desmurget M, Basal ganglia contributions to motor control: a vigorous tutor.Curr Opin Neurobiol 20:6, 704-16 (2010 Dec)

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ref: Kuhn-2004.04 tags: STN LFP syncronization movement motor planning parkinsons PD DBS beta date: 01-26-2012 17:28 gmt revision:7 [6] [5] [4] [3] [2] [1] [head]

PMID-14960502[0] Event-related beta desynchronization in human subthalamic nucleus correlates with motor performance.

  • Asked 6 PD patients to play a game where they were warned to move / not to move.
  • Beta-frequency (20hz) power decreased prior to movement, with a time course correlated to reaction time.
    • This was followed by a late post-movement increase in beta power.
  • No-go trials showed a brief dip in beta power, with quick resumption.
  • conclude that:
    • the subthalamic nucleus is involved in the preparation of externally paced voluntary movements in humans
    • the degree of synchronization of subthalamic nucleus activity in the beta band may be an important determinant of whether motor programming and movement initiation is favored or suppressed. (hum, maybe).
  • found via Romulo's references; see the list of papers that cite it.

____References____

[0] Kühn AA, Williams D, Kupsch A, Limousin P, Hariz M, Schneider GH, Yarrow K, Brown P, Event-related beta desynchronization in human subthalamic nucleus correlates with motor performance.Brain 127:Pt 4, 735-46 (2004 Apr)

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ref: Foffani-2004.07 tags: STN motor preparation human 2003 basal_ganglia DBS SMA date: 01-26-2012 17:23 gmt revision:3 [2] [1] [0] [head]

PMID-15249649 Involvement of the human subthalamic nucleus in movement preparation

  • STN receives large afferent from SMA, so it should be involved in movement planning.
  • the STN and nearby structures are active before self-paced movements in humans.
  • normal patients show a negative EEG movement-related potential (MRP) starting 1-2 seconds before the onset of self-paced movements.
  • STN also shows premovement negative MRP.
    • REquire very sensitive methods to record this MRP -- it's on the order of 1 uv.
  • the amplitude of the scalp MRP is reduced in parkinson's patients.
    • impairment of movement preparation in PD may be related to deficits in the SMA and M1, e.g. underactivity.
    • the MRP is normalized with the administration of levodopa.
  • MPTP monkeys have increased activity in the STN
  • examined the role of the STN in movement preparation and inhibition via MRP recorded from DBS electrodes in the STN + simultaneously recorded scalp electrodes.
  • their procedure has the leads externalized during the first week after surgery.

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ref: Monakow-1978.11 tags: motor_cortex STN subthalamic nucleus anatomy DBS date: 01-26-2012 17:17 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-83239[0] Projections of the precentral motor cortex and other cortical areas of the frontal lobe to the subthalamic nucleus in the monkey.

  • this paper is old and important!
  • The ipsilateral subthalamic nucleus receives a moderately strong and somatotopic organized projection from Woolsey's precentral motor cortex (PMd, M1 i guess)
    • No projections from the postcentral gyrus! (S1) (Is this still thought to be true?)
  • The remaining nucleus is occupied by less intensive projections from premotor and prefrontal areas
  • STN is a convergence site for pallidal and cortical motor/frontal projections.
  • autoradiography slices are damn hard for me to read.

____References____

[0] Monakow KH, Akert K, Künzle H, Projections of the precentral motor cortex and other cortical areas of the frontal lobe to the subthalamic nucleus in the monkey.Exp Brain Res 33:3-4, 395-403 (1978 Nov 15)

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ref: Lehericy-2005.08 tags: fMRI motor_learning basal_ganglia STN subthalamic date: 01-25-2012 00:20 gmt revision:2 [1] [0] [head]

PMID-16107540[0] Distinct basal ganglia territories are engaged in early and advanced motor sequence learning

  • generally a broad, well-referenced study.
  • they used a really high-field magnet (3T) during tapping-learning task over the course of a month.
  • STN was activated early in motor learning, but not afterward, specifically the sequence learning
  • during the course of learning (an as the task became progressively more automatic) associative striatal activation shifted to motor activity.
    • STN could act by inhibiting competing motor outputs, thus building a temporally ordered sequence of movements.
  • SN was active throughout the course of the experiment.
  • during the 'fast learning' stage, there was transient activation of the ACC
  • also during the beginning portion of motor learning lobules V and VI of the cerebellum were activated.
  • rostral premotor and prefrontal cortical areas are connected to the associative territory of the striatum, which projects back to the frontal cortex the VA/VL nuclei of the thalamus.

____References____

[0] Lehéricy S, Benali H, Van de Moortele PF, Pélégrini-Issac M, Waechter T, Ugurbil K, Doyon J, Distinct basal ganglia territories are engaged in early and advanced motor sequence learning.Proc Natl Acad Sci U S A 102:35, 12566-71 (2005 Aug 30)

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ref: Breit-2006.1 tags: parkinsons basal_ganglia palladium substantia_nigra motor_control striate date: 01-24-2012 22:10 gmt revision:1 [0] [head]

I wish i could remember where i got these notes from, so as to verify the somewhat controversial statements. I found them written on the back of a piece of scrap paper.

  • neurophysiological recordings in animals show that over half of basal ganglia neurons fire in response to motor activity but none are triggered by passive limb movement.
  • in parkinson's disease (PD), the substantia nigra actually becomes pale to the eye.
  • stimulation of the striatum does not result in low-threshold movements like stimulation of the cortex does.
  • palladium does not seem linked to motor planning. (just execution?)
  • stimulation of the caudate causes movement, i.e. head turning, while stimulation of the ventromedial caudate produces arrest and crouching movements. (Delgado etc)
  • large bilateral striatal leasions cause inattention.
  • striatal units appear to signal movement, not generate/compute it (really?)
  • in parkinson's disease, motor learning appears normal - it is the initial slowness that is abnormal :: PD relates to the quality of movement, not the quality of the motor commands. Thus, perhaps PD is a disease of gating/attention?
  • in PD, all reflexes except the Hoffman-reflex appear normal.
    • The primary difference between the H-reflex and the spinal stretch reflex is that the H-reflex bypasses the muscle spindle and, therefore, is a valuable tool in assessing modulation of monosynaptic reflex activity in the spinal cord. The H-reflex is an estimate of alpha motoneuron ( alphaalpha MN) excitability when presynaptic inhibition and intrinsic excitability of the alphaalpha MNs remain constant.
  • A lesion of the PPN (pedunculo pontine nucleus) was shown to restore decreased activity levels in the SNr and STN of a rat model of parkinson's (lesion of the SNc) PMID-17042796

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ref: Brown-2001.12 tags: EMG ECoG motor control human coherence dopamine oscillations date: 01-19-2012 21:41 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-11765129[0] Cortical network resonance and motor activity in humans.

  • good review.
  • No coherence between ECoG and eMG below 12 Hz; frequency coherence around 18 Hz.
    • This seen only in high-resolution ECoG; lower resolution signals blurs the sharp peak.
  • Striking narrowband frequency of coherence.
  • ECoG - ECoG coherence not at same frequency as EMG-ECoG.
  • Marked task-dependence of these coherences, e.g. for wrist extension and flexion they observed similar EMG/ECoG coherences; for different tasks using the same muscles, different patterns of coherence.
  • Pyramidal cell discharge tends to be phase-locked to oscillations in the local field potential (Murthy and Fetz 1996)
    • All synchronization must ultimately be through spikes, as LFPs are not transmitted down the spinal cord.
  • Broadband coherence is pathological // they note it occurred during cortical myclonus (box 2)
  • Superficial chattering pyramidal cells (!!) firing bursts of frequency at 20 to 80 Hz, interconnected to produce spike doublets (Jefferys 1996).
  • Dopamine restores coherence between EMG and ECoG in a PD patient.

____References____

[0] Brown P, Marsden JF, Cortical network resonance and motor activity in humans.Neuroscientist 7:6, 518-27 (2001 Dec)

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ref: Evarts-1968.01 tags: Evarts motor control pyramidal tract M1 PTN tuning date: 01-16-2012 18:59 gmt revision:4 [3] [2] [1] [0] [head]

PMID-4966614[] Relation of pyramidal tract activity to force exerted during voluntary movement

  • PTNs with high conduction velocity tend to be silent during motor quiescence and show phasic activity with movement.
  • PTNs with lower axonal conduction velocities are active in the absence of movement; with movement they show both upward and downward modulations of the resting discharge.
  • many PTNs responded to a conditional stimulus before the movement.
  • in this study, they wanted to determine if phasic response was more correlated with displacement or with force.
    • did this with two different motions (flexion and extension) in two different force loads (opposing flexion and opposing extransion)
      • movements were slow (or at least nonballistic) and somewhat controlled - they had to last between 400 and 700ms.
      • monkeys usually carried out 3,000 cycles of the movement daily !!
  • "prior to the experiment, hte authour was biased to think that the displacement model (where the cortex commands a location/movement of the arm, which is then accomplished through feedback & feedforward mechanisms e.g. in the spinal cord) was correct; experimental results seem to indicate that force is very strongly represented in PTN population.
  • many PTN firing rates reflected dF/dt very strongly.
  • old, good paper. made with 'primitive' technology - but why do we need to redo this?

____References____

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ref: Wyler-1980.05 tags: operant control motor learning interspike intervals ISI Wyler Lange Neafsey Robbins date: 01-07-2012 21:46 gmt revision:1 [0] [head]

PMID-6769536[0] Operant control of precentral neurons: Control of modal interspike intervals

  • Question: can monkeys control the ISI of operantly controlled neurons?
    • Answer: Seems they cannot. Operant and overt movement cells have about the same ISI, and this cannot be changed by conditioning.
  • Task requires a change from tonic to phasic firing, hence they call it "Differential reinforcement of Tonic Patterns".
    • That is, the monkey is trained to produce spikes within a certain ISI window.
    • PDP8 control, applesauce feedback.
    • modal ISI, in this case, means mode (vs. mean and median) of the ISI.
  • Interesting: "It was not uncommon for a neuron to display bi- or trimodal ISI distributions when the monkey was engaged in a movement unrelated to a unit's firing"
  • For 80% of the units, the more tightly a neuron's firing was related to a specific movement, the more gaussian its ISI became.
  • As the monkey gained control over reinforced units, the ISI became more gaussian.
  • Figure 2: monkey was not able to significantly change the modal ISI.
    • Monkeys instead seem to succeed at the task by decreasing the dispersion of the ISI distribution and increasing the occurrence of the modal ISI.
  • Monkeys mediate response through proprioceptive feedback:
    • Cervical spinal cord sectioning decreases the fidelity of control.
    • When contralateral C5-7 ventral roots were sectioned, PTN responsive to passive arm movements could not be statistically controlled.
    • Thus, monkeys operantly control precentral neurons through peripheral movements, perhaps even small and isometric contractions.
  • Excellent paper. Insightful conclusions.

____References____

[0] Wyler AR, Lange SC, Neafsey EJ, Robbins CA, Operant control of precentral neurons: control of modal interspike intervals.Brain Res 190:1, 29-38 (1980 May 19)

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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. "

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ref: notes-0 tags: motor control BMI M1 date: 01-06-2012 03:11 gmt revision:13 [12] [11] [10] [9] [8] [7] [head]

with: {277}

  • Correlations have been described between neuronal activity and the static and dynamic forces and torques generated across single joints
  • or by the whole arm
  • or by precision pinch [14,15,16]
  • or there are strong correlations to muscle activity [17,18,19,20,21,22,23,24,25]
  • or there is strong correlations to kinematic parameters
  • these kinematic parameters are dependent on location in the external workspace [10][28,29]
  • kinematic tuning can be subserved by training! [30]
  • distance to target representation [31]

____References____

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ref: tlh24-2011 tags: motor learning models BMI date: 01-06-2012 00:19 gmt revision:1 [0] [head]

Experiment: you have a key. You want that key to learn to control a BMI, but you do not want the BMI to learn how the key does things, as

  1. That is not applicable for when you don't have training data - amputees, parapalegics.
  2. That does not tell much about motor learning, which is what we are interested in.

Given this, I propose a very simple groupweight: one axis is controlled by the summed action of a certain population of neurons, the other by a second, disjoint, population; a third population serves as control. The task of the key is to figure out what does what: how does the firing of a given unit translate to movement (forward model). Then the task during actual behavior is to invert this: given movement end, what sequence of firings should be generated? I assume, for now, that the brain has inbuilt mechanisms for inverting models (not that it isn't incredibly interesting -- and I'll venture a guess that it's related to replay, perhaps backwards replay of events). This leaves us with the task of inferring the tool-model from behavior, a task that can be done now with our modern (though here-mentioned quite simple) machine learning algorithms. Specifically, it can be done through supervised learning: we know the input (neural firing rates) and the output (cursor motion), and need to learn the transform between them. I can think of many ways of doing this on a computer:

  1. Linear regression -- This is obvious given the problem statement and knowledge that the model is inherently linear and separable (no multiplication factors between the input vectors). n matlab, you'd just do mldivide (backslash opeartor) -- but but! this requires storing all behavior to date. Does the brain do this? I doubt it, but this model, for a linear BMI, is optimal. (You could extend it to be Bayesian if you want confidence intervals -- but this won't make it faster).
  2. Gradient descent -- During online performance, you (or the brain) adjusts the estimates of the weights per neuron to minimize error between observed behavior and estimated behavior (the estimated behavior would constitute a forward model..) This is just LMS; it works, but has a exponential convergence and may get stuck in local minima. This model will make predictions on which neurons change relevance in the behavior (more needed for acquiring reward) based on continuous-time updates.
  3. Batched Gradient descent -- Hypothetically, one could bolster the learning rate by running batches of data multiple times through a gradient descent algorithm. The brain very well could offline (sleep), and we can observe this. Such a mechanism would improve performance after sleep, which has been observed behaviorally in people (and primates?).
  4. Gated Gradient Descent -- This is halfway between reinforcement learning and gradient descent. Basically, the brain only updates weights when something of motivational / sensory salience occurs, e.g. juice reward. It differs from raw reinforcement learning in that there is still multiplication between sensory and motor data + subsequent derivative.
  5. Reinforcement learning -- Neurons are 'rewarded' at the instant juice is delivered; they adjust their behavior based on behavioral context (a target), which presumably (given how long we train our keys), is present in the brain at the same time the cursor enters the target. Sensory data and model-building are largely absent.

{i need to think more about model-building, model inversion, and songbird learning?}

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ref: Evarts-1969.05 tags: Evarts pyramidal tract motor control M1 tuning date: 01-03-2012 23:08 gmt revision:2 [1] [0] [head]

PMID-4977837[0] Activity of Pyramidal Tract neurons during postural fixation

  • Force was thus dissociated from displacement, and it was possible to determine whether PTN discharges were related to position or force.
  • for the majority of PTNs discharge frequency was related to to the magnitude and rate of change of force rather than to the joint position or the speed of joint movement (same as the MUA in the Kinarm data!!)
  • task was simple: just try to avoid joint movement.
  • in comparison to [1] where PTN were related to force under joint displacement, this task shows they are still related to force even when the joint angle is fixed.
  • used sharpened tungsten electrodes to record 102 pyramidal tract neurons.
  • monkeys were trained to do the tasks in their home cages (obviously weren't recorded there - need to be headposted)
  • I'm not sure how he determined if it was or was not a pyramidal tract neuron.

____References____

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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____

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ref: Olson-2005 tags: Arizona rats BMI motor control training SVM single-unit left right closed-loop learning Olson Arizona date: 01-03-2012 23:06 gmt revision:1 [0] [head]

bibtex:Olson-2005 Evidence of a mechanism of neural adaptation in the closed loop control of directions

  • from abstract:
    • Trained rats to press left/right paddles to center a LED. e.g. paddles were arrow keys, LED was the cursor, which had to be centered. Smart rats.
      • Experiment & data from Olson 2005
    • Then trained a SVM to discriminate left/right from 2-10 motor units.
    • Once closed-loop BMI was established, monitored changes in the firing properties of the recorded neurons, specifically wrt the continually(?) re-adapted decoding SVM.
    • "but expect that the patients who use the devices will adapt to the devices using single neuron modulation changes. " --v. interesting!
  • First page of article has an excellent review back to Fetz and Schmidt. e.g. {303}
  • Excellent review of history altogether.
    • Notable is their interpretation of Sanchez 2004 {259}, who showed that most of the significant modulations are from a small group of neurons, not the large (up to 320 electrodes) populations that were actually recorded. Carmena 2003 showed that the population as a whole tended to group tuning, although this was imperfectly controlled.
  • Also reviewed: Zacksenhouse 2007 {901}
  • SVM is particularly interesting as a decoding algorithm as it weights the input vectors in projecting onto a decision boundary; these weights are experimentally informative.
  • Figure 7: The brain seems to modulate individual firing rate changes to move away from the decision boundary, or at least to minimize overlap.
  • For non-overt movements, the distance from decision function was greater than for overt movements.
  • Rho ( ρ\rho ) is the Mann-Whitney test statistic, which non-parametrically estimates the difference between two distributions.
  • δf(X t)\delta f(X_t) is the gradient wrt the p input dimensions o9f the NAV, as defined with their gaussian kernel SVM.
  • They show (i guess) that changes in ρ\rho are correlated with the gradient -- e.g. the brain focuses on neurons that increase fidelity of control?
    • But how does the brain figure this out??
  • Not sure if i fully understand their argument / support.
  • Conclusion comes early in the paper
    • figure 5 weakly supports the single-neuron modulation result.

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ref: Penfield-1937 tags: Penfield 1937 motor cortex stimulation ICMS human neurosurgery electrodes date: 01-03-2012 22:08 gmt revision:3 [2] [1] [0] [head]

No PMID / bibtex penfield-1937. Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation

  • Fritsch and Hitzig (1870) [0] cited as the first paper in electrical excitation of the CNS.
  • Good review of the scientific experiments thereafter, including stimulation to S1 by Ferrier, work with apes etc.
  • Central sulcus called the 'Rolandic fissure'.
  • Interesting! quote:

The account of Bartholow (1874) is interesting to say the least and may be cited. His patient was a 30-year old-domestic. As an infant this unfortunate had chanced to fall into the fire, burning her scalp so badly that " hair was never reproduced." A piece of whale bone in the wig she was forced to wear irritated the scarred scalp and, by her statement, three months before she was admitted, an ulcer appeared. When she presented herself for relief, this had eroded the skull over a space 2 in. in diameter " where the pulsations of the brain are plainly seen." Although " rather feeble-minded " Bartholow observed that Mary returned replies to all questions and no sensory or motor loss could be made out in spite of the fact that brain substance apparently had been injured in the process of evacuation of pus from the infected area. The doctor believed, therefore, that fine insulated needles could be introduced without further damage.

While the electrodes were in the right side Bartholow decided to try the effect of more current. ' Her countenance exhibited great distress and she began to cry. Very soon the left hand was extended as if in the act of taking hold of some object in front of her; the arm presently was agitated with clonic spasms ; her eyes became fixed with pupils widely dilated ; the lips were blue and she frothed at the mouth ; her breathing became stertorous, she lost conscious-ness and was violently convulsed on the left side. This convulsion lasted for five minutes and was succeeded by coma. She returned to consciousness in twenty minutes from the beginning of the attack and complained of some weakness and vertigo." Three days after this stimulation, following a series of right-sided seizures, the patient died.

  • Relatively modern neurosurgical procedures.
  • They observe changes to blood circulation prior epileptic procedures. wow!
  • Very careful hand-drawn maps of what they have observed. Important, as you'll probably never get this trough an IRB. It pays to be meticulous.

____References____

[0] Fritsch G, Hitzig E, Electric excitability of the cerebrum (Uber die elektrische Erregbarkeit des Grosshirns).Epilepsy Behav 15:2, 123-30 (2009 Jun)

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ref: Aflalo-2007.03 tags: Graziano motor cortex M1 SUA macaque monkey electrophysiology tuning date: 01-03-2012 03:37 gmt revision:1 [0] [head]

PMID-17360898[] Relationship between Unconstrained Arm Movements and Single-Neuron Firing in the Macaque Motor Cortex

  • the best explanation of neuronal firing was the final mulijoint configuration of the arm - it accounted for 36% of the SUA variance.
  • the search for the 'correct' motor parameter (that neurons are tuned to) is an ill-posed experimental question because motor parameters are very intercorrelated.
  • they knock experiments in which the animals are overtrained & the movements limited - and they are right!
  • single electrode recording with cronically implanted steel chamber - e.g. it took a damn long time!
    • imaged the central sulcus through the dura.
    • verified location with single unit responses to palpation of the contralateral hand/arm (in S1) & microstimulation-evoked movements in M1.
  • used optotrak to measure the position of the monkey.
  • occasionally, the monkey attemptted to scratch the experimenter with fast semi-ballistic arm movement. heh. :)
  • movements were seprarated based on speed analysis - that is, all the data were analyzed as discrete segments.
  • neurons were inactive during periods of hand stasis between movements.
  • tested the diversity of their training set in a clever way: they simulated neurons tuned to various parameters of the motion, and tested to see if their analysis could recover the tuning. it could.
    • however, they still used unvalidated regression analysis to test their hypothesis. regression analysis estimates how much variance is estimated by the cosine-tuning model - it returns an R^2.
  • either averaged the neuronal tuning over an entire movement or smoothed the firing rate using a 10hz upper cutoff.
  • Moran & Schwartz' old result seems to be as much a consequence of averaging across trials as it is a consequence of actual tuning...
    • whithout the averaging, only 3% of the variance could be attributed to speed tuning.
  • i think that they have a good point in all of this: when you eliminate sources of variance (e.g. starting position) from the behavior, either by mechanical restraint or simple omission of segments or even better averaging over trials, you will get a higher R^2. but it may be false, a compression of the space along an axis where they are not well correlated!
  • a model in which the final position matters little, but the velocity used to get there does, has been found to account for little of the neuronal variance.
    • instead, neurons are tuned to any of a number of movements that terminate near a preferred direction.
  • observational studies of of the normal psontaneous behavior of monkeys indicate that a high proportion of time is spent using the arm as a postural device.
    • therefore, they expect that neurons are tuned to endpoint posture.
    • modeled the neuronal firing as a gaussian surface in the 8-dimensional space of the arm posture.
  • in comparison to other studies, the offset between neural activity and behavior was not significantly different, over the entire population of recorded neurons, from zero. This may be due to the nature of the task, which was spontaneous and ongoing, not cue and reaction based, as in many other studies.
    • quote: This result suggests that the neuronal tuning to posture reflects reatively more and anticipation of the future state of the limb rather than a feedback signal about a recent state of the limb.

____References____

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ref: Moran-1999.11 tags: electrophysiology motor cortex Schwartz Moran M1 tuning date: 01-03-2012 03:36 gmt revision:2 [1] [0] [head]

PMID-10561437[0] Motor cortical representation of speed and direction during reaching

  • velocity is represented in the motor cortex.
  • they developed an equation relating firing rate to the position and velocity.
  • EMG direction had significantly different tuning from the cortical activity
    • the effect of speed on EMG was also different.
  • used single-electrode recording - 1,066 cells!!
  • introduce the square-root transformation of the firing rate (from Ashe and Georgopolous 1994)

____References____

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ref: Fu-1993.11 tags: electrophysiology Ebner premotor motor tuning M1 date: 01-03-2012 03:34 gmt revision:1 [0] [head]

PMID-8294972 Neuronal specification of direction and distance during reaching movements in the superior precentral premotor area and primary motor cortex of monkeys. 1993

  • trained monkey to do center-out task, 48 targets (8 angles, 6 distances).
  • single-electrode recording of 197 neurons in the primary motor and secondary motor / premotor (in the superior precentral sulcus).
  • cells were mostly tuned to direction, and less to distance, in both the premovement and movement periods. distance tuning was much stronger in the movement period.
    • tuning was measure by average firing rate for the premovement, movement, and total periods.
  • long, very detailed!

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ref: Donoghue-1990.01 tags: Donoghue Suner Sanes rat motor cortex reorganization M1 tuning surprising date: 01-03-2012 03:30 gmt revision:4 [3] [2] [1] [0] [head]

PMID-2340869[0] Dynamic organization of primary motor cortex output to target muscles in adult rats. II. Rapid reorganization following motor nerve lesions.

  1. Map out the motor cortex into vibrissa and forelimb areas using ICMS.
  2. Implant a simulating electrode in the vibrissa motor cortex.
  3. Implant EMG electrodes in the forearm.
  4. Sever the buccal and mandibular branches of the facial nerve.
  5. stimulate, and wait for forearm EMG to be elicited by ICMS. Usually occurs! Why? Large horizontal axons in motor cortex? Uncovering of silent synapses, and homeostatic modulation of firing rates?

____References____

[0] Donoghue JP, Suner S, Sanes JN, Dynamic organization of primary motor cortex output to target muscles in adult rats. II. Rapid reorganization following motor nerve lesions.Exp Brain Res 79:3, 492-503 (1990)

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ref: Fontani-2007.12 tags: mental training skilled motor control date: 01-03-2012 02:33 gmt revision:2 [1] [0] [head]

PMID-18229536[0] Effect of mental imagery on the development of skilled motor actions.

  • with trained subjects (performing something called Ura-Shuto-Uchi (Japanese? but the researchers are Italian)) showed a decrease in reaction time and EMG activity, as well as a increase in movement speed, muscle strength, power, and work. These results did not apply to untrained individuals. EEG also apparently changed vs. the untrained condition.

____References____

[0] Fontani G, Migliorini S, Benocci R, Facchini A, Casini M, Corradeschi F, Effect of mental imagery on the development of skilled motor actions.Percept Mot Skills 105:3 Pt 1, 803-26 (2007 Dec)

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

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

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

____References____

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ref: Lemon-1976.1 tags: Lemon motor recording afferent input date: 01-03-2012 01:01 gmt revision:1 [0] [head]

PMID-11491[0] Afferent input to movement-related precentral neurones in conscious monkeys.

  • Trained monkeys to make both a stereotyped movement and respond passively and calmly to external stimulation.
  • Most cells recorded responded to joint velocity; none to joint position.
  • A smaller subset responded to muscle palpitation
  • Cells were tuned to similar things as their neighbors, though sometimes they responded to markedly different stimuli. Consistent with Wyler.

____References____

[0] Lemon RN, Porter R, Afferent input to movement-related precentral neurones in conscious monkeys.Proc R Soc Lond B Biol Sci 194:1116, 313-39 (1976 Oct 29)

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ref: Atallah-2007.01 tags: striatum skill motor learning VTA substantia nigra basal ganglia reinforcement learning date: 12-31-2011 18:59 gmt revision:3 [2] [1] [0] [head]

PMID-17187065[0] Separate neural substrates for skill learning and performance in the ventral and dorsal striatum.

  • good paper. via SCLin's blog. slightly confusing anatomical terminology.
  • tested in rats, which has a anatomically different basal ganglia system than primates.
  • Rats had to choose which driection in a Y maze based on olfactory cues. Normal rats figure it out in 60 trials.
  • ventral striatum (nucleus accumbens here in rats) connects to the ventral prefrontal cortices (for example, the orbitofrontal cortex)
    • in primates, includes the medial caudate, which has been shown in fMRI to respond to reward prediction error. Neural activity in the caudate is attenuated when a monkey reaches optimal performance.
  • dorsal parts of the striatum (according to web: caudate, putamen, globus pallidus in primates) connect to the dorsal prefrontal and motor cortices
    • (according to them:) this corresponds to the putamen in primates. Activity in the putamen reflects performance but not learning.
    • activity in the putamen is highest after successful learning & accurate performance.
  • used muscimol (GABAa agonist, silences neural activity) and AP-5 (blocks NMDA based plasticity), in each of the target areas.
  • dorsal striatum is involved in performance but not learning
    • Injection of muscimol during acquisition did not impair test performance
    • Injection of muscimol during test phase did impair performance
    • Injection of AP-5 during acquisition had no effect.
    • in acquisition sessions, muscimol blocked instrumental response (performance); but muscimol only has a small effect when it was injected after rats perfected the task.
      • Idea: consistent behavior creates a stimulus-response association in extrastriatal brain areas, e.g. cerebral cortex. That is, the basal ganglia is the reinforcement signal, the cortex learns the association due to feedback-driven behavior? Not part of the habit system, but make and important contribution to goal-directed behavior.
      • This is consistent with the observation that behavior is initially goal driven but is later habitual.
    • Actually, other studies show that plasticity in the dorsal striatum may be detrimental to instrumental learning.
    • The number of neurons that fire just before the execution of a response is larger in the putamen than the caudate.
  • ventral striatum is involved in learning and performance.
    • Injection of AP-5 or muscimol during acquisition (learning behavior) impairs test performance.
    • Injection of AP-5 during test performance has no effect , but muscimol impairs performance.
  • Their data support an actor-director-critic architecture of the striatum:
    • Actor = dorsal striatum; involved in performance, but not in learning them.
    • Director = ventral striatum; quote "it somehow learns the relevant task demands and directs the dorsal striatum to perform the appropriate action plans, but, crucially, it does not train the dorsal striatum"
      • ventrai striatum acts through the orbitofrontal cortex that mantains representations of task-reward contingencies.
      • ventral striatum might also select action selection through it's projections to the substantia nigra.
    • Critic = dopaminergic inputs from the ventral tegmental area and substantia nigra.

____References____

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ref: -0 tags: Scott M1 motor control pathlets filter EMG date: 12-22-2011 22:52 gmt revision:1 [0] [head]

PMID-19923243 Complex Spatiotemporal Tuning in Human Upper-Limb Muscles

  • Original idea: M1 neurons encode 'pathlets', sophisticated high-level movement trajectories, possibly through the action of both the musculoskeletal system and spinal cord circuitry.
  • Showed that muscle pathlets can be extracted from EMG data, relkiably and between patients, implying that M1 reflects 'filter-like' properties of the body, and not high level representations.

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ref: -0 tags: Moran Schwartz Todorov controversy PV M1 motor control date: 12-22-2011 22:04 gmt revision:0 [head]

PMID-11017157 One motor cortex, two different views.

  • Commentary on {950}
  • Refutes Todorov's stiff -muscle perturbation analysis, saying that it grossly misapproximates what the monkey is actually doing (drawing on a touchscreen vertical in front of it), as the model of the arm in this case would be held stiffly in front of the monkey, rather than realistically falling to the animal's side.
  • They also claim that any acceleration term would cause the PV tuning to lead with higher curvature, which is not what they saw (?)

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ref: -0 tags: Todorov motor control models 2000 date: 12-22-2011 21:18 gmt revision:3 [2] [1] [0] [head]

PMID-10725930 Direct cortical control of muscle activation in voluntary arm movements: a model.

  • Argues that the observed high-level control of parameters (movement direction) is inconsistent with demonstrated low-level control (control of individual muscles / muscle groups, as revealed by STA [5] or force production [3]), but this inconsistency is false: the principle of low level control is correct, and high level control appears due to properties of the musculoskeletal system.
  • "Yet the same cells that encode hand velocity in movement tasks can also encode the forces exerted against external objects in both movement and isometric tasks [9,10].
  • The following other correlations have been observed:
    • arm position [11]
    • acceleration [12]
    • movement preparation [13]
    • target position [14]
    • distance to target [15]
    • overall trajectory [16]
    • muscle coactivation [17]
    • serial order [18]
    • visual target position [19]
    • joint configuration [20]
    • instantaneous movement curvature [7]
    • time from movement onset [15]
  • although these models can fit the data well, they leave a crucial question unanswered, namely, how such a mixed signal can be useful for generating motor behavior.
    • What? No! The diversity of voices gives rise to robust, dynamic computation. I think this is what Miguel has written about, will need to find a reference.
  • Anyway, all the motor parameters are related by the laws of physics -- the actual dimensionality of real reaches is relatively low.
  • His model: muscle activity simply reflects M1 PTN activity.
  • If you include real muscle parameters, a lot of the observed correlations make sense: muscle force depends not only on activation, but also on muscle length and rate of change of length.
  • In this scientific problem, the output (motor behavior) specified by the motor task is easily measured, and the input (M1 firing) must be explained.
    • Due to the many-to-one mapping, there is a large null-space of the inverse transform, so individual neurons cannot be predicted. Hence focus on population vector average.
  • Cosine tuning is the only activation pattern that minimizes neuromotor noise (derived in methods, Parseval's theorem)). Hence he uses force, velocity, and displacement tuning for his M1 cells.
  • Activity of M1 cells is constrained in endpoint space, hence depends only on behavioral parameters.
    • The muscles were "integrated out".
  • Using his equation, it is clear that for an isometric task, M1 activity is cosine tuned to force direction and magnitude -- x(t) is constant.
  • For hand kinematics in the physiological range with an experimentally measured inertia-to-damping ratio, the damping compensation signal dominates the acceleration signal.
    • Hence population x˙(t)\propto \dot x(t)
    • Muscle damping is asymmetric: predominant during shortening.
  • The population vector ... is equal not to the movement direction or velocity, but instead to the particular sum of position, velocity, acceleration, and force signals in eq. 1
  • PV reconstruction fails when movement and force direction are varied independently. [28]
  • Fig 4. Schwartz' drawing task -- {951} -- and shows how curvature, naturalistic velocity profiles, the resultant accelerations, and leading neuronal firing interact to distort the decoded PV.
    • Explains why, when assuming PV tuning, there seems to be variable M1-to-movement delay. At high curvature PV tuning can apprently lag movement. Impossible!
  • Fig 5 reproduces [21]
    • Mean firing rate (mfr, used to derive the poisson process spike times) and r^2 based classification remarkably different -- smoothing + square root biases toward finding direction-tuned cells.
    • Plus, as P, V, and A are all linearly related, a sum of the 3 is closer to D than any of the three.
    • "Such biases raise the important question of how one can determine what an individual neuron controls"
  • PV reversals occur when the force/acceleration term exceeds the velocity scaling term -- which is 'equivalent' to the triphasic burst pattern observed in EMG. Ergo monkeys should be trained to make faster movements.
  • The structure of your model -- for example firingrate=b 0+b xX+b yY+b mMfiringrate = b_0 + b_x X + b_y Y + b_m M biases analysis for direction, not magnitude; correct model is firingrate=b 0+b xmXM+b ymYM firingrate = b_0 + b_{xm}XM + b_{ym}YM -- multiplicative.
  • "Most of these puzzling phenomena arise from the feedforward control of muscle viscoelasticity."
  • Implicit assumption is that for the simple, overtrained, unperturbed movements typically studied, feedforward neural control is quite accurate. When you get spinal reflexes involved things may change. Likewise for projections from the red nucleus.

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ref: -0 tags: Georgopoulos population vector arm motor control date: 12-20-2011 22:26 gmt revision:1 [0] [head]

PMID-3139485 Neural integration of movement: role of motor cortex in reaching.

  • Reviews his 2D and 3D population vector / cosine tuning results.
  • Isometric task in [13] varied as a sinusoidal function of load.
    • [1]3 Kalaska 1985 Area 4 and area 5: differences between the load direction-dependent discharge variability of cells during active postural fixation.
  • [14] suggests that separate motor cortical populations are concerned with the control of joint stiffness.
    • [14] Humphrey 1983 Seperate cortical systems for control of joint movement and joint stiffness: reciprocal activation and coactivation of antagonist muscles.
  • proximal muscles are controlled through C3-C4 propriospinal neurons, which receive input from corticospinal, rubrospinal, reticulospinal, and tectospinal tracts, and distribute axons to proximal motorneuron pools [25]
    • The propriospinal system seems to be selectively engaged during reaching movements [28].
    • There is corticspinal input on key inhibitory interneuron that mediates inhibition from afferent fibers to propriospinal neurons [29].
    • References in this from the cat.
  • This is similar to 'the sophisticated integration seen in the locomotor system' locomotive system.
  • From this, Georgopoulos supposes that the motor cortex is concerned with the specification of the direction of reaching in space.
  • He further supposes that this is enacted by individual motor cortical cells influence motoneuronal pools in a weighted fashion.
  • Looking back, I'm surprised at how clean his PV tuning plots are -- the neurons stop fiting when the monkey moves his arm in certain directions.

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ref: Schwartz-1988.08 tags: Georgopoulos 1988 motor coding cortex population vector date: 12-20-2011 00:49 gmt revision:3 [2] [1] [0] [head]

PMID-3411361[0] Primate motor cortex and free arm movements to visual targets in three-dimensional space. I. Relations between single cell discharge and direction of movement.

  • 475/568 (83%) of cells varied in an orderly fashion with movement -- tuned to a movement direction.
    • As before, binned the firing based on movement direction.
  • generalize 2-D results [1][2]
  • Totally awesome tracking system: a spark gap was attached to the monkey's wrist and was discharged every 20ms. The sonic signal was picked up by at least 3 of the 8 ultrasonic recievers placed at the corners of the workspace and the xyz coordinates were calculated from the sonic delays using a microprocessor-based system.
  • monkey(s) had to press lighted buttons (arcade buttons) within this workspace.
  • otherwise same materials / methods as before.
  • every effort was made to isolate initially negative-going action potentials, and indication that the neuron was less likely to be damaged.
    • fiber spikes are initially positive. Cite Mountcastle et al 1969.
  • EMG signals gained 3000 and bandpassed 100-500Hz. rather narrow, but normal I guess.
  • Neural data recorded as interspike intervals.
  • vectoral dot-product tuning of cells, with the coeficients set by multiple linear regression.
    • This is equivalent to cosine tuning.
  • rather complicated CUSUM for determining onset of activity - including inhibition.
  • as in the earlier study, 60% of cells were tuned in the reaction time, and 85% within the movement time.
  • EMG activity looks like it can be described with cosine tuning as well.
  • 3D tuning directed over the whole space.
  • Residuals of firing rates measured with respect to the tuning functions; residuals were mean zero and approximately the same spread, and were distributed equally over the 3D space.
  • movement latency about 300ms. pretty quick reaction time?
  • Got some pretty awesome graphics for 1986 :)
  • The discharge rate of motor cortical cells varies with the magnitude of force and that cells with higher thresholds are recruited at progressively higher forces (Hepp-Reymond et al 1978).
  • Murphy et al 1982 found that ICMS to M1 caused rotation about single joints, which is inconsistent with cosine tuning (would require complex tuning, or tuning to joints).
  • They argue that cosine tuning refects transformatino by the propriospinal system, which engages patterns of muscle activity.
    • Most PTNs can influence several motoneuron pools in the spinal cord. (Fetz and Finocchio 1975, Fetz and Cheney 1978, 1980 ... Lemon 1986, Cheney and Fetz 1985)
    • Suggest that PTNs related to the weighted combinations of muscles.

____References____

[0] Schwartz AB, Kettner RE, Georgopoulos AP, Primate motor cortex and free arm movements to visual targets in three-dimensional space. I. Relations between single cell discharge and direction of movement.J Neurosci 8:8, 2913-27 (1988 Aug)
[1] Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT, On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex.J Neurosci 2:11, 1527-37 (1982 Nov)
[2] Thach WT, Correlation of neural discharge with pattern and force of muscular activity, joint position, and direction of intended next movement in motor cortex and cerebellum.J Neurophysiol 41:3, 654-76 (1978 May)

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ref: Georgopoulos-1982.11 tags: Georgopoulos 1982 motor tuning cortex M1 population vector date: 12-19-2011 23:52 gmt revision:1 [0] [head]

PMID-7143039 On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex.

  • eight directions 45deg intervals, 2D joystick, frictionless, LED tarkets in a blocked randomized experimental design.
    • MK made simultaneous saccades; saccade latency 150-170ms.
      • some motor cells responded to visual movement.
    • EMG activity began ~80ms before movement.
    • monkeys used both arms.
  • bell-shaped or cosine tuning in 75% of the cells.
    • This has also been described in the saccade system in the paramedian pontine reticular formation (Henn and Cohen 1976), the mesencelphatic reticular formation (Buttner eta la 1977) and the internal medullary lamina of the thalamus (Schlag and Schlag-Ney 1977)
  • cells tended to cluster by tuning in depth.
  • cells tended to respond to movement & small corrections to movement, but did not necessarily respond to non-task related movement. "Yet these same cells were frequently silent during other movements which also involved contraction of the same muscles [as used in the task]"
  • cell discharge was much stronger during active movements than during passive manipulations.
  • 64% of cells were activated before the earliest EMG changes; 87% before the onset of movement.
  • The famous one, where the population vector was formalized / conceived / validated.
  • most neurons begin firing ~ 100ms before movement begins.
  • useda PDP11/20 minicomputer to control the LEDs & data recording.
  • Thach 1978 -- approxmately equal proportions of motor cortical cells were related to muscle activity, hans position, and direction of intended movement Thach 1978) PMID-96223
  • single electrode Pt/Ir recording 2-3Mohm; recordings made for 6-7 hours.
  • cite georgopoulos 1983 -- they propose distributed population coding.
  • point out that the central problem -- upon which some progress has been made - is the translation between visual and motor coordinate frames.

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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____

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ref: Vijayakumar-2005.12 tags: schaal motor learning LWPL PLS partial least sqares date: 12-07-2011 04:09 gmt revision:1 [0] [head]

PMID-16212764[0] Incremental online learning in high dimensions

ideas:

  • use locally linear models.
  • use a small number of regressions in selected dimensions of input space in the spirit of partial least squares regression. (like partial least-squares) hence, can operate in very high dimensions.
  • function to be approximated has locally low-dimensional structure, which holds for most real-world data.
  • use: the learning of of value functions, policies, and models for learning control in high-dimensional systems (like complex robots or humans).
  • important distinction between function-approximation learning:
    • methods that fit nonlinear functions globally, possibly using input space expansions.
      • gaussian process regression
      • support vector machine regression
        • problem: requires the right kernel choice & basis vector choice.
      • variational bayes for mixture models
        • represents the conditional joint expectation, which is expensive to update. (though this is factored).
      • each above were designed for data analysis, not incremental data. (biology is incremental).
    • methods that fit simple models locally and segment the input space automatically.
      • problem: the curse of dimensionality: they require an exponential number of models for accurate approximation.
        • this is not such a problem if the function is locally low-dim, as mentioned above.
  • projection regression (PR) works via decomposing multivariate regressions into a superposition of single-variate regressions along a few axes of input space.
    • projection pursuit regression is a well-known and useful example.
    • sigmoidal neural networks can be viewed as a method of projection regression.
  • they want to use factor analysis, which assumes that the observed data is generated from a low-dimensional distribution with a limited number of latent variables related to the output via a transformation matrix + noise. (PCA/ wiener filter)
    • problem: the factor analysis must represent all high-variance dimensions in the data, even if it is irrelevant for the output.
    • solution: use joint input and output space projection to avoid elimination of regression-important dimensions.
----
  • practical details: they use the LPWR algorithm to model the inverse dynamics of their 7DOF hydraulically-actuated gripper arm. That is, they applied random torques while recording the resulting accelerations, velocities, and angles, then fit a function to predict torques from these variables. The robot was compliant and not very well modeled with a rigid body model, though they tried this. The resulting LPWR generated model was 27 to 7, predicted torques. The control system uses this functional approximation to compute torques from desired trajectories, i think. The desired trajectories are generated using spline-smoothing ?? and the control system is adaptive in addition to the LPWR approximation being adaptive.
  • The core of the LPWR is partial-least squares regression / progression pursuit, coupled with gaussian kernels and a distance metric (just a matrix) learned via constrained gradient descent with cross-validation. The partial least squares (PLS) appears to be very popular in many fields, and there are an number of ways of computing it. Distance metric can expand without limit, and overlap freely. Local models are added based on MSE, i think, and model adding stops when the space is well covered.
  • I think this technique is very powerful - you separate the the function evaluation from the error minimization, to avoid the problem of ambiguous causes. Instead, when applying the LPWR to the robot, the torques cause the angles and accelerations -> but you invert this relationship: want to control the torques given trajectory. Of course, the whole function approximation is stationary in time - the p/v/a is sufficient to describe the state and the required torques. Does the brain work in the same way? do random things, observe consequences, work in consequence space and invert ?? e.g. i contracted my bicep and it caused my hand to move to the face; now I want my hand to move to my face again, what caused that? Need reverse memory... or something. Hmm. let's go back to conditional learning: if any animal does an action, and subsequently it is rewarded, it will do that action again. if this is conditional on a need, then that action will be performed only when needed.. when habitual, the action will be performed no matter what.. this is the nature of all animals, i think, and corresponds to rienforcement learning? but how? I suppose it's all about memory, and assigning credit where credit is due. the same problem is dealt with rienforcement learning. and yet things like motor learning seem so far out of this paradigm - they are goal-directed and minimize some sort of error. eh, not really. Clementine is operating on the conditioned response now - has little in the way of error. but gradually this will be built; with humans, it is built very quickly by reuse of existing modes. or conciousness.
  • back to the beginning: you dont have to regress into output space - you regress into sensory space, and do as much as possible in that sensory space for control. this is very powerful, and the ISO learning people (Porr et al) have effectively discovered this: you minimize in sensory space.
    • does this abrogate the need for backprop? we are continually causality-inverting machines; we are prredictive.

____References____

[0] Vijayakumar S, D'Souza A, Schaal S, Incremental online learning in high dimensions.Neural Comput 17:12, 2602-34 (2005 Dec)

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ref: Wyler-1980.08 tags: Wyler Lange Robbins operant conditioning motor neurons contralateral bilateral specificity monkeys motor learning date: 12-06-2011 06:36 gmt revision:1 [0] [head]

PMID-6772272 Operant control of precentral neurons: bilateral single unit conditioning.

  • Used bilateral electrodes.
  • One neuron operantly conditioned, one not.
  • Switched the conditioned / controlled after performance was attained.
  • Evidence: neurons can be individually tuned, and operant control is not the result of spinal-level conditioning or change.
    • It is not the result of increased attention or increased muscle tone.
  • Simple question, simple paper.

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ref: Carlton-1981.1 tags: visual feedback 1981 error correction movement motor control reaction time date: 12-06-2011 06:35 gmt revision:1 [0] [head]

PMID-6457106 Processing visual feedback information for movement control.

  • Vusual feedback can correct movement within 135ms.
  • Measured this by simply timing the latency from presentation of visual error to initiation of corrective movement.

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ref: Gandolfo-2000.02 tags: Gandolfo Bizzi dynamic environment force fields learning motor control MIT M1 date: 12-02-2011 00:10 gmt revision:1 [0] [head]

PMID-10681435 Cortical correlates of learning in monkey adapting to a new dynamical environment.

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ref: Burnod-1982.11 tags: operant conditioning motor control learning Burnod Maton Calvet date: 11-26-2011 02:22 gmt revision:0 [head]

PMID-7140894 Short-term changes in cell activity of areas 4 and 5 during operant conditioning.

  • Seems that layers 4 and 5 act differently during operant conditioning of a simple task.
  • Layer 5 neurons become tuned to reward (?)
  • Can't get this article, have to go from the abstract.

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ref: Shadmehr-1997.01 tags: Shadmehr human long term memory learning motor M1 cortex date: 03-25-2009 15:29 gmt revision:2 [1] [0] [head]

PMID-8987766[0] Functional Stages in the Formation of Human Long-Term Motor Memory

  • We demonstrate that two motor maps may be learned and retained, but only if the training sessions in the tasks are separated by an interval of ~5 hr.
  • Analysis of the after-effects suggests that with a short temporal distance, learning of the second task leads to an unlearning of the internal model for the first.
  • many many citations!

____References____

[0] Shadmehr R, Brashers-Krug T, Functional stages in the formation of human long-term motor memory.J Neurosci 17:1, 409-19 (1997 Jan 1)

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ref: BrashersKrug-1996.07 tags: motor learning sleep offline consolidation Bizzi Shadmehr date: 03-24-2009 15:39 gmt revision:1 [0] [head]

PMID-8717039[0] Consolidation in human motor memory.

  • while practice produces speed and accuracy improvements, significant improvements - ~20% also occur 24hours later following a period of sleep. Why is this? We can answer it with the recording system!

____References____

[0] Brashers-Krug T, Shadmehr R, Bizzi E, Consolidation in human motor memory.Nature 382:6588, 252-5 (1996 Jul 18)

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ref: Debarnot-2009.03 tags: sleep motor imagery practice date: 03-24-2009 15:32 gmt revision:3 [2] [1] [0] [head]

PMID-18835655[0] Sleep-related improvements in motor learning following mental practice.

  • shows that after both physical practice and mental imagery on day 1, sleep improves test performance in both when testing on day 2.

____References____

[0] Debarnot U, Creveaux T, Collet C, Gemignani A, Massarelli R, Doyon J, Guillot A, Sleep-related improvements in motor learning following mental practice.Brain Cogn 69:2, 398-405 (2009 Mar)

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ref: Matsuzaka-2007.02 tags: skill learning M1 motor control practice cortex date: 03-20-2009 18:31 gmt revision:1 [0] [head]

PMID-17182912[0] Skill Representation in the Primary Motor Cortex After Long-Term Practice

  • The acquisition of motor skills can lead to profound changes in the functional organization of the primary motor cortex (M1) yes
  • 2 task modes: random target acquisition, and one of 2 repeating sequences (predictable, repeating mode)
  • 2 years of training -> 40% of units were differentially active during the two task modes
  • variations in movement types in the two classes did not fully explain the difference in activity between the 2 tasks
    • M1 neurons are more influence by the task than the actual kinematics.

____References____

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ref: Cooke-1990.03 tags: motor organization triphasic control EMG date: 03-11-2009 21:42 gmt revision:12 [11] [10] [9] [8] [7] [6] [head]

the organization of the human triphasic EMG control sequence:

  • PMID-2329356[0] Movement-related phasic muscle activation. II. Generation and functional role of the triphasic pattern.
  • PMID-8989378[1]
  • PMID-1629754[2]
  • PMID-2230915[3]
  • PMID-2329365[4]
  • PMID-2769335[5]
  • PMID-2769334[6]
  • PMID-3622686[7] Trajectory control in targeted force impulses. I. Role of opposing muscles.
    • Our findings emphasize that neuronal commands to opposing muscles acting at a joint must be adapted to constraints imposed by the properties of the neuromuscular plant.
  • PMID-10085332[8] Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms.
    • frictionless air-jet system, rapid movements, inertia perturbation via masses on the joints, surprise trials.
    • surprise trials were well predicted by an open-loop feedforward controller.
    • there was feedback compensation upon return-to-center: it is not all feedforward (of course!)

____References____

[0] Cooke JD, Brown SH, Movement-related phasic muscle activation. II. Generation and functional role of the triphasic pattern.J Neurophysiol 63:3, 465-72 (1990 Mar)[1] Almeida GL, Hong DA, Corcos D, Gottlieb GL, Organizing principles for voluntary movement: extending single-joint rules.J Neurophysiol 74:4, 1374-81 (1995 Oct)[2] Gottlieb GL, Latash ML, Corcos DM, Liubinskas TJ, Agarwal GC, Organizing principles for single joint movements: V. Agonist-antagonist interactions.J Neurophysiol 67:6, 1417-27 (1992 Jun)[3] Corcos DM, Agarwal GC, Flaherty BP, Gottlieb GL, Organizing principles for single-joint movements. IV. Implications for isometric contractions.J Neurophysiol 64:3, 1033-42 (1990 Sep)[4] Gottlieb GL, Corcos DM, Agarwal GC, Latash ML, Organizing principles for single joint movements. III. Speed-insensitive strategy as a default.J Neurophysiol 63:3, 625-36 (1990 Mar)[5] Corcos DM, Gottlieb GL, Agarwal GC, Organizing principles for single-joint movements. II. A speed-sensitive strategy.J Neurophysiol 62:2, 358-68 (1989 Aug)[6] Gottlieb GL, Corcos DM, Agarwal GC, Organizing principles for single-joint movements. I. A speed-insensitive strategy.J Neurophysiol 62:2, 342-57 (1989 Aug)[7] Ghez C, Gordon J, Trajectory control in targeted force impulses. I. Role of opposing muscles.Exp Brain Res 67:2, 225-40 (1987)[8] Sainburg RL, Ghez C, Kalakanis D, Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms.J Neurophysiol 81:3, 1045-56 (1999 Mar)[9] Seidler RD, Noll DC, Chintalapati P, Bilateral basal ganglia activation associated with sensorimotor adaptation.Exp Brain Res 175:3, 544-55 (2006 Nov)

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ref: Diedrichsen-2005.1 tags: Shadmehr error learning basal ganglia cerebellum motor cortex date: 03-09-2009 19:26 gmt revision:0 [head]

PMID-16251440[0] Neural correlates of reach errors.

  • Abstract:
  • Reach errors may be broadly classified into errors arising from unpredictable changes in target location, called target errors, and errors arising from miscalibration of internal models (e.g., when prisms alter visual feedback or a force field alters limb dynamics), called execution errors.
    • Execution errors may be caused by miscalibration of dynamics (e.g., when a force field alters limb dynamics) or by miscalibration of kinematics (e.g., when prisms alter visual feedback).
  • Although all types of errors lead to similar on-line corrections, we found that the motor system showed strong trial-by-trial adaptation in response to random execution errors but not in response to random target errors.
  • We used functional magnetic resonance imaging and a compatible robot to study brain regions involved in processing each kind of error.
  • Both kinematic and dynamic execution errors activated regions along the central and the postcentral sulci and in lobules V, VI, and VIII of the cerebellum, making these areas possible sites of plastic changes in internal models for reaching.
    • Only activity related to kinematic errors extended into parietal area 5.
    • These results are inconsistent with the idea that kinematics and dynamics of reaching are computed in separate neural entities.
  • In contrast, only target errors caused increased activity in the striatum and the posterior superior parietal lobule.
  • The cerebellum and motor cortex were as strongly activated as with execution errors. These findings indicate a neural and behavioral dissociation between errors that lead to switching of behavioral goals and errors that lead to adaptation of internal models of limb dynamics and kinematics.

____References____

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ref: Brown-2007.09 tags: motor force field learning vision date: 02-20-2009 00:28 gmt revision:1 [0] [head]

PMID-17855611 Motor Force Field Learning Influences Visual Processing of Target Motion

  • as you can see from the title - this is an interesting result.
  • learning to compensate for forces applied to the hand influenced how participants predicted target motion for interception.
  • subjects were trained on a robotic manipulandum that applied different force fields; they had to use the manipulandum to hit a accelerating target.
  • There were 3 force feilds: rightward, leftward, and null. Target accelerated left to right. Subjects with the rightward force field hit more targets than the null, and these more targets than the leftward force field. Hence motor knowledge of the environment (associated accelerations, as if there were wind or water current...) influenced how motion was perceived and acted upon.
    • perhaps there is a simple explanation for this (rather than their evolutionary information-sharing hypothesis): there exists a network that serves to convert visual-spatial coordinates into motor plans, and later muscle activations. The presence of a force field initially only affects the motor/muscle control parts of the ctx, but as training continues, the changes are propagated earlier into the system - to the visual system (or at least the visual-planning system). But this is a complicated system, and it's hard to predict how and where adaptation occurs.

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ref: Porro-1996.12 tags: motor imagery fMRI practice date: 02-19-2009 22:50 gmt revision:0 [head]

PMID-8922425 Primary Motor and Sensory Cortex Activation during Motor Performance and Motor Imagery: A Functional Magnetic Resonance Imaging Study.

  • says exactly what you might expect: that the motor cortex is active during motor imagery, and the regions active during motor performance and motor imagery are overlapping.

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ref: Tamaki-2008.02 tags: sleep spindle NREM motor learning date: 02-18-2009 17:44 gmt revision:0 [head]

PMID-18274267[0] Fast sleep spindle (13-15 hz) activity correlates with sleep-dependent improvement in visuomotor performance.

  • mirror-tracing task performance improves following a night's sleep.
  • the improvement is correlated with the fast-spindle activity.
  • spindles were detected from EEG recordings with a 10-16hz butterworth filter in matlab. Spindles had to be >= 15uv, >= 0.5s
    • slow spindles = 10-13Hz, predominant in the frontal regions.
    • fast spindles > 13hz, predominant in the parietal regions.

____References____

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ref: Morin-2008.08 tags: sleep spindles NREM motor learning date: 02-18-2009 17:35 gmt revision:2 [1] [0] [head]

PMID-18714787[0] Motor sequence learning increases sleep spindles and fast frequencies in post-training sleep.

  • as you can read in the title, it is the motor learning that increases the spindles. They did not look for causality in the opposite direction.
  • Task was finger-tap motor sequence learning, with control. Subjects had to type on a computer keyboard using the nondominant hand. No visual feedback was given during non-training performance (e.g. during practice).
  • Beta-frequencies are greater in sleep after motor learning. , though this is not correlated with actual consolidation.
  • Other studies have shown that spindles are also more frequent after spatial or verbal learning.
  • observed no effect of SWS on motor sequence learning.

____References____

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ref: Song-2009.01 tags: sleep motor learning consolidation attention date: 02-18-2009 17:28 gmt revision:1 [0] [head]

PMID-18951924[0] Consciousness and the consolidation of motor learning

  • Not all consolidation occurs during sleep; in some instances consolidation only occurs during the day; in other times, neither daytime or sleep consolidates a memory.
  • Attention is an important factor that may determine if sleep or daytime replay plays a role in consolidation.
  • In a tapping task, after a night of sleep performance is faster and more accurrate. Without the sleep, but with the same 12-hour interval, the same improvement is absent.
  • Evidence suggests though we experience the sensation of 'voluntary' movement, the conscious wish to move is more an afterthought than the cause.
    • Source: Libet et al 1983. (Subjects could accurately time events, and reported that the will to move preceded actual movement. However, the cortical potentials associated with movement preceded conscious awareness).
    • nonetheless, studies indicate that conscious awareness can affect movements, and how they are consolidated.
  • people with no declarative memory (like HR) can still remember procedural skills.
  • Consolidation = the process by which a fragile memory acquired via practice or exposure is consolidated into a more permanent, stable long-term form. If it occurs in the hours after practice, then it is 'off-line'; likewise for sleep.
    • Consolidation also includes stabilization, or making the memories robust to interference from new memories (retroactive interference).
    • This seems to be dependent on sleep, specifically NREM.
    • In studies where attention was broken using a tone counting task, neither over-night nor over-day enhancements were found to occur for motor sequence learning.
    • Another interesting effect is the development of explicit memory over the course of a night's sleep. Sleep seems to encourage conscious awareness of implicit patterns. -- probably through replay and integration.
  • Regarding "thinking too much" about sports: "As in the studies cited above, motor learning may initially rely on more explicit and prefrontal areas, but after extended practice and expertise, shift to more dorsal areas, but thinking about the movement can shift activity back to the less skilled explicit areas. Although many explanations may be derived, one could argue that these athletes show that even when years of practice has given the implicit system an exquisitely fine tuned memory for a movement, the explicit system can interfere at the time of performance and erase all evidence of implicit memory."
  • Well-written throughout, especially the conclusion paragraph.

____References____

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ref: Rasch-2009.06 tags: sleep cholinergic acetylcholine REM motor consolidation date: 02-18-2009 17:27 gmt revision:0 [head]

PMID-19194375[0] "Impaired Off-Line Consolidation of Motor Memories After Combined Blockade of Cholinergic Receptors During REM Sleep-Rich Sleep."

  • In REM sleep there is high, almost to wake-like, levels of ACh activity (in the cortex? they don't say).
  • Trained subjects on a motor task after a 3-hour period of slow wave sleep.
  • Then administered ACh (muscarinic + nicotinic) blockers or placebo
  • Subjects with blocked ACh reception showed less motor consolidation. So, ACh is needed! (This is consistent with Ach being an attentional / selective signal for activating the cortex).

____References____

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ref: Peters-2008.05 tags: Schaal reinforcement learning policy gradient motor primitives date: 02-17-2009 18:49 gmt revision:4 [3] [2] [1] [0] [head]

PMID-18482830[0] Reinforcement learning of motor skills with policy gradients

  • they say that the only way to deal with reinforcement or general-type learning in a high-dimensional policy space defined by parameterized motor primitives are policy gradient methods.
  • article is rather difficult to follow; they do not always provide enough details (for me) to understand exactly what their equations mean. Perhaps this is related to their criticism that others's papers are 'ad-hoc' and not 'statistically motivated'
  • none the less, it seems interesting..
  • their previous paper - Reinforcement learning for Humanoid robotics - maybe slightly easier to understand.

____References____

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ref: Mamassian-2008.06 tags: overconfidence human motor learning date: 02-17-2009 17:51 gmt revision:0 [head]

PMID-18578851 Overconfidence in an objective anticipatory motor task.

  • Participants were asked to press a key in synchrony with a predictable visual event and were rewarded if they succeeded and sometimes penalized if they were too quick or too slow.
  • If they had used their own motor uncertainty in anticipating the timing of the visual stimulus, they would have maximized their gain.
  • However, they instead displayed an overconfidence in the sense that they underestimated the magnitude of their uncertainty and the cost of their error.
  • Therefore, overconfidence is not limited to subjective ratings in cognitive tasks, but rather appears to be a general characteristic of human decision making. interesting! but is overconfidence really so bad?

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ref: Churchland-2006.12 tags: motor_noise CNS Churchland execution variance motor_planning 2006 date: 12-08-2008 22:50 gmt revision:2 [1] [0] [head]

PMID-17178410[0] A central source of movement variability.

  • Small variations in preparatory neural activity were predictive of small variations in the upcoming reach
    • About half of the noise in reaching movements seems to be from variability during the preparatory phase, as estimated from regressions between preparatory neural activity and variability in performance.
  • even for a highly practiced task, the ability to repeatedly plan the same movement limits our ability to repeatedly execute the same movement.
  • when cocontraction increases, EMG variablility increases, but movement variability decreases. (This is consistent with poisson-based noise source?)
  • see the related articles!!

____References____

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ref: Karni-1998.02 tags: motor learning skill acquisition fMRI date: 10-08-2008 21:05 gmt revision:1 [0] [head]

PMID-9448252[0] The acquisition of skilled motor performance: Fast and slow experience-driven changes in primary motor cortex

  • a few minutes of daily practice on a sequential finger opposition task induced large, incremental performance gains over a few weeks of training
  • performance was lateralized
  • limited training experience can be sufficient to trigger performance gains that require time to become evident.
  • learning is characterized by two stages:
    • "fast” learning, an initial, within-session improvement phase, followed by a period of consolidation of several hours duration
      • possibly this is due to synaptic plasticity.
    • and then “slow” learning, consisting of delayed, incremental gains in performance emerging after continued practice
      • In many instances, most gains in performance evolved in a latent manner not during, but rather a minimum of 6–8 hr after training, that is, between sessions
      • this is thought to correspond to the reorganization of M1 & other cortical structures.
  • long-term training results in highly specific skilled motor performance, paralleled by the emergence of a specific, more extensive representation of a trained sequence of movements in the contralateral primary motor cortex. this is seen when imaging for activation using fMRI.
  • why is there the marked difference between declarative learning, which often only takes one presentation to learn, and procedural memory, which takes several sessions to learn? Hypothetically, they require different neural substrates.
  • pretty good series of references...

____References____

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ref: Nakahara-2001.07 tags: basal ganglia model cerebral cortex motor learning date: 10-05-2008 02:38 gmt revision:0 [head]

PMID-11506661[0] Parallel cortico-basal ganglia mechanisms for acquisition and execution of visuomotor sequences - a computational approach.

  • Interesting model of parallel motor/visual learning, the motor through the posterior BG (the middle posterior part of the putamen) and supplementary motor areas, and the visual through the dorsolateral prefrontal cortex and the anterior BG (caudate head and rostral putamen).
  • visual tasks are learned quicker due to the simplicity of their transform.
  • require a 'coordinator' to adjust control of the visual and motor loops.
  • basal ganglia-thalamacortical loops are highly topographic; motor, oculomotor, prefrontal and limbic loops have been found.
  • pre-SMA, not the SMA, is connected to the prefrontal cortex.
  • pre-SMA receives connections from the rostral cingulate motor area.
  • used actor-critic architecture, where the critic learns to predict cumulative future rewards from state and the actor produces movements to maximize reward (motor) or transformations (sensory). visual and motor networks are actors in visual and motor representations, respectively.
  • used TD learning, where TD error is encoded via SNc.
  • more later, not finished writing (need dinner!)

____References____

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ref: Hikosaka-2002.04 tags: motor learning SMA basal ganglia M1 dopamine preSMA review date: 10-05-2008 02:06 gmt revision:1 [0] [head]

PMID-12015240[0] Central mechanisms of motor skill learning

  • review article.
  • neurons in the SMA become active at particular transitions in sequential movements; neurons in the pre-SMA maybe active specifically at certain rank orders in a sequence.
    • Many neurons in the preSMA were activated during learning of new sequences
  • motor skill learning is associated with coactivation of frontal and partietal cortices.
  • With practice, accuracy of performance was acquired earlier than speed of performance. interesting...
  • Striatum:
    • Reversible blockade of the anterior striatum (associative region) leads to deficits in learning new sequences
    • blockade of the posterior striatum (motor region) leads to disruptions in the execution of learned sequences
  • Cerebellum: In contrast, blockade of the dorsal part of the dentate nucleus (which is connected with M1) does not affect learning new sequences, but disrupts the performance of learned sequences. The conclude from this that long-term memories for motor skills ma be storerd in the cerebellum.
  • Doya proposed that learning in the basal ganglia and cerebellum maybe guided by error signals, as opposed to the cerebral cortex.

____References____

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ref: Graybiel-1994.09 tags: basal ganglia graybeil expert systems motor learning date: 10-03-2008 22:18 gmt revision:2 [1] [0] [head]

PMID-8091209[0] The basal ganglia and adaptive motor control (I couldn't find the pdf for this)

  • the basal ganglia is essentially an expert system which is trained via dopamine.

____References____

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ref: Graybiel-2005.12 tags: graybiel motor_learning reinforcement_learning basal ganglia striatum thalamus cortex date: 10-03-2008 17:04 gmt revision:3 [2] [1] [0] [head]

PMID-16271465[] The basal ganglia: Learning new tricks and loving it

  • learning-related changes occur significantly earlier in the striatum than the cortex in a cue-reversal task. she says that this is because the basal ganglia instruct the cortex. I rather think that they select output dimensions from that variance-generator, the cortex.
  • dopamine agonist treatment improves learning with positive reinforcers but not learning with negative reinforcers.
  • there is a strong hyperkinetic pathway that projects directly to the subthalamic nucleus from the motor cortex. this controls output of the inhibitor pathway (GPi)
  • GABA input from the GPi to the thalamus can induce rebound spikes with precise timing. (the outputs are therefore not only inhibitory).
  • striatal neurons have up and down states. recommended action: simultaneous on-line recording of dopamine release and spike activity.
  • interesting generalization: cerebellum = supervised learning, striatum = reinforcement learning. yet yet! the cerebellum has a strong disynaptic projection to the putamen. of course, there is a continuous gradient between fully-supervised and fully-reinforcement models. the question is how to formulate both in a stable loop.
  • striosomal = striatum to the SNc
  • http://en.wikipedia.org/wiki/Substantia_nigra SNc is not an disorganized mass: the dopamergic neurons from the pars compacta project to the cortex in a topological map, dopaminergic neurons of the fringes (the lowest) go to the sensorimotor striatum and the highest to the associative striatum

____References____

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ref: Radhakrishnan-2008.1 tags: EMG BMI Jackson motor control learning date: 10-03-2008 16:45 gmt revision:0 [head]

PMID-18667540[0] Learning a novel myoelectric-controlled interface task.

  • EMG-controlled 2D cursor control task with variable output mapping.
  • Subjects could learn non-intuitive output transforms to a high level of performance,
  • Subjects preferred, and learned better, if hand as opposed to arm muscles were used.

____References____

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

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

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

____References____

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ref: Zhu-2003.1 tags: M1 neural adaptation motor learning date: 09-24-2008 22:17 gmt revision:0 [head]

PMID-14511525 Probing changes in neural interaction during adaptation.

  • looking at the changes in te connectivity between cells during/after motor learning.
  • convert sparse spike trains to continuous firing rates, use these as input to granger causality test
  • used the Dawn Taylor monkey task, except with push-buttons.
  • perterbed the monkey's reach trajectory with a string to a pneumatic cylinder.
  • their data looks pretty random. 9-17 neurons recorded. learning generally involves increases in interaction.
  • sponsored by DARPA
  • not a very good paper, alas.

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ref: Narayanan-2005.04 tags: Laubach M1 motor rats statistics BMI prediction methods date: 09-07-2008 19:51 gmt revision:4 [3] [2] [1] [0] [head]

PMID-15858046[] Redundancy and Synergy of Neuronal Ensembles in Motor Cortex

  • timing task.
  • rats.
  • 50um teflon microwires in motor cortex
  • ohno : neurons that were the best predictors of task performance were not necessarily the neurons that contributed the most predictive information to an ensemble of neurons.
  • most all contribute redundant predictive information to the ensemble.
    • this redundancy kept the predictions high, even if neurons were dropped.
  • small groups of neurons were more synergistic
  • large groups were more redundant.
  • used wavelet based discriminant pursuit.
    • validated with draws from a random data set.
  • used R and Weka
  • data looks hella noisy ?

____References____

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ref: Francis-2005.11 tags: Joe_Francis motor_learning reaching humans delay intertrial interval date: 04-09-2007 22:48 gmt revision:1 [0] [head]

PMID-16132970[0] The Influence of the Inter-Reach-Interval on Motor Learning.

Previous studies have demonstrated changes in motor memories with the passage of time on the order of hours. We sought to further this work by determining the influence that time on the order of seconds has on motor learning by changing the duration between successive reaches (inter-reach-interval IRI). Human subjects made reaching movements to visual targets while holding onto a robotic manipulandum that presented a viscous curl field. We tested four experimental groups that differed with respect to the IRI (0.5, 5, 10 or 20 sec). The 0.5 sec IRI group performed significantly worse with respect to a learning index than the other groups over the first set of 192 reaches. Each group demonstrated significant learning during the first set. There was no significant difference with respect to the learning index between the 5, 10 or 20 sec IRI groups. During the second and third set of 192 reaches the 0.5 sec IRI group's performance became indistinguishable from the other groups indicating that fatigue did not cause the initial poor performance and that with continued training the initial deficit in performance could be overcome.

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ref: Kawato-1999.12 tags: kawato inverse dynamics cerebellum motor control learning date: 04-09-2007 22:45 gmt revision:1 [0] [head]

PMID-10607637[0] Internal models for motor control and trajectory planning

  • in this review, I will discuss evidence supporting the existence of internal models.
  • fast coordinated arm movement canot be executed under feedback control, as biological feedback loops are slow and have low gains. hence, the brain mostly needs to control things in a pure feedforward manner.
    • visual feedback delay is about 150-200ms.
    • fast spinal reflexes still require 30-50ms; large compared to fast movements (150ms).
    • muscle intrinsic mechanical properties produce proportional (stiffness) and derivative (viscosity) gains without delay.
    • inverse models are required for fast robotics, too. http://www.erato.atr.co.jp/DB/
  • talk about switching external force field to gauge the nature of the internal model - these types of experiments verily prove that feedforward / model-based control is happening. has anyone shown what happens neuronally during the course of this learning? I guess it might be in my datar.

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ref: Scott-2004.07 tags: Scott motor control optimal feedback cortex reaching dynamics review date: 04-09-2007 22:40 gmt revision:1 [0] [head]

PMID-15208695[0] PDF HTML summary Optimal feedback control and the neural basis of volitional motor control by Stephen S. Scott

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ref: Boline-2005.11 tags: electrophysiology motor cortex force isometric Ashe 2005 date: 04-09-2007 22:39 gmt revision:3 [2] [1] [0] [head]

this seems to be the same as {339}, with a different pubmed id & different author list. bug in the system!

PMID-16193273[0] On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional dynamic isometric force* the majority of cells responded to direction

  • few to the magnitude,
  • and ~10% to the direction & magnitude
  • control of static and dynamic motor systems is based on a common control process!
  • 2d task, monkeys, single-unit recording, regression analysis.

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ref: Chan-2006.12 tags: computational model primate arm musculoskeletal motor_control Moran date: 04-09-2007 22:35 gmt revision:1 [0] [head]

PMID-17124337[0] Computational Model of a Primate Arm: from hand position to joint angles, joint torques, and muscle forces ideas:

  • no study so far has been able to incorporate all of these variables (global hand position & velocity, joint angles, joint angular velocities, joint torques, muscle activations)
  • they have a 3D, 7DOF model that translate actual motion to optimized muscle activations.
  • knock the old center-out research (nice!)
  • 38 musculoskeletal-tendon units
  • past research: people have found correlations to both forces and higher-level parameters, like position and velocity. these must be transformed via inverse dynamics to generate a motor plan / actually move the arm.
  • used SIMM to optimize the joint locations to replicate actual movements...
  • assume that the torso is the inertial frame.
  • used infrared Optotrak 3020
  • their model is consistent - they can use the inverse model to calculate muscle activations, which when fed back into the forward model, results in realistic movements. still yet, they do not compare to actual EMG.
  • for working with the dynamic model of the arm, they used AUTOLEV
    • I wish i could figure out what the Kane method was, they seem to leverage it here.
  • their inverse model is pretty clever:
  1. take the present attitude/orientation & velocity of the arm, and using parts of the forward model, calculate the contributions from gravity & coriolis forces.
  2. subtract this from the torques estimated via M*A (moment of interia times angular acceleration) to yield the contributions of the muscles.
  3. perturb each of the joints / DOF & measure the resulting arm motion, integrated over the same period as measurement
  4. form a linear equation with the linearized torque-responses on the left, and the muscle torque contributions on the right. Invert this equation to get the actual joint torques. (presumably the matrix spans row space).
  5. to figure out the muscle contributions, do the same thing - apply activation, scaled by the PCSA, to each muscle & measure the resulting torque (this is effectively the moment arm).
  6. take the resulting 38x7 matrix & p-inverse, with the constraint that none of the muscle activations are negative, yielding a somewhat well-specified muscle activation. not all that complicated of a method

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ref: Sergio-2005.1 tags: isometric motor control kinematics kinetics Kalaska date: 04-09-2007 22:33 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-15888522[0] Motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks.

  • see [1]
  • recorded 132 units from the caudal M1
  • two tasks: isometric and movement of a heavy mass, both to 8 peripheral targets.
    • target location was digitized using a 'sonic digitizer'. trajectories look really good - the monkey was well trained.
  • idea: part of M1 functions near the output (of course)
    • evidence supporting this: M1 rasters during movement of the heavy mass show a triphasic profile: one to accelerate the mass, one to decelerate it, and another to hold it steady on target. see [2,3,4,5,6,7,8,9,10]

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ref: Hatsopoulos-2005.1 tags: motor control M1 Hatsopoulos date: 04-09-2007 22:26 gmt revision:1 [0] [head]

PMID-16160087[] Encoding in the Motor Cortex: Was Evarts Right After All? Focus on "Motor Cortex Neural Correlates of Output Kinematics and Kinetics During Isometric-Force and Arm-Reaching Tasks"

  • this is a discussion of the isometric vs. pendulum movement task. (and editorial focus)
    • in the isometric task, neurons are tuned to the direction of force, and fire anticipatorily.
    • in the movement task, they show a characteristic triphasic activity profile, very similar to what the muscles need.
  • neurons in the rostral bank of the CS seem to (almost) control muscle activation directly.
  • One possibility mentioned by the authors is that motor cortex may need to compensate for nonlinearities at the spinal motoneuron level as well as the low-pass filter characteristics of the muscles resulting in a nonlinear mapping between these neurons and the resulting forces at the hand.

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ref: Ashe-1994.12 tags: Georgoplous motor control M1 S1 SUA electrophysiology 2D date: 04-09-2007 20:27 gmt revision:2 [1] [0] [head]

PMID-7703686[0] Movement parameters and neural activity in motor cortex and area 5

  • 290 cells in the motor cortex and 207 cells in area 5 (S1)
  • median R^2 = 0.581 & 0.530 in motor cortex
  • most prominent representation of target direction; least prominent representation of acceleration. (though statistically significant correlations were observed for all behavioral parameters)

Duke does not have online access to the article :(

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ref: Maier-1993.03 tags: force motor control grip electrophysiology date: 04-09-2007 20:20 gmt revision:4 [3] [2] [1] [0] [head]

PMID-8463818[0] Contribution of the monkey corticomotoneuronal system to the control of force in precision grip

  • recorded 33 corticomotoneronal cells
  • used spike-triggered averaging to find putative pyramidal tract neurons.
  • considerable trail-by-trial variability in the cells activity-force relationship
  • and, in an earlier work: PMID-810360[1] Relation of activity in precentral cortical neurons to force and rate of force change during isometric contractions of finger muscles.

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ref: HeppReymond-1999.09 tags: force motor control grip electrophysiology date: 04-09-2007 20:20 gmt revision:0 [head]

PMID-10473750[0] Context-dependent force coding in motor and premotor cortical areas.

  • here they found neurons related to dF/dt during another isometric precision grip task.

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ref: Caminiti-1991.05 tags: transform motor control M1 3D population_vector premotor Caminiti date: 04-09-2007 20:10 gmt revision:2 [1] [0] [head]

PMID-2027042[0] Making arm movements within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets.

  • trained monkeys to make similar movements in different parts of external/extrinsic 3D space.
  • change of preferred direction was graded in an orderly manner across extrinsic space.
  • virtually no correlations found to endpoint static position: "virtually all cells were related to the direction and not to the end point of movement" - compare to Graziano!
  • yet the population vector remained an accurate predictor of movement: "Unlike the individual cell preferred directions upon which they are based, movement population vectors did not change their spatial orientation across the work space, suggesting that they remain good predictors of movement direction regardless of the region of space in which movements are made"

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ref: Caminiti-1990.07 tags: transform motor control M1 3D population_vector premotor Caminiti date: 04-09-2007 20:07 gmt revision:4 [3] [2] [1] [0] [head]

PMID-2376768[0] Making arm movements within different parts of space: dynamic aspects in the primate motor cortex

  • monkeys made similar movements in different parts of external/extrinsic 3D space.
  • change of preferred direction was graded in an orderly manner across extrinsic space.
    • this change closely followed the changes in muscle activation required to effect the observed movements.
  • motor cortical cells can code direction of movement in a way which is dependent on the position of the arm in space
  • implies existence of mechanisms which facilitate the transformation between extrinsic (visual targets) and intrinsic coordinates
  • also see [1]

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

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

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

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ref: Georgopoulos-1992.06 tags: motor control force Georgopoulos date: 04-09-2007 19:56 gmt revision:1 [0] [head]

PMID-1609282[0] The motor cortex and the coding of force.

  • 2D isometric force, which dissociated force & changed in force.
  • cells are not tuned to the direction of the absolute force, but rather to the direction of both the visual cue and change in force (dF/dt) as measured using linear regressions in an isometric force task.

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

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

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

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ref: Thach-1978.05 tags: monkeys motor control M1 cerebellum electropysiology date: 04-09-2007 19:53 gmt revision:3 [2] [1] [0] [head]

PMID-96223[0] Correlation of neural discharge with pattern and force of muscular activity, joint position, and direction of intended next movement in motor cortex and cerebellum.

  • recorded from M1 and interpositus/dentate nucleus of the cerebellum.
  • three classes of response in the interpositus/dentate and M1
    1. some in relation to the pattern of muscle activity
    2. some in relation to the position of the wrist
    3. some in relation to the next intended movement.

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

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

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

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ref: BrashersKrug-1996.07 tags: consolidation motor learning Shadmher Bizzi date: 04-09-2007 14:35 gmt revision:2 [1] [0] [head]

PMID-8717039[0] Consolidation in human motor memory

  • tested interference between the learning of two motor skills
    • no interference if the delay between practice on each task was > 4 hours
    • this implies that some memory consolodation occurs within those 4 hours.. the same as previous work which implicated the medial temporal lobe as an important region for memory encoding.
  • found with MIT open course ware -- there are a lot of good papers referenced there.

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ref: DeLong-1974.05 tags: motor control basal ganglia cerebellum motor cortex DeLong putamen original date: 04-09-2007 01:51 gmt revision:1 [0] [head]

PMID-4219745[0] Relation of basal ganglia, cerebellum, and motor cortex units to ramp and ballistic limb movements.

  • monkey trained to make both ballistic movement and slow, pulling movements by pulling a manipulandum between three targets.
  • cells in the putamen discharged preferentially during slow movements.
    • consistent with a sequence / temporal scaling (?) role.
    • also consistent with the cerebellum creating rapid/feedforward trajectories.
  • cells in the motor cortex discharged for both types of movements, though a bit more for ballisic type movements (where the forces were higher).
  • paper is thankfully short and concise.
    • and also humble: "the mere correlation of unit discharge with some aspect of a movement without knowledge of the peripheral site influenced by the unit under study can only provide grounds for speculation".

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

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

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

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ref: Fu-1993.11 tags: premotor M1 PMd PMv SUA date: 04-05-2007 17:12 gmt revision:0 [head]

PMID-8294972[0] Neuronal specification of direction and distance during reaching movements in the superior precentral premotor area and primary motor cortex of monkeys.

  • key thing: distance to target is represented in the motor & premotor corticies.

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ref: Kettner-1988.08 tags: 3D motor control population_vector Schwartz Georgopoulos date: 04-05-2007 17:09 gmt revision:1 [0] [head]

A triptych of papers (good job increasing your publication count, guys!):

  • PMID-3411363[0] Primate motor cortex and free arm movements to visual targets in three-dimensional space. III. Positional gradients and population coding of movement direction from various movement origins.
    • propose multilinear model to predict firing rate of nneuron (a regression that is the same direction as the kalman filter)
    • i don't see how this is that much different from below (?)
  • PMID-3411362[1] Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population.
    • they show, basically, that they can predict movement direction (note this is different from actual movement!) using the poulation vector scheme.
  • PMID-3411361[2] Primate motor cortex and free arm movements to visual targets in three-dimensional space. I. Relations between single cell discharge and direction of movement.
    • 568 cells!!
    • 8 directional targets, again -- not sure how they were aranged; they say 'in approximately equal angular intervals'
    • these findings generalize the previous 2D results [3] (tuning to external space) to 3D

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

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

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

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ref: Georgopoulos-1982.11 tags: georgopoulos kalaska caminiti M1 motor control tuning population_vector date: 04-05-2007 16:27 gmt revision:0 [head]

PMID-7143039[0] On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex

  • famous 8-target center out task
  • dot-product tuning
  • 75% of cells were found to be tuned.
  • posits the population code for directional movements - statistical summation & averaging, i presume.

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ref: Ostry-2003.12 tags: force motor control review cortex M1 date: 04-05-2007 15:21 gmt revision:0 [head]

PMID-14610628[0] A critical evaluation of the force control hypothesis in motor control.

  • the target of this review is the inverse dynamics model of motor control, which is very successful in robots. however, it seems that the mammalian nervous system does things a bit more complicated than this.
  • they agree that motor learning is most likely the defining feature of the cortex (i think that the critical and essential element of the cortex is not what control solution it arrives at, but rather how it learns that solution given the anatomical connections development has endowed it with.
  • they also find issue with the failure to incorporate realistic spinal reflexes into inverse-dynamics models.
  • However, we find little empirical evidence that specifically supports the inverse dynamics or forward internal model proposals per se.
  • We further conclude that the central idea of the force control hypothesis--that control levels operate through the central specification of forces--is flawed.

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ref: Cabel-2001.1 tags: Stephen Scott Kinarm motor control date: 04-04-2007 21:51 gmt revision:0 [head]

PMID-11600665[] Neural Activity in Primary Motor Cortex Related to Mechanical Loads Applied to the Shoulder and Elbow During a Postural Task

  • experiment w/ the kinarm. w/ Stephen Scott.
  • roughly equal numbers of neuons responsive to mechanical loads on shoulder, elbow, and both.
  • neural activity is also strongly influenced by the specific motor patterns used to perform a given task.

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ref: engineering notes-0 tags: homopolar generator motor superconducting magnet date: 03-09-2007 14:39 gmt revision:0 [head]

http://hardm.ath.cx:88/pdf/homopolar.pdf

  • the magnets are energized in 'opposite directions - forcing the field lines to go normal to the rotar.
  • still need brushes - perhaps there is no way to avoid them in a homopolar generator.

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ref: Brown-2001.11 tags: Huntingtons motor_learning intentional implicit cognitive deficits date: 0-0-2007 0:0 revision:0 [head]

PMID-11673321 http://brain.oxfordjournals.org/cgi/content/full/124/11/2188 :

  • 16 genetically-confirmed Huntington's patients (and matched controls) trained on a task using trial and error learning (intentional), and implicit learning (unintentional).
  • the task setup was simple: they had to press one of four keys arranged in a cross (with center) either in response to commands or while guessing a sequence of a few keys.
  • Within the random, commanded task there was a sequence that could/should be noticed.
  • Huntington's patients performed worse on the intentional learning segment, but comparably on the implicit learning / implicit sequence awareness, though the latter test seems rather weak to me.

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ref: bookmark-0 tags: machine_learning todorov motor_control date: 0-0-2007 0:0 revision:0 [head]

Iterative Linear Quadratic regulator design for nonlinear biological movement systems

  • paper for an international conference on informatics in control/automation/robotics

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ref: bookmark-0 tags: motor learning control Wolpert Ghahramani date: 0-0-2007 0:0 revision:0 [head]

http://www.bcs.rochester.edu/people/alex/bcs547/readings/WolpertGhahr00.pdf

  • the curse of dimensionality: there are about 600 muscles in the human body; 2^600 >> than the # of atoms in the universe! we must structure this control problem.
  • there are about 200,000 alpha motor neurons.
  • damage to parietal cortex can lead to an inability to maintain state estimates of the limb (and other objects?)
  • damage to pareital cortex can lead to and inability to mentally simulate movement with the affected hand.
  • damage to the left pareital cortex can lead to a relative inability to determine wheither viewed movements are ones own or not.
  • state prediction can reduce the effect of delays in sensorimotor feedback loops.
    • example: soleus and gastrocinemus tightent before lifting a heavy load with the arms.
  • the primate CNS models both the expected sensory feedback and represents the likelihood of the sensory feedback given the context. e.g. if people think that they are moving, they will compensate for non-existent coriolis forces.
  • ''how are we able to learn a variety of contexts?
    • when subjects try to learn two different dynamics (e.g. forward and reverse on sideskates), interference occurs when they are presented in rapid sucession, but not when they are separated by several hours.)
  • has a good list of refs.

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ref: learning-0 tags: motor control primitives nonlinear feedback systems optimization date: 0-0-2007 0:0 revision:0 [head]

http://hardm.ath.cx:88/pdf/Schaal2003_LearningMotor.pdf not in pubmed.

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ref: Schaal-2005.12 tags: schaal motor learning review date: 0-0-2007 0:0 revision:0 [head]

PMID-16271466 Computational Motor control in humans and robots

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ref: Blankertz-2003.06 tags: BMI BCI EEG error classification motor commands Blankertz date: 0-0-2007 0:0 revision:0 [head]

PMID-12899253 Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis

  • want to minimize subject training and maximize the major learning load on the computer.
  • task: predict the laterality of imminent left-right hand finger movements in a natural keyboard typing condition. they got ~15bits/minute (in one subject, ~50bits per minute!)
    • used non-oscilatory signals.
  • did a to detect 85% percent of error trials, and limited false-positives to ~2%

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ref: Flash-2001.12 tags: Flash Sejnowski 2001 computational motor control learning PRR date: 0-0-2007 0:0 revision:0 [head]

PMID-11741014 Computational approaches to motor control. Tamar Flash and Terry Sejnowski.

  • PRR = parietal reach region
  • essential controviersies (to them):
    • the question of motor variables that are coded by neural populations.
    • equilibrium point control vs. inverse dynamics (the latter is obviously better/more correct)

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ref: Stefani-1995.09 tags: electrophysiology dopamine basal_ganglia motor learning date: 0-0-2007 0:0 revision:0 [head]

PMID-8539419 Electrophysiology of dopamine D-1 receptors in the basal ganglia: old facts and new perspectives.

  • D1 is inhibitory (modulatory) on striatal neurons.
  • D1 cloned in 1990
  • D1 stimulates adenyl cyclase. (cAMP)
  • D1 activity shown to be necessary, but not sufficient, to generate long-term depression in striatal slices.
  • SKF 38393 was designed as a selective D1 receptor agonist; it has been available since the late 70's; it has nanomolar affinity for D1-R. SKF 38393 inhibits action potential discharge in striatal neurons as measued through response to intracellular current depolarizations.
  • striatal cells project to the substantia nigra.
  • alternate hypothesis: D1 activation on the striatonigral afferents to the ventral tegmental area (VTA) promotes GABA release.
    • recall that the VTA projects to the frontal/prefrontal cortex (PFC) via the mesocortical dopiminergic pathway. http://grad.uchc.edu/phdfaculty/antic.html There, DA synapese on spines of distal dendrites in juxtaposition with glutamergic synapses. this guy posits that these DA synapses are involved in the pathology of schizophrenia, and he uses optical techniques to measure the DA/Glu synapses.
    • VTA is just below the red nucleus in rats.
  • some people report that SKF 38393 potentiated depolarizing membrane responses to exogenous NMDA (agonist, excitotoxin).
  • they prefer the magnesium-dependent LTD pathway.
    • D1 receptor antagonist SCH 23390 prevented the generation of LTD in striatum. (Calabresi et al 1992).
    • in DA-depleted slices, LTD could be restored by the co-administration of D1 and D2 agonists.

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ref: Harris-1998.08 tags: motor_control error variance optimal_control 1998 wolpert date: 0-0-2007 0:0 revision:0 [head]

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

  • key idea: neural control signals are corrupted by noise whose variance increases with the size of the control signal
  • this idea is sufficient to explain a number of features of human motor behavior.

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ref: bookmark-0 tags: smith predictor motor control wolpert cerebellum machine_learning prediction date: 0-0-2006 0:0 revision:0 [head]

http://prism.bham.ac.uk/pdf_files/SmithPred_93.PDF

  • quote in reference to models in which the cerebellum works as a smith predictor, e.g. feedforward prediction of the behavior of the limbs, eyes, trunk: Motor performance based on the use of such internal models would be degraded if the model was inavailable or inaccurate. These theories could therefore account for dysmetria, tremor, and dyssynergia, and perhaps also for increased reaction times.
  • note the difference between inverse model (transforms end target to a motor plan) and inverse models 9is used on-line in a tight feedback loop).
  • The difficulty becomes one of detecting mismatches between a rapid prediction of the outcome of a movement and the real feedback that arrives later in time (duh! :)
  • good set of notes on simple simulated smith predictor performance.

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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.