In a previous study (Keith, Smith & Crawford, sfn Abstracts 2004) we trained a 3-layer feed-forward neural network using back-propagation to perform the remapping of visual target positions across an intervening saccade. Units in the hidden layer showed two properties seen in LIP and deeper layers of the superior colliculus (SC): predictive remapping and open-ended receptive fields. In the current study we added dynamic elements, first in the form of recurrent connections between all hidden-layer units, with a one-time-step delay associated with this signal. The inputs to the network were the initial retinal error of the visual target, represented as a topographic array of units, and eye position and motor error, both represented by a push-pull pairs of units for each degree of freedom. The output of the network was the dynamic motor error of the saccade to the target. The network was trained using back-propagation-through-time to perform the remapping of the target across the intervening saccade. The netw...