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ACTIVE SENSING Lecture 10: High-order motor loops (planning and execution)

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ACTIVE SENSING Lecture 10: High-order motor loops (planning and execution)
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ACTIVE SENSINGLecture 10:

High-order motor loops (planning and execution)

K1

K2

d F

P = 0

d

F

P

Desired:d = dD

d

F

0

0

d=-k1FF = k2(d-dD)

F = k2d

0

controlling the loop’s set-point

K1

K2

d F

P = 0

d

F

P

Desired:d = dD

d

F

0

0

d=-k1FF = k2(d-dD)

F = k2d

0

dD

Proportional control

dss

controlling the loop’s set-point

dss ≠ dD

d

F

P 0

controlling the loop’s set-point

ways to decrease the error

•loop gain

•servo-mechanism

Locomotion control

Cowan, N. J. et al. J. Neurosci. 2007;27:1123-1128

Noah’s movie

Copyright ©2007 Society for Neuroscience

Cowan, N. J. et al. J. Neurosci. 2007;27:1123-1128

Figure 1. Identifying the whole-animal transfer function for longitudinal tracking behavior in Eigenmannia

Figure 1. Identifying the whole-animal transfer function for longitudinal tracking behavior in Eigenmannia. A, The fish (brown) maintains its position within a rectangular tube. This refuge has a clear polycarbonate top and white plastic sides with ceramic-filled windows (gray). B, Schematic view of the video data captured using a camera positioned above the fish. Three values are the position of the fish [x(t), purple lines], the position of the refuge [r(t), green lines], and the relative difference between the fish and the refuge [e(t), dashed blue line]. C, Tracking data from a fish (stimulus amplitude, 0.5 cm). The height of each trace is scaled identically; the width of each trace is scaled to show two stimulus cycles (bottom; green) at three rates of motion (labeled). The amplitude of fish movements, x(t), decreased with increasing stimulus frequency with increasing phase lag. D, Bode amplitude. E, Bode phase plots for stimulus rates from 0.1–1.3 Hz. Error bars indicate the SD (N = 4 fish). The dashed curve indicates the first-order model (rejected), and the gold region indicates 95% confidence intervals for this model. The solid curve indicates the second-order model, and the blue region indicates 95% confidence intervals for this model.

dF

PI. nested loops

Loops hierarchy

K1

K2

dF

P

I. nested loops

Loops hierarchy

K3

d*

dD

d

F

0

P

d=P-FF = k2(d-dD)

F = k2d

dF

P

K1

K2

dF

P

I. nested loops

Loops hierarchy

d

F

0

0

d=P-FF = k2(d-dD)

K3

d

dD

d

dD

0

0

dD=-k3dd=G/(1+G) dD

I. nested loops

Loops hierarchy

d

F

0

0

d=P-FF = k2(d-dD)

d

dD

0

0

dD=-k3dd=G/(1+G) dD

K1

K2

F

P

K3

d

dD

K3

d

dD

G1+G

Multiple-level (nested) control loops

Control loops can be arranged in multiple levels

for each level, controlled variables, loop variables

and transfer functions can be defined

independently

the stability and dynamics of each loop can be

analyzed independently

K1

K2

F

P

dF

P

Loops hierarchy

d

F

0

P

d=P-F F = k2d+ k3d

K3

II. parallel loops

K1

dd

dD

K1

K2

F

P

dF

P

Loops hierarchy

d

F

0

P

d=P-F F = k2d+ k3d

K3

II. parallel loops

K1

dd

dD

K1

K2

F

P

dF

P

Loops hierarchy

K3

III. cascade of loops

K1

dd

dD

MotorSensory

BrainstemLoop

FacialNucleus

-

Zona Incerta

ex

tra

lem

nis

ca

l

lem

nis

cal

par

alem

nis

cal

Cerebellar/Olivary

VLThalamicNuclei

Vibrissae

Thalamus

Cortex

POm

TrigeminalGanglion

PrimaryMotor CortexSecondary

SuperiorColliculus

+

Red Nucleus

Reticular Formation

Brainstem Reticular

Nucleus

Pontine

PrimarySensory Cortex

VPM-dm

VPM-vl

TrigeminalNuclei

Sensory-motor loops of the vibrissal system

MotorSensory

BrainstemLoop

FacialNucleus

-

Zona Incerta

ex

tra

lem

nis

ca

l

lem

nis

cal

par

alem

nis

cal

Cerebellar/Olivary

VLThalamicNuclei

Vibrissae

Thalamus

Cortex

POm

TrigeminalGanglion

PrimaryMotor CortexSecondary

SuperiorColliculus

+

Red Nucleus

Reticular Formation

Brainstem Reticular

Nucleus

Pontine

PrimarySensory Cortex

VPM-dm

VPM-vl

TrigeminalNuclei

Sensory-motor loops of the vibrissal system

MotorSensory

BrainstemLoop

FacialNucleus

-

Zona Incerta

ex

tra

lem

nis

ca

l

lem

nis

cal

par

alem

nis

cal

Cerebellar/Olivary

VLThalamicNuclei

Vibrissae

Thalamus

Cortex

POm

TrigeminalGanglion

PrimaryMotor CortexSecondary

SuperiorColliculus

+

Red Nucleus

Reticular Formation

Brainstem Reticular

Nucleus

Pontine

PrimarySensory Cortex

VPM-dm

VPM-vl

TrigeminalNuclei

Sensory-motor loops of the vibrissal system

MotorSensory

BrainstemLoop

FacialNucleus

-

Zona Incerta

ex

tra

lem

nis

ca

l

lem

nis

cal

par

alem

nis

cal

Cerebellar/Olivary

VLThalamicNuclei

Vibrissae

Thalamus

Cortex

POm

TrigeminalGanglion

PrimaryMotor CortexSecondary

SuperiorColliculus

+

Red Nucleus

Reticular Formation

Brainstem Reticular

Nucleus

Pontine

PrimarySensory Cortex

VPM-dm

VPM-vl

TrigeminalNuclei

Sensory-motor loops of the vibrissal system

MotorSensory

BrainstemLoop

FacialNucleus

-

Zona Incerta

ex

tra

lem

nis

ca

l

lem

nis

cal

par

alem

nis

cal

Cerebellar/Olivary

VLThalamicNuclei

Vibrissae

Thalamus

Cortex

POm

TrigeminalGanglion

PrimaryMotor CortexSecondary

SuperiorColliculus

+

Red Nucleus

Reticular Formation

Brainstem Reticular

Nucleus

Pontine

PrimarySensory Cortex

VPM-dm

VPM-vl

TrigeminalNuclei

Sensory-motor loops of the vibrissal system

PCT Perceptual Control

TheoryW.T. Powers, 1973

“an organism's behavior is a means of controlling its

perceptions.” “a control system does not control

what it does; it controls what it senses. ”

MotorSensory

BrainstemLoop

FacialNucleus

-

Zona Incerta

ex

tra

lem

nis

ca

l

lem

nis

cal

par

alem

nis

cal

Cerebellar/Olivary

VLThalamicNuclei

Vibrissae

Thalamus

Cortex

POm

TrigeminalGanglion

PrimaryMotor CortexSecondary

SuperiorColliculus

+

Red Nucleus

Reticular Formation

Brainstem Reticular

Nucleus

Pontine

PrimarySensory Cortex

VPM-dm

VPM-vl

TrigeminalNuclei

Sensory-motor loops of the vibrissal system

Standard conceptionMotor Control Theory

M Jordan and others

“perceptions are means of controlling an organism's behavior

” “a control system does control what

it does, using what it senses. ”

so far lecture 10


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