Intrinsic high-frequency neural activities have been observed in the visual system of several species, but their functional significance for visual perception remains a fundamental puzzle in cognitive neuroscience. Spatiotemporal integration in the human visual system acts as a low-pass filter and makes the psychophysical observation of high-frequency activities very difficult. A computational model of retino-cortical dynamics (RECOD) is used to derive experimental paradigms that allow psychophysical studies of high-frequency neural activities. A reduced-parameter version of the model is used to quantitatively relate psychophysical data collected in two of these experimental paradigms. Statistical analysis shows that the model's account of the variance in the data is, in general, highly significant. We suggest that psychophysically measured oscillations reflect intrinsic neuronal oscillations observed in the visual cortex.

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.