papers on stereo and motor modeling available

[Ning Qian]

Dear Colleagues,

The pdf files of two preprints from my lab are available at the links 
below.  The first paper is about a multi-scale and multi-orientation 
stereo model, and the second is on comparing models of motor planning. 
The pdf files of some old papers, including the one on protein structure 
prediction that several people asked me for, can also be find at the 
same site http://brahms.cpmc.columbia.edu .

Best regards,

Ning


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A Coarse-to-fine Disparity Energy Model with both Phase-shift and 
Position-shift Receptive Field Mechanisms

Yuzhi Chen and Ning Qian, Neural Computation, 2004, in press.

Numerous studies suggest that the visual system uses both phase- and 
position-shift receptive field (RF) mechanisms for the processing of 
binocular disparity. Although the difference between these two 
mechanisms has been analyzed before, previous work mainly focused on 
disparity tuning curves instead of population responses. However, tuning 
curve and population response can exhibit different characteristics, and 
it is the latter that determines disparity estimation. Here we 
demonstrate, in the framework of the disparity energy model, that for 
relatively small disparities, the population response generated by the 
phase-shift mechanism is more reliable than that generated by the 
position-shift mechanism. This is true over a wide range of parameters 
including the RF orientation. Since the phase model has its own 
drawbacks of underestimating large stimulus disparity and covering only 
a restricted range of disparity at a given scale, we propose a 
coarse-to-fine algorithm for disparity computation with a hybrid of 
phase-shift and position-shift components. In this algorithm, disparity 
at each scale is always estimated by the phase-shift mechanism to take 
advantage of its higher reliability. Since the phase based estimation is 
most accurate at the smallest scale when the disparity is 
correspondingly small, the algorithm iteratively reduces the input 
disparity from coarse to fine scales by introducing a {\em constant} 
position-shift component to all cells for a given location in order to 
offset the stimulus disparity at that location. The model also 
incorporates orientation pooling and spatial pooling to further enhance 
reliability. We have tested the algorithm on both synthetic and natural 
stereo images, and found that it often performs better than a simple 
scale averaging procedure.

http://brahms.cpmc.columbia.edu/publications/stereo-scale.pdf



Different Predictions by the Minimum Variance and Minimum Torque-Change 
Models on the Skewness of Movement Velocity Profiles

Hirokazu Tanaka, Meihua Tai and Ning Qian, Neural Computation, 2004, in 
press.

We investigated the differences between two well-known optimization 
principles for understanding movement planning: the minimum variance 
(MV) model of Harris \& Wolpert and the minimum torque-change (MTC) 
model of Uno et al. Both models accurately describe the properties of 
human reaching movements in ordinary situations (e.g. nearly straight 
paths and bell-shaped velocity profiles). However, we found that the two 
models can make very different predictions when external forces are 
applied or when the movement duration is increased. We considered a 
second-order linear system for the motor plant that has been used 
previously to simulate eye movements and single-joint arm movements, and 
were able to derive analytical solutions based on the MV and MTC 
assumptions. With the linear plant, the MTC model predicts that the 
movement velocity profile should always be symmetric, independent of the 
external forces and movement duration. In contrast, the MV model 
strongly depends on the movement duration and the system's degree of 
stability; the latter in turn depends on the total forces. The MV model 
thus predicts a skewed velocity profile under many circumstances. For 
example, it predicts that the peak location should be skewed toward the 
end of the movement when the movement duration is increased in the 
absence of any elastic force. It also predicts that with appropriate 
viscous and elastic forces applied to increase the system stability, the 
velocity profile should be skewed toward the beginning of the movement. 
The velocity profiles predicted by the MV model can even show 
oscillations when the plant becomes highly oscillatory. Our analytical 
and simulation results suggest specific experiments for testing the 
validity of the two models.

http://brahms.cpmc.columbia.edu/publications/motor-models.pdf



-- 
Ning Qian, Ph. D.
Associate Professor
Ctr. Neurobiology & Behavior
Columbia University / NYSPI
Kolb Annex, Rm 519
1051 Riverside Drive, Box 87
New York, NY 10032, USA

http://brahms.cpmc.columbia.edu
nq6 at columbia.edu
212-543-6931 ext 600 (Office)
212-543-5816 (Fax)