A model of visual–spatial memory across saccades

This paper describes a neural network model that directs saccades back to targets after they disappear and other saccades intervene. This is a simple example of knowing where something is after it is no longer visible and the observer has moved. These tasks require a short-term memory that can store continuous values of spatial location. The model was generated by training a neural network with a recurrently connected hidden layer to specify memory-guided saccades. The trained network maintains stored locations accurately for a few seconds. It uses a leaky integrator mechanism in which there is a slow decay of the stored value to a small number of fixed point attractors. Similar mechanisms have been used to model oculomotor integration (Cannon, S., Robinson, D., & Shamma, S. (1983). A proposed neural network for the integrator of the oculomotor system. Biological Cybernetics, 49, 127-136; Seung, H. (1998). Continuous attractors and oculomotor control. Neural Networks, 11, 1253-1258). The mechanism is robust to parameters such as the input and output format and the constraints in training. However, the receptive field properties of the hidden units do depend on these parameters. It was possible to find biologically plausible parameters that produced hidden unit behavior similar to that of real neurons involved in saccade memory. In particular, training the model to simultaneously represent the target location in both eye- and head-based reference frames produces units similar to neurons in parietal saccade areas.