Leishmania Genome Dynamics during Environmental Adaptation ...
Dynamics of sensorimotor adaptation
20
Dynamics of sensorimotor adaptation Sen Cheng, Philip N Sabes University of California, San Francisco Annual Swartz-Sloan Centers Meeting, 26 th July 2005
description
Dynamics of sensorimotor adaptation. Sen Cheng, Philip N Sabes University of California, San Francisco Annual Swartz-Sloan Centers Meeting, 26 th July 2005. A simple sensorimotor task. Motivation and outline. block design. trial-by-trial dynamics What is the learning rule of adaptation? - PowerPoint PPT Presentation
Transcript of Dynamics of sensorimotor adaptation
Dynamics of sensorimotor adaptation
Sen Cheng, Philip N SabesUniversity of California, San Francisco
Annual Swartz-Sloan Centers Meeting, 26th July 2005
2
A simple sensorimotor task
3
Motivation and outline
trial-by-trial dynamics
What is the learning rule of adaptation?1. What signals drive
learning?
2. Noise in the learning process?
3. Spatial anisotropies?
More powerful correlation between behavior and neural activity.
Steady-state of adaptation Compare average behavior
pre- and post-exposure
block design
4
Virtual reality setup
5
Concurrent test and exposure
6
Model for dynamics of adaptation
),0(~),,0(~
1
RNtrQNtq
ttt
tttt
rxy
qBuAxx
Linear dynamical system (LDS) ut : inputs (?)
xt : internal state, planned/expected reach error
yt : actual reach error
qt : learning noise
rt : motor noise
general state space model
7
1. What signals drive learning?
2. Noise in the learning process?
3. Spatial anisotropies?
Questions
8
Two candidate learning signals
System identification with expectation-maximization (EM) algorithm, Cheng and Sabes, 2005, submitted
Learning equation with two input signals
t : visual error
t : perturbation/ discrepancy betw. vision and proprioception
),0(~),,0(~
1
RNtrQNtq
ttt
tttt
rxy
qBuAxx
ttt
ttt
rx
y
ttttt qHGAxx 1
9
Sample data and vis-model fit
),0(~),,0(~
1
RNtrQNtq
ttt
tttt
rxy
qBAxx
perturbation
reach error
model prediction
10
Portmanteau test for serial autocorrelations
tt yy ˆIs the sequence of residuals a white noise sequence?
010
1
2 tr1
CCCCn
nPm
m
)ˆ)(ˆ(1
1
ttt
n
tt yyyy
nC
Portmanteau statistic (Hosking, 1980)
Residual autocorrelations Portmanteau test for vis-model
11
pert-model fit to sample data
),0(~),,0(~
1
RNtrQNtq
ttt
tttt
rxy
qBAxx
perturbation
reach error
vis-model
pert-model
12
Portmanteau test cannot distinguish models
for vis-modelfor pert-model
13
Likelihood ratio test (LRT) for nested models
M1: no input
M2: pert
M4: pert and vis
M3: vis error
p < 10-4 (n=18)p < 10-4 (n=18)
p=0.006 (n=1), p>0.067 (n=17) p>0.22 (n=18)
)(~)data|(
)data|(log2 2
ijj
i
ML
ML
ttt qAxx 1
tttt qGAxx 1
ttttt qHGAxx 1
tttt qHAxx 1
14
1. What signals drive learning?
2. Noise in the learning process?
3. Spatial anisotropies?
Questions
15
The signal that drives learning
tttt qGAxx 1
ttt qAxx 1
apply to no feedback (noFB) reaches:
Estimated modelspert-model
vis-model
tttt qHAxx 1
pert-model
vis-model
16
1. What signals drive learning? 2. Noise in the learning process?
3. Spatial anisotropies?
Questions
17
Learning noise
stochastic
pert
LRT (n=18)
p < 10-4
noFB
LRT (n=18)p < 0.0003
),0(~),,0(~
1
RNtrQNtq
ttt
tttt
rxy
qBuAxx
x
18
1. What signals drive learning? 2. Noise in the learning process? 3. Spatial anisotropies?
Questions
19
Anisotropy in learning and noise
*
),0(~),,0(~
1
RNtrQNtq
ttt
tttt
rxy
qBAxx
20
Conclusions
LDS are good models for adaptation dynamics
New insights into adaptation1. Visual error drives adaptation predominantly
2. There is learning noise
3. Dynamics are anisotropic
Can now correlate trial-by-trial changes of behavior with neural activity.
supported by the Swartz foundation
