Figure 36.

An illustration of how continuous spatial transformation (CT) learning would function in a network with a single-layer of forward synaptic connections between an input layer of neurons and an output layer. Initially the forward synaptic weights are set to random values. The top part (A) shows the initial presentation of a stimulus to the network in position 1. Activation from the (shaded) active input cells is transmitted through the initially random forward connections to stimulate the cells in the output layer. The shaded cell in the output layer wins the competition in that layer. The weights from the active input cells to the active output neuron are then strengthened using an associative learning rule. The bottom part (B) shows what happens after the stimulus is shifted by a small amount to a new partially overlapping position 2. As some of the active input cells are the same as those that were active when the stimulus was presented in position 1, the same output cell is driven by these previously strengthened afferents to win the competition again. The rightmost shaded input cell activated by the stimulus in position 2, which was inactive when the stimulus was in position 1, now has its connection to the active output cell strengthened (denoted by the dashed line). Thus the same neuron in the output layer has learned to respond to the two input patterns that have similar vector elements in common. As can be seen, the process can be continued for subsequent shifts, provided that a sufficient proportion of input cells stay active between individual shifts. (After Stringer et al., 2006.)