The organization of primary visual cortex has been heavily studied for nearly 50 years, and in the last 20 years functional imaging has provided high-resolution maps of its tangential organization. Recently, however, the usefulness of maps like those of orientation and spatial frequency (SF) preference has been called into question because they do not, by themselves, predict how moving images are represented in V1. In this review, we discuss a model for cortical responses (the spatiotemporal filtering model) that specifies the types of cortical maps needed to predict distributed activity within V1. We then review the structure and interrelationships of several of these maps, including those of orientation, SF, and temporal frequency preference. Finally, we discuss tests of the model and the sufficiency of the requisite maps in predicting distributed cortical responses. Although the spatiotemporal filtering model does not account for all responses within V1, it does, with reasonable accuracy, predict population responses to a variety of complex stimuli.

Neuronal autofluorescence, which results from the oxidation of flavoproteins in the electron transport chain, has recently been used to map cortical responses to sensory stimuli. This approach could represent a substantial improvement over other optical imaging methods because it is a direct (i.e., nonhemodynamic) measure of neuronal metabolism. However, its application to functional imaging has been limited because strong responses have been reported only in rodents. In this study, we demonstrate that autofluorescence imaging (AFI) can be used to map the functional organization of primary visual cortex in both mouse and cat. In cat area 17, orientation preference maps generated by AFI had the classic pinwheel structure and matched those generated by intrinsic signal imaging in the same imaged field. The spatiotemporal profile of the autofluorescence signal had several advantages over intrinsic signal imaging, including spatially restricted fluorescence throughout its response duration, reduced susceptibility to vascular artifacts, an improved spatial response profile, and a faster time course. These results indicate that AFI is a robust and useful measure of large-scale cortical activity patterns in visual mammals.

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The organization of cat primary visual cortex has been well mapped using simple stimuli such as sinusoidal gratings, revealing superimposed maps of orientation and spatial frequency preferences. However, it is not yet understood how complex images are represented across these maps. In this study, we ask whether a linear filter model can explain how cortical spatial frequency domains are activated by complex images. The model assumes that the response to a stimulus at any point on the cortical surface can be predicted by its individual orientation, spatial frequency, and temporal frequency tuning curves. To test this model, we imaged the pattern of activity within cat area 17 in response to stimuli composed of multiple spatial frequencies. Consistent with the predictions of the model, the stimuli activated low and high spatial frequency domains differently: at low stimulus drift speeds, both domains were strongly activated, but activity fell off in high spatial frequency domains as drift speed increased. To determine whether the filter model quantitatively predicted the activity patterns, we measured the spatiotemporal tuning properties of the functional domains in vivo and calculated expected response amplitudes from the model. The model accurately predicted cortical response patterns for two types of complex stimuli drifting at a variety of speeds. These results suggest that the distributed activity of primary visual cortex can be predicted from cortical maps like those of orientation and SF preference generated using simple, sinusoidal stimuli, and that dynamic visual acuity is degraded at or before the level of area 17.

To determine the organization of spatial frequency (SF) preference within cat Area 17, we imaged responses to stimuli with different SFs using optical intrinsic signals (ISI) and flavoprotein autofluorescence (AFI). Previous studies have suggested that neurons cluster based on SF preference, but a recent report argued that SF maps measured with ISI were artifacts of the vascular bed. Because AFI derives from a non-hemodynamic signal, it is less contaminated by vasculature. The two independent imaging methods produced similar SF preference maps in the same animals, suggesting that the patchy organization of SF preference is a genuine feature of Area 17.