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Detecting spatial and temporal dot patterns in noise

The visual system can be thought of as an image processor that first reduces the dynamic retinal image to a temporal succession of noisy but redundant arrays of retinal ganglion cell signals and then reconstructs from these signals a stable representation of the external world. The process by which this reconstruction takes place is still poorly understood. An obvious requirement, however, is the capability to reject the noise in the individual neural signals. I am investigating the visual system's noise rejection capabilities by determining how much noise must be added to dot patterns to reduce them to detection threshold. The stimuli are patches of nonrandom dots surrounded by dynamic random dots of the same mean luminance and contrast. The non randomness, or coherence, of the stimulus patterns is controlled by randomizing a known percentage of stimulus dots in each frame of the dynamic display. The stimulus patterns can be limited to either spatial or temporal information. In addition to coherence, the size, duration and retinal location of the stimulus can be varied, as well as the temporal frequency, dot size, contrast and mean luminance of the entire display. Coherence thresholds are generally elevated by any operation that reduced the number of ganglion cells responding to the stimulus, either by reducing the stimulus area or duration or by limiting the response to a subset of ganglion cells (e.g., the receptive field overlap or response redundancy factor can be reduced by preferentially stimulating only one functional ganglion cell type, or by testing glaucoma patients with partially destroyed ganglion cell layers). The visual system thus appears to reduce noise effects by integrating neural responses that are correlated in either space or time.