Modeling of saccadic movements using neural networks

We propose a new computational model for mimicking the behavior of a human eye movement during saccades. The different characteristics of two types of saccades such as a reflexive saccade and an intentional saccade are reflected on the proposed model. We have divided the visual pathway for generating a saccadic eye movement into three parts, of which each part has been modeled using different neural networks. The visual pathway from the visual receptors to the visual cortex including the frontal eye field is modeled by the self-organizing feature map, and the visual pathway from the visual cortex to the superior colliculus is modeled by a modified learning vector quantization (LVQ) network. The visual pathway from the superior colliculus to the motoneuron is modeled by a multilayer neural network learned by error backpropagation algorithm. Experimental results from computer simulation show that the proposed computational model is able to mimic well the behavior of the human eye movement for two different saccades.