Abstract
A neural network model of biophysical neurons in the midbrain is presented to drive a muscle fiber oculomotor plant during horizontal monkey saccades. Neural circuitry, including omnipause neuron, premotor excitatory and inhibitory burst neurons, long lead burst neuron, tonic neuron, interneuron, abducens nucleus, and oculomotor nucleus, is developed to examine saccade dynamics. The time-optimal control strategy by realization of agonist and antagonist controller models is investigated. In consequence, each agonist muscle fiber is stimulated by an agonist neuron, while an antagonist muscle fiber is unstimulated by a pause and step from the antagonist neuron. It is concluded that the neural network is constrained by a minimum duration of the agonist pulse and that the most dominant factor in determining the saccade magnitude is the number of active neurons for the small saccades. For the large saccades, however, the duration of agonist burst firing significantly affects the control of saccades. The proposed saccadic circuitry establishes a complete model of saccade generation since it not only includes the neural circuits at both the premotor and motor stages of the saccade generator, but also uses a time-optimal controller to yield the desired saccade magnitude.
Highlights
Saccades are described as fast eye movements in which a target is tracked by registering the image of that target on the fovea
The persistent firing neuron stems from the Izhikevich neuron model [9], the habituating synapse is a conductance-based model, and the motor neuron captures the essence of the Hodgkin Huxley (HH) model [10]
To develop the quantitative computational models that establish the basis of this functional neural network model, we described the saccade burst generator dynamics
Summary
Saccades are described as fast eye movements in which a target is tracked by registering the image of that target on the fovea. The persistent firing neuron stems from the Izhikevich neuron model [9], the habituating synapse is a conductance-based model, and the motor neuron captures the essence of the Hodgkin Huxley (HH) model [10] These studies provide abundant evidence that an SNN is well suited to evoke the properties of the firing patterns of the premotor neurons during the pulse and slide phases of a saccade. Investigation of muscle fiber model is imperative because it allows for recognizing the effects of the firing of individual neurons, as well as the number of active neurons firing maximally, in controlling the saccades This investigation as well provides an optimum fit for the agonist and antagonist neural controllers to match the experimental data for the small saccades. The terms “motoneurons” and “agonist (antagonist) neurons” are substitutable in this paper
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