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Fig. 8.2. Effect of afferent normalization on V1 responses. The LGN response (b) to the activation in (a) is visualized by subtracting the OFF channel activation from the ON, and the V1 responses (c-e) by color coding each neuron according to how active it is and what orientation it prefers (as in Figure 6.5, except this network, from Section 10.2, is much larger). (c) Without afferent normalization (&gamma_{n =} 0), the network can respond only to the strongest contrasts in the image (as in Figure 6.5): The low-contrast oriented lines, such as those along the bottom of the chin, are lost. (d) When the afferent scale (&gamma_A) is increased, the network begins to respond to these lines as well, but its activation resulting from the high-contrast contours becomes widespread and unselective. (e) With normalization (&gamma_n = 80, &gamma_A = 30), the responses are largely invariant to input contrast, and instead are determined by how closely the input pattern matches the receptive field pattern of each neuron. The activations preserve the important features of the input, and the V1 activation pattern can be used as input to a higher level map for tasks such as face processing. Afferent normalization is therefore crucial for producing meaningful responses to natural inputs, which vary widely in contrast. Figure 8.3 shows how afferent normalization affects the responses of single neurons, which underlie these differences in the V1 response.