A quantitative explanation of responses to disparity-defined edges in macaque V2.

Previous experiments have shown that V2 neurons respond to complex stimuli such as cyclopean edges (edges defined purely by binocular disparity), angles, and motion borders. It is currently unknown whether these responses are a simple consequence of converging inputs from a prior stage of processing (V1). Alternatively, they may identify edges in a way that is invariant across a range of visual cues defining the edge, in which case they could provide a neuronal substrate for scene segmentation. Here, we examine the ability of a simple feedforward model that combines two V1-like inputs to describe the responses of V2 neurons to cyclopean edges. A linear feedforward model was able to qualitatively reproduce the major patterns of response enhancement for cyclopean edges seen in V2. However, quantitative fitting revealed that this model usually predicts response suppression by some edge configurations and such suppression was rarely seen in the data. This problem was resolved by introducing a squaring nonlinearity at the output of the individual inputs prior to combination. The extended model produced extremely good fits to most of our data. We conclude that the responses of V2 neurons to complex stimuli such as cyclopean edges can be adequately explained by a simple convergence model and do not necessarily represent the development of sophisticated mechanisms that signal scene segmentation, although they probably constitute a step toward this goal.

Dynamics of spatial frequency tuning in macaque V1.

Spatial frequency tuning in the lateral geniculate nucleus of the thalamus (LGN) and primary visual cortex (V1) differ substantially. LGN responses are largely low-pass in spatial frequency, whereas the majority of V1 neurons have bandpass characteristics. To study this transformation in spatial selectivity, we measured the dynamics of spatial frequency tuning using a reverse correlation technique. We find that a large proportion of V1 cells show inseparable responses in spatial frequency and time. In several cases, tuning becomes more selective over the course of the response, and the preferred spatial frequency shifts from low to higher frequencies. Many responses also show suppression at low spatial frequencies, which correlates with the increases in response selectivity and the shifts of preferred spatial frequency. These results indicate that suppression plays an important role in the generation of bandpass selectivity in V1.

Suppression of neural responses to nonoptimal stimuli correlates with tuning selectivity in macaque V1.

Neural responses in primary visual cortex (area V1) are selective for the orientation and spatial frequency of luminance-modulated sinusoidal gratings. Selectivity could arise from enhancement of the cell's response by preferred stimuli, suppression by nonoptimal stimuli, or both. Here, we report that the majority of V1 neurons do not only elevate their activity in response to preferred stimuli, but their firing rates are also suppressed by nonoptimal stimuli. The magnitude of suppression is similar to that of enhancement. There is a tendency for net response suppression to peak at orientations near orthogonal to the optimal for the cell, but cases where suppression peaks at oblique orientations are observed as well. Interestingly, selectivity and suppression correlate in V1: orientation and spatial frequency selectivity are higher for neurons that are suppressed by nonoptimal stimuli than for cells that are not. This finding is consistent with the idea that suppression plays an important role in the generation of sharp cortical selectivity. We show that nonlinear suppression is required to account for the data. However, the precise structure of the neural circuitry generating the suppressive signal remains unresolved. Our results are consistent with both feedback and (nonlinear) feed-forward inhibition.