HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Patel, S. S. Articles by Ogmen, H. Search for Related Content PubMed PubMed Citation Articles by Patel, S. S. Articles by Ogmen, H.

Vergence Dynamics Predict Fixation Disparity

College of Optometry and Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204, U.S.A.

Bai-Chuan Jiang

College of Optometry, Nova Southeastern University, Ft. Lauderdale, FL 33328, U.S.A.

Haluk Ogmen

Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204, U.S.A.

The neural origin of the steady-state vergence eye movement error, called binocular fixation disparity, is not well understood. Further, there has been no study that quantitatively relates the dynamics of the vergence system to its steady-state behavior, a critical test for the understanding of any oculomotor system. We investigate whether fixation disparity can be related to the dynamics of opponent convergence and divergence neural pathways. Using binocular eye movement recordings, we first show that opponent vergence pathways exhibit asymmetric angle-dependent gains. We then present a neural model that combines physiological properties of disparity-tuned cells and vergence premotor cells with the asymmetric gain properties of the opponent pathways. Quantitative comparison of the model predictions with our experimental data suggests that fixation disparity can arise when asymmetric opponent vergence pathways are driven by a distributed disparity code.