Course Syllabus
Obiettivi
Studio dei principi che governano il controllo sensorimotorio e delle aree neurali coinvolte
Contenuti sintetici
Principi computazionali del controllo sensorimtorio
Apprendimento sensorimotorio
Aree neurali coinvolte
Programma esteso
Introduzione al controllo motorio
Livelli di analisi di Marr
Pianificazione e controllo
Cinematica diretta e inversa
Dinamica diretta e inversa
Schemi di controllo e predizione dello stato
feedforward e feedback
Internal model (inverse e forward)
Stima dello stato
Inferenza Bayesiana
Ottimalità
Pianificazione della traiettoria
Funzioni di costo: minimum jerk, minimum torque, minimum variance
Optimal feedback control
Minimum intervention principle
Apprendimento sensorimotorio
Adattamento
Task e prediction error
Cervelletto
Funzioni
Microcircuito cerebellare
Apprendimento cerebellare
Aree motorie corticali
Corteccia motoria primaria
Corteccia premotoria
Tratti discendenti
Circuiti spinali
Midollo spinale
Recettori propriocettivi muscolari
Archi riflessi e loro modulazione
Controllo della locomozione
Central Pattern Generator (CPG)
Modulazione CPG da parte di afferenze sensoriali e aree sovraspinali
Modalità didattica
lezioni frontali
Materiale didattico
Le lezioni di questo modulo sono sviluppate sulla base di due libri di riferimento e soprattutto articoli scientifici. Per ciascuna lezione verrà comunicato il relativo materiale didattico.
Libri di riferimento:
Kandel E., et al. (2021). Principles of Neural Science. (6th ed). McGraw Hill. Capitoli 30-36.
Purves D., et al. (2021). Neuroscienze. (5th ed. italiana; 6th ed. americana). Zanichelli. Capitoli 16-19.
Articoli scientifici (necessari):
Marr D. (2010) Vision: A Computational Investigation Into the Human Representation and Processing of Visual Information. The MIT Press. Capitolo 1.
Wolpert D, Ghahramani Z. (2000). Computational principles of movement neuroscience. Nat Neurosci. Nat Neurosci 3 (Suppl 11), 1212–1217.
Kawato M. (1999). Internal models for motor control and trajectory planning. Curr Opin Neurobiol. 9(6):718-27.
Todorov E. (2004). Optimality principles in senosrimotor control. Nat Neurosci. 7(9):907-915.
Articoli scientifici (approfondimenti):
Körding KP, Wolpert DM. (2004). Bayesian integration in sensorimotor learning. Nature. 427(6971):244-7
Shadmehr R, Mussa-Ivaldi F. (1994) Adaptive representation of dynamics during learning of a motor task. JNeurosci. 14(4):3208:24
Morasso, P. (1981) Spatial control of arm movements. Exp Brain Res 42, 223–227.
Todorov E, Jordan MI. (2002). Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5(11):1226-1235.
Shadmehr R, Krakauer JW. A computational neuroanatomy for motor control. Exp Brain Res. 2008 Mar;185(3):359-81
Modalità di verifica del profitto e valutazione
come da syllabus dell'insegnamento
Orario di ricevimento
Su appuntamento
Sustainable Development Goals
Aims
Study of the principles of sensorimotor control and of the involved neural structures
Contents
Computational principles of sensorimotor control
Sensorimotor learning
Involved neural structures
Detailed program
Introduction to sensorimotor control
Marr Marr's levels of analysis
Planning and control
Direct and inverse kinematics
Direct and inverse dynamics
Control schema and prediction
Feedforward e feedback control
Internal models (inverse e forward)
State estimation
Bayesian inference
Optimality
Trajectory planning
Cost functions: minimum jerk, minimum torque, minimum variance
Optimal feedback control
Minimum intervention principle
Sensorimotor learning
Adaptation
Task e prediction error
Cerebellum
Functions
Cerebellar microcircuit
Cerebellar learning
Motor cortical regions
Primary motor cortex
Premotor cortex
Descedent pathways
Spinal circuity
Spinal cord
Muscle proprioceptors
Spinal reflees and their modulation
Control of locomotion
Central Pattern Generator (CPG)
CPG modulation by sensory afferents and sovraspinal regions
Teaching form
in presence
Textbook and teaching resource
This course has been developed based on two books and several scientific articles. The teaching resources specific for each topic will be communicated during the classes.
Textbooks:
Kandel E., et al. (2021). Principles of Neural Science. (6th ed). McGraw Hill. Capitoli 30-36.
Purves D., et al. (2021). Neuroscienze. (5th ed. italiana; 6th ed. americana). Zanichelli. Capitoli 16-19.
Scientific papers (required):
Marr D. (2010) Vision: A Computational Investigation Into the Human Representation and Processing of Visual Information. The MIT Press. Capitolo 1.
Wolpert D, Ghahramani Z. (2000). Computational principles of movement neuroscience. Nat Neurosci. Nat Neurosci 3 (Suppl 11), 1212–1217.
Kawato M. (1999). Internal models for motor control and trajectory planning. Curr Opin Neurobiol. 9(6):718-27.
Todorov E. (2004). Optimality principles in senosrimotor control. Nat Neurosci. 7(9):907-915.
Scientific papers (suggested):
Körding KP, Wolpert DM. (2004). Bayesian integration in sensorimotor learning. Nature. 427(6971):244-7
Shadmehr R, Mussa-Ivaldi F. (1994) Adaptive representation of dynamics during learning of a motor task. JNeurosci. 14(4):3208:24
Morasso, P. (1981) Spatial control of arm movements. Exp Brain Res 42, 223–227.
Todorov E, Jordan MI. (2002). Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5(11):1226-1235.
Shadmehr R, Krakauer JW. A computational neuroanatomy for motor control. Exp Brain Res. 2008 Mar;185(3):359-81
Semester
Annual
Assessment method
Described in the subject's syllabus
Office hours
By appointment
Sustainable Development Goals
Staff
-
Cristiano Alessandro
