...respectively.

The initial lateral excitation radius was 19 and was gradually decreased to 1 . The lateral inhibitory radius of each neuron was 47 , and inhibitory connections whose strength was below 0.00025 were pruned away at 30,000 iterations. The lateral inhibitory connections were preset to a Gaussian profile with $\sigma=100$, and the lateral excitatory connections to a Gaussian with $\sigma=15$. The lateral excitation $\gamma_e$ and inhibition strength $\gamma_i$ were both 0.9 . The learning rate $\alpha_{\rm A}$ was gradually decreased from 0.007 to 0.0015 , $\alpha_{\rm E}$ from 0.002 to 0.001 and $\alpha_{\rm I}$ was a constant 0.00025 . The lower and upper thresholds of the sigmoid were increased from 0.1 to 0.24 and from 0.65 to 0.88 , respectively. The number of iterations for which the lateral connections were allowed to settle at each training iteration was initially 9 , and was increased to 13 over the course of training. The parameter settings were identical to those of Sirosh (1995), and were not tuned or tweaked for the tilt aftereffect simulations. Small variations produce roughly equivalent results (Sirosh, 1995).