Visual cortex neurons in monkeys and cats: Detection, discrimination, and identification

A descriptive function method was used to measure the detection, discrimination, and identification performance of a large population of single neurons recorded from within the primary visual cortex of the monkey and the cat, along six stimulus dimensions: contrast, spatial position, orientation, spatial frequency, temporal frequency, and direction of motion. First, the responses of single neurons were measured along each stimulus dimension, using analysis intervals comparable to a normal fixation interval (200 ms). Second, the measured responses of each neuron were fitted with simple descriptive functions, containing a few free parameters, for each stimulus dimension. These functions were found to account for approximately 90% of the variance in the measured response means and response standard deviations. (A detailed analysis of the relationship between the mean and the variance showed that the variance is proportional to the mean.) Third, the parameters of the best-fitting descriptive functions were utilized in conjunction with Bayesian (optimal) decision theory to determine the detection, discrimination, and identification performance for each neuron, along each stimulus dimension. For some of the cells in monkey, discrimination performance was comparable to behavioral performance; for most of the cells in cat, discrimination performance was better than behavioral performance. The behavioral contrast and spatial-frequency discrimination functions were similar in shape to the envelope of the most sensitive cells; they were also similar to the discrimination functions obtained by optimal pooling of the entire population of cells. The statistics which summarize the parameters of the descriptive functions were used to estimate the response of the visual cortex as a whole to a complex natural image. The analysis suggests that individual cortical neurons can reliably signal precise information about the location, size, and orientation of local image features.