An updated SIMULINK model of the horizontal saccade generator is described in this report. The neural network model incorporates the superior colliculus as the saccade initiator and the cerebellum as the saccade terminator. The neural burst generator model uses a first-order time optimal (burst discharge-saccade amplitude independent) controller, and is based on microelectrode recordings, eye movement measurements and systems control theory.

The neural circuit consists of the neurones in the paramedian pontine reticular formation (burst, tonic and pause cells), the vestibular nucleus, abducens nucleus, oculomotor nucleus, cerebellum, substantia nigra, nucleus reticularis tegmenti pontis, the thalamus, the deep layers of the superior colliculus, and the oculomotor plant for each eye. Agonist burst cell activity begins with the maximal firing due to an error between the target and the eye position, and continues until the internal eye position in the cerebellar vermis reaches the desired position, then decays to zero. The cerebellar vermis is responsible for adapting the duration of the burst firing based on the initial eye position of the eye. There are two sets of neural integrators in the neural network. One operates within the cerebellar vermis to predict the width of the pulse, and the other within the paramedian pontine reticular formation to maintain the eyes at their destination. Antagonist neural activity inhibited during the agonist burst activity. After the agonist burst, antagonist neural activity rises with a stochastic rebound burst due to input from the fastigial nucleus, and then falls to a tonic firing level necessary to keep the eye at its destination. The onset of antagonist tonic firing is stochastic, weakly co-ordinated with the end of the agonist burst, and under cerebellar control. A linear homeomorphic oculomotor plant is used that is fourth-order linear homeomorphic with state variables of angular position, angular velocity, angular acceleration and angular jerk, and with inputs of agonist and antagonist active state tension. This model depicts the agonist and antagonist (lateral and medial rectus) muscles, and the eyeball.

Each of the neural sites in the model fire as predicted by the experimental data and simulate fast eye movements with main sequence characteristics. The neural network successfully simulates (predicts) horizontal saccades of all sizes, including microsaccades, under the time optimal controller and using parameters based on physiological evidence. A common mechanism based on cerebellar gating explains a number of different saccade types, including dynamic overshoot, glissadic overshoot and undershoots, and undershoot.