DLM in the form presented here is computationally expensive. We have performed single recognition tasks with the complete system, but for the experiments referred to in Table 3 we have modified the system in several respects to achieve a reasonable speed. We split up the simulation into two phases. The only purpose of the first phase is to let the attention blob become aligned with the face in the input image. No modification of the connectivity was applied in this phase, and only one average model was simulated. Its connectivity was derived by taking the maximum synaptic weight over all real models for each link:
This attention period takes 1000 time steps. Then the complete system, including the attention blob, is simulated, and the individual connection matrices are subjected to DLM. Neurons in the model layers are not connected to all neurons in the image layer, but only to an patch. These patches are evenly distributed over the image layer with the same spatial arrangement as the model neurons themselves. This still preserves full translational invariance. Full rotational invariance is lost, but the jets used are not rotationally invariant in any case. The link dynamics is not simulated at each time step, but only after 200 simulation steps or 100 time units. During this time a running blob moves about once over all of its layer, and the correlation is integrated continuously. The simulation of the link dynamics is then based on these integrated correlations, and since the blobs have moved over all of the layers, all synaptic weights are modified. For further increase in speed, models that are ruled out by the winner-take-all mechanism are no longer simulated; they are just set to zero and ignored from then on (). The CPU time needed for the recognition of one face against a gallery of 111 models is approximately 10--15 minutes on a Sun SPARCstation 10-512 with a 50 MHz processor.
In order to avoid border effects, the image layer has a frame with a width of 2 neurons without any features or connections to the model layers. The additional frame of neurons helps the attention blob to move to the border of the image layer. Otherwise, it would have a strong tendency to stay in the center.