Existing neural network models are capable of tracking linear trajectories of moving visual objects. This paper describes an additional neural mechanism, disfacilitation, that enhances the ability of a visual system to track curved trajectories. The added mechanism combines information about an object's trajectory with information about changes in the object's trajectory, to improve the estimates for the object's next probable location. Computational simulations are presented that show how the neural mechanism can learn to track the speed of objects and how the network operates to predict the trajectories of accelerating and decelerating objects.

Three adaptive rules are combined in a neural network simulation: an anti-Hebbian inhibitory learning rule, a variant of a Hebbian excitatory learning rule, and a Weber law neuron-growth rule. The neuron-growth rule permits the network to learn and classify input patterns despite variations in their spatial scale. The inhibitory learning rule permits superposition of multiple simultaneous neural activations (multiple winners) under strictly regulated circumstances, instead of forcing winner-take-all pattern classifications. The selected multiple activations can represent uncertainty or multiplicity in perception and pattern recognition. Such behavior is useful in representing visual ambiguity in the aperture problem, determining visual depth from motion parallax, forming a category-recognition network in a complex environment, and representing visual transparency. The techniques illustrate how higher-level pattern sensitivities may self-organize in complex perceptual environments in the presence of multiple scales, multiple patterns, and uncertainty