When the system is exposed to many motion sequences containing occlusion and disocclusion events, the stage gradually undergoes a self-organized bifurcation into two distinct pools of neurons, as shown in Figure 4 . The learning procedure causes the representation of each trajectory to split into two parallel opponent channels. The Visible and Invisible channel pair for a single trajectory are shown. Solid arrows indicate excitatory connections. Hash marks indicate time-delayed connections.
Broken arrows indicate inhibitory connections.
The object is represented successively at each moment by neuron activations in the Visible channel. When the object reaches the region indicated by gray shading, it disappears behind an occluder. The network responds by successively activating neurons in the Invisible channel. These neuron activations amodally represent the network's inference that the object continues to exist and continues to move while it is invisible. If any such neurons exist, we can call them occlusion indicators.