In the primary visual cortex (V1), images are broken down into spatially-localized components by neurons having narrow-band orientation and spatial frequency (SF) tuning. Outputs of these neurons would make ideal inputs for a popular class of models of motion detection, i.e., motion energy models. Recent work in V1 and in the cortical motion area V5/MT,however, has shown that direction selective (DS) neurons compute motion with a fundamental spatial subunit that is small in scale and relatively fixed across the visual field (Livingstone et al., 2001, Neuron 30:781; Pack etal., 2006, JNeurosci 26:893). This presents a paradox: why should the visual system represent images using a wide array of SF-tuned channels and then only use a narrow range of those channels for motion computation, and why would spatial scaling with eccentricity not apply to the motion pathway? To test whether spatial integration varied with changes in stimulus structure, we made extracellular recordings from DS neurons in V1 and V5/MT in the anesthetized macaque monkey, and we varied the spatial scale of moving visual stimuli to determine the size of the displacements that were optimal at each scale. We used two stimuli, a sinusoidal grating and a two-bar stimulus that had been used by others. The sinusoidal grating patch was presented at a variety of SFs and stepped according to a random sequence at various displacements along the preferred axis of motion of the cell. The two-bar stimulus was the one used by Livingstone et al. except that we varied the width of the bars and the range over which they were presented across trials. We presented the same sets of stimuli to two commonly used models for DS neurons: a Reichardt detector and a motion energy model. We found that for the large majority of neurons, the optimal displacement increased as SF decreased for the sinusoidal grating stimulus, and the optimal displacement over all SFs for a neuron was highly correlated with the optimal SF of the neuron. Using the two-bar stimulus, we found that the optimal step size changed with the width of the bars being used. These experimental results suggest that motion is computed across a range of spatial scales and that there is not a fundamental, small step size that characterizes most neurons. Both the motion energy and Reichardt models were able to match the experimental results to some degree, but our motion energy model gave a better account for the shapes of the optimal displacement tuning curves for the two-bar stimuli. Thus, the observed changes of the scale of motion computations within and across cells appear to be an inherent property of simple models of motion detection.
|Publication status||Published - 2008|
|Event||Society for Neuroscience Annual meeting, 2008 - Washington DC, United States|
Duration: 15 Nov 2008 → 19 Nov 2008
|Conference||Society for Neuroscience Annual meeting, 2008|
|Period||15/11/08 → 19/11/08|