Figure 1.

Computing information represented in a ganglion cell array. A, We constructed a model whose inputs were images of natural scenes. Each pixel was represented by a cone with SNR estimated from the literature (see Materials and Methods). Postsynaptic to the cone, SNR was reduced to account for the reported loss of contrast sensitivity in the bipolar cell and ganglion cell (see Materials and Methods). The resulting cone signals were integrated with a difference-of-Gaussians weighting function with center and surround parameters obtained from real cells, which gave a spatial receptive field for each ganglion cell in the array (shown here for 2 cells). The integrated cone signal was mapped nonlinearly onto the dynamic response range of the ganglion cell (see Materials and Methods). We then computed, for a range of receptive field center separations, information represented by the array. B, In images of natural scenes, luminance correlations persisted across space, but contrast correlations decayed sharply. Correlations between luminance responses [responses of receptive field (RF) centers] persisted at large separations (circles; plotted here in units of σ). Correlations between contrast responses (responses of receptive field center − a balanced surround) vanished at ∼4σ (triangles). Luminance and contrast responses were independent at any separation (squares). Here σ = 12 image pixels.