Transformation of sensory signals into commands for saccadic eye movements: a neural network study

A biological plausible neural network which simulated the input-output transformation performed by primates during saccadic eye movements is constructed using a selective attention module and multi-layered neural networks with improved back propagation and a competitive learning algorithm. Simulation results show that the trained model can make fine saccades directed by the target. Representations and processing mechanisms in the saccade system are investigated. The features of most hidden units resemble those that have been observed in physiological recordings of neurons in primates visual cortex. The hidden layer even developed structures similar to those of area 7a.