Click on the image to see a PDF version (for zooming in)

Fig. 15.1. Scaling retinal and cortical area. The small retina (a) and V1 (c) was scaled to a size 16 times larger (b,d) using Equation 15.1. To make it easier to compare map structure, especially in early iterations, the OR maps are plotted without selectivity in this chapter. The lateral inhibitory connections of one central neuron, marked with a small white square, are indicated in white outline. The simulation time and the number of connections scale approximately linearly with the area, and thus the larger network takes about 16 times more time and memory to simulate. For discrete input patterns like these oriented Gaussians, it is necessary to have more patterns to keep the total learning per neuron and per iteration constant. Because the inputs are generated randomly across the retina, each map sees a different stream of inputs, and so the patterns of orientation patches on the final maps differ. The area scaling equations are most useful for developing a model with a small area and then scaling it up to eliminate border effects and to simulate the full area of a corresponding biological preparation.