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Lecture 7

Lecture 7.docx

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University of Toronto Scarborough
Matthias Niemeier

Lecture 7 Motor systems - Carpenter chapter 9 Overview 1. Challenges of motor control research 2. Feedback for motor control 3. Efference copy 4. Internal models / forward models 5. Descending spinal tracks 6. Hierarchy of motor control Challenges of motor control research 1. No motor control without sensory feedback a. i.e. Motor control can't be studied in isolation 2. Instead studied by tracing effects of natural stimuli along sensorimotor pathways a. Done with simpler animals or systems 3. Chance of motor control research a. Studying complete systems with an output most powerful i. E.g. sensory research is tough without knowing what a neuron / area is for Feedback for motor control 1. No feedback - ballistic system a. Throwing orange peels in the garbage b. Vulnerable to noise (wind) c. Noise is never entirely predictable 2. No feedback - parametric feed forward a. Sensor to monitor noise before it affects the system (speed of wind) i. Adjust accordingly b. Infinitely many possible sources of noise 3. Parametric feedback a. Comparator: i. Actual result vs desired result 1. Information comes from actual result  feedback information to controller b. Error signal i. Adjust accordingly to fix error c. Gradual fine tuning of general purpose commands d. Motor learning (VOR) i. Slow; requires vast memory and IQ 4. Direct feedback a. Guided control / servo system b. Feedback compared to desired results i.  Error generates motor command 1. Error tells you how far away you are from goal c. Simple: i. Specifies goal, not how to get there 1. Distance from goal a. Hot b. Cold d. Immune to noise i. E.g. thermostat 1. Tells you temperature of room 2. Certain level turns on furnace 3. Certain level turns off furnace e. Slow oscillations i. Takes time to turn on and turn off furnace f. Solution i. Monitor actual error and rate at which it changes 1. x and dx/dt ii. Ia fibers respond to error and its rate of change iii. Still too slow 5. Internal copy a. Efference copy and virtual models i. Branched off from motor command ii. Forms a model of the plant iii. Results in predicted result iv. Results in predicted error v. Adjusted accordingly to reach actual result vi. Internal feedback to form a prediction error that affects the model of the plant 1. Makes the model better to result in less of a predicted error b. Continuous updating of the model c. Saccades d. Embodies a model of a muscle or entire body i. Learning motor skills  learning to predict the behavior of one's own body ii. Athletes and motor imagery Summary of feedback for motor control 1. Gaze holding a. Conjunction i. Optokinetic ii. VOR 2. Gaze shifting a. Conjunction i. Saccades ii. Smooth pursuit b. Disjunction i. Vergence 3. Spontaneous a. Drift b. Microsaccades c. Tremor Overview of efference copy 1. Synthetic sensory signal derived from motor command 2. Feedback for motor control 3. Feedback for perception a. The problem of spatial stability across saccades 4. A neural pathway for efference copies (corollary discharge) The problem of stability across saccades Sensory signals available to achieve stability 1. Vision alone might be insufficient to perceive the world 2. Additional sources of information a. Retinal information i. Reafference b. Extraretinal information i. Proprioception (do not use this at all) ii. Corollary discharge (efference copy) 3. No other information available 4. Efference copy = corollary discharge = effort of will 5. Study a. Botox  complete paralysis b. Perceived displacements during intended saccades c. No motion  displacements were instant d. Eye tapping A neural pathway for efference copies 1. Intermediate layers of SC (superior colliculus)  medial dorsal nucleus of the thalamus  FEF (frontal eye field) 2. Supporting evidence a. Pathway is associated with shifting RFs (receptive fields) in FEF i. Signal is appropriate ii. Necessary Internal models / forward models Forward models: Thought paper 2 TP2: What is necessary to perform a movement? TP2: Introduction 1. Dynamic updating of motor states a. Forward models: i. Estimate the state of the peripheral motor apparatus (and compensate for sensory delays) 1. E.g. for accurate reaches b. Dependent variable i. Cutaneous temporal order judgment (TOJ) task c. Independent variable 1 i. Crossed / Uncrossed hands d. Independent variable 2 i. Movement planning bias TP2: Methods - 2 factorial design 1. IV1: a. Arm cros
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