Article Figures & Data
Figures
- Figure 1.
Characterizing pattern-motion selectivity using random dot motion. A, Random dot stimuli contain components of all orientations. The velocity of a random dot stimulus (object velocity, red arrow) and the velocity of each orientation component (component velocity, blue arrow) are related by a cosinusoidal function. B, A single component velocity is consistent with two-object velocity at a faster speed. This indicates that CDS neurons respond to two directions away from the preferred direction at faster speeds. C, The direction-tuning curve of an ideal CDS neuron selective for upward motion. Different shadings represent different motion speeds. For speed ≤ optimal speed, the direction-tuning curve has a single peak (brighter lines). For speed > optimal speed, the direction-tuning curve is bimodal (darker lines). D, These direction-tuning curves across all speeds are converted to the 2D velocity-tuning map by plotting responses in polar coordinates. There is elongation of the response region orthogonal to the preferred direction. E, An ideal PDS neuron is selective for the same direction across all speeds. F, The 2D velocity tuning of an ideal PDS neuron does not show elongation.
- Figure 2.
An example PDS neuron. A, Direction-tuning curves across all speeds. Different colors denote different speeds, as indicated in the inset. Error bars indicate SEM. B, 2D velocity-tuning map. C, For CDS predictions, a periodic spline curve was interpolated to the direction-tuning data at the optimal speed. The direction tuning for a speed higher than the optimal speed was computed as the sum of two interpolated curves, each shifted by an amount determined from the ratio of the optimal speed to each speed. D, 2D velocity-tuning map of CDS predictions. E, For PDS predictions, the interpolated curve was used across all speeds. F, 2D velocity-tuning map of PDS predictions. The pattern and component correlations are noted on the top right of the 2D velocity-tuning maps.
- Figure 3.
An example CDS neuron. Conventions are the same as in Figure 2.
- Figure 4.
Pattern- and component-motion selectivity obtained from the responses to random dot motion. The Z-scored pattern correlation was plotted against the Z-scored component correlation. Gray symbols represent the examples in Figures 2 and 3.
- Figure 5.
Analysis of direction tuning for a Gabor and a plaid. A, For each neuron, we measured the direction-tuning curve for a Gabor and a plaid that was constructed by superimposing two Gabors drifting 120° apart. B, An example CDS neuron. The polar plots represent the direction-tuning curve for Gabors (left), plaids (middle), and the two predictions (right). The pattern prediction (red) was the same as the direction-tuning curve for Gabors. The component prediction (blue) was computed as the sum of two direction-tuning curves, each shifted by 60°. C, An example PDS neuron. D, Pattern and component-motion selectivity for plaids. Z-scored pattern correlations were plotted against Z-scored component correlations. Based on the correlation values, each neuron was classified as a PDS neuron (red), a CDS neuron (blue), or unclassified (black).
- Figure 6.
The distribution of pattern indices for random dot motion and plaids. A, The pattern index for random dot motion was plotted against the pattern index for plaids. Color denotes the classification, based on plaids. Many blue points (classified as CDS for plaids) lie above the diagonal line, suggesting pattern-motion selectivity. B, The pattern index for random dot motion (ordinate) was recalculated using direction tuning at the optimal speed and a speed just above it. The pattern index for plaids was also recalculated using the subsampled direction-tuning data at 45° and spline interpolation. C, The pattern indices for random dot motion and plaids were calculated using a standard computational model of MT neurons. The bandwidth of integration across orientations and inhibitory weights on V1 afferents were modified. Each line represents pattern indices for a unique inhibitory weight. For each inhibitory weight, from the top right to bottom left, the integration bandwidth across orientations decreased from 8 to 1. Gray dots represent a subpopulation of neurons from A whose preferred speed was <8°/s.
