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The nervous system maps high-dimension sensory inflow to low-dimension motor outputs during postural responses
J. Lucas McKay1 and Lena H. Ting2
1Electrical and Computer Engineering, 2The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology / Emory University
IntroductionMultiple sources of sensory information are used in patterning appropriate postural responses (Peterka, 2002). Despite this rich sensory inflow, muscle activity during the automatic postural response (APR) in cats is composed of a small number of underlying muscle synergies (Torres-Oviedo et al., 2006). This suggests that the nervous system may map high-dimension sensory information to low-dimension motor outputs during the patterning of the postural response. Such a noninvertible sensorimotor transformation would be consistent with sparse coding schemes observed in sensory processing (Olshausen and Field, 2004). However, it is also possible that sensory information is low-dimension, resulting in similarly low-dimension motor outputs.
SensoryInput
Low-Dimension Sensory InputLimits Motor Output
MotorOutput
Dimension
SensoryInput
Sensorimotor TransformationLimits Motor Output
MotorOutput
Dimension
We hypothesized that muscle synergies during postural responses •to perturbations arise from neural constraints rather than the low dimension of the available sensory information. Somatosensory information from muscles throughout the body is necessary • and sufficient for the generation of appropriate postural responses (Stapley et al., 2002). Visual and vestibular information are used to modulate responses (Inglis and Macpherson 1995). We therefore estimated and compared the dimension of somatosensory • sensory information and motor responses during postural tasks in cats.We predicted that sensorimotor transformations during postural responses • would reduce high sensory input dimension to low motor output dimension.
Methods1 Postural perturbation paradigm temporally
dissociates sensory inputs from motor outputs.Motor outputs follow sensory inputs by a long delay• 16 directions• in the horizontal plane; 15 cm/s vel., 5 cm amp. 3 healthy, unrestrained cats - 365 previously collected trials total•
0-30 ms 120-200 ms
PerturbationOnset
60-140 ms
ProcessingDelay
MechanicalDelay
Disturbance EMG ResponsePerturbation
SOMATOSENSORYINPUT DIMENSION
MOTOR OUTPUTDIMENSION
2DSTIMULUS
DELAY
SOMATOSENSORY INPUTS (0-30 ms)Biomechanical disturbance = 32 Joint Angles• from across the body, 32 Joint Angular Velocities, 12 Ground-reaction forces at the feet
MOTOR OUTPUTS (60-120 ms, 120-180 ms)Neuromotor response (60-120 ms): 16 left hindlimb EMGS• Biomechanical response (120-180 ms) = • Joint angles, Joint angular velocities, forces.Neural delay (60 ms) + electromechanical delay (60 ms) = Biomechanical • response delay (120)
2 Perturbations cause complex joint angle changes in different directions.
Diagonal perturbations are not a superposition of rightwards and • forwards perturbations.
0
200
400
600Time (ms) MCP
Wrist
Elbow
Shoulder
Scapula
Pelvis
Hip
Knee
Ankle
MTP
60°Perturbation
0°Perturbation
2D STIMULUS
0°
90°
270°
180°
3 Input and output time windows were examined.Biomechanical variables: input (0-30 ms) and output (120-200 ms) during • each trial.EMGs: output (initial burst of the APR, 60-120 ms; Ting and Macpherson • 2004) during each trial.
2.5 cm
RFEM
MTPAnkleKnee
Hip
MTPAnkleKnee
Hip
FxFyFz
SEMP
PlatformPosition
JointAngles
JointAngular
Velocities
GroundReaction
Forces
5°
50 °/sec
2.5 N
-250 0 500 1000 -250 0 500Time (ms) Time (ms)
1000
60°Perturbation
0°Perturbation
SENSORYINPUT
MOTOROUTPUT
4 Data Dimension was estimated with PCA.Mean values for each trial were assembled into matrices•
LimbForces
Sensory Input,Motor Output
EMGs
Motor Output
Joint Angles,Joint Velocities
Sensory Input,Motor Output
TrialsVariables
TrialsVariables
TrialsVariables
Dimension of each matrix was estimated as the number of singular values • of the correlation matrix ≥ 0.95.Criterion using R• 2 yields very high (>20) numbers of components, likely due to the large number of experimental variables.
Results5 Both sensory information and motor outputs exhibit significant correlation structure when
compared to shuffled data.
# Components
Sing
ular
Val
ues
0 32
0.95Threshold
0
3
6
# Components
Sensory InputDimension = 8
Sensory InputDimension = 3
NNMFDimension = 4Sensory Input
Dimension = 11
Motor OutputDimension = 8
Motor OutputDimension = 2
Motor OutputDimension = 3
Motor OutputDimension = 5
Sing
ular
Val
ues
Sensory
0 320
3
6
# Components
Sing
ular
Val
ues
0 120
3
6
# Components
Joint Angles Joint Angular Velocities Forces EMG
Sing
ular
Val
ues
NN
MF
VAF
(%)
0 16
3 100
50
6
Motor
Sensory (Shuffled)
Motor (Shuffled)
81.7
AcknowledgmentsWe thank Jane Macpherson and the other researchers responsible for collecting the experimental data used in this retrospective study. Supported by NIH Grant HD46922 to LHT.
ConclusionsPlanar translation perturbations during standing balance are made more complex due to the effects of gravity, introducing 3D joint disturbances throughout the body.
Translation perturbations to standing balance are not equivalent to planar • reaching tasks, or other tasks where 2D motion is imposed by the experimental apparatus (e.g., Kurtzer et al., 2006).
The nervous system maps high-dimension somatosensory information to lower-dimension motor responses during translation perturbations.
Reduced dimension in sensory information due to musculoskeletal dynamics • is further reduced by the sensorimotor transformation during the postural response.
Dimension estimates were pooled across cats and subjected to three-factor ANOVA.
Epoch: Input vs. Output•Data Type: Joint angle, Joint •angular velocity, Force, EMGAnimal•
01
5
10
1 2 3 4 5
PCA Reconstruction
Cor
rela
tion
Mat
rixS
ingu
lar
Valu
es
# Components
ExcludeComponents
< 0.95
6 Sensory inputs are > 2D, although perturbations are 2D by construction.
7.4 (0.2) *ns
5.3 (1.0)3.3 (0.6)
Forces
10.3 (0.6)‡
JointAngles
SOMATOSENSORY INPUTS MOTOR OUTPUTS
Dimension
STIMULUSDIMENSION = 2
8.7 (1.2)
‡
Joint AngularVelocities
3.7 (1.2)
EMG
2.3 (0.6)
Forces
6.7 (1.5)
JointAngles
8.7 (1.2)
Joint AngularVelocities
3.7 (1.2)
PCA
3.7 (0.6)
NNMF0
2
10
* ANOVA, F (1,14) = 8.0; p < 0.013‡ t-test, H0: mean = 2; Bonferroni correction, p < 0.00125
EMGDimension
0
6
t-test; p >> 0.05. (cf. Torres-Oviedo et al., 2006)
8 EMG dimension estimates using PCA are consistent with previous results using NNMF.
7 Motor outputs are lower-dimensional than sensory inputs.
Neural representation and control
Biomechanical interactions with environment
motorbinding
motoneurons
sensorybinding
sensory receptors
estimatedsensory events
desired motoroutputs
hierarchal selection and modulation
sparsesensory and motor
representations
Chiel, Ting, Ekeberg, and Hartmann, 2009.
Symposium:
The Brain in Its Body: Motor Control and Sensing in a
Biomechanical Context.
Wednesday, 1:30-4:00 PM, S100B
ReferencesChiel HJ, Ting LH, Ekeberg O, and Hartmann MJZ. The Brain in Its Body: Motor Control and Sensing in a Biomechanical Context. J Neurosci 29: 12807-12814, 2009.
Inglis JT, and Macpherson JM. Bilateral labyrinthectomy in the cat: effects on the postural response to translation. J Neurophysiol 73: 1181-1191, 1995.
Kurtzer I, Pruszynski JA, Herter TM, and Scott SH. Primate Upper Limb Muscles Exhibit Activity Patterns That Differ From Their Anatomical Action During a Postural
Task. J Neurophysiol 95: 493-504, 2006.
Olshausen BA, and Field DJ. Sparse coding of sensory inputs. Curr Opin Neurobiol 14: 481-487, 2004.
Peterka RJ. Sensorimotor integration in human postural control. J Neurophysiol 88: 1097-1118, 2002.
Stapley PJ, Ting LH, Hulliger M, and Macpherson JM. Automatic postural responses are delayed by pyridoxine-induced somatosensory loss. J Neurosci 22: 5803-5807,
2002.
Ting LH, and Macpherson JM. Ratio of shear to load ground-reaction force may underlie the directional tuning of the automatic postural response to rotation and
translation. J Neurophysiol 92: 808-823, 2004.
Torres-Oviedo G, Macpherson JM, and Ting LH. Muscle synergy organization is robust across a variety of postural perturbations. J Neurophysiol 96: 1530-1546, 2006.