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A neuron-based time-optimal controller of horizontal saccadic eye movements.

Alireza Ghahari¹, John D Enderle

¹Department of Electrical Engineering, University of Connecticut, 260 Glenbrook Road, Storrs, Connecticut 06269-2247, USA.

International Journal of Neural Systems

|August 2, 2014

Summary

This study introduces a novel neural network model simulating midbrain neurons for controlling eye movements during human saccades. It details neural circuits and a time-optimal controller for investigating saccade dynamics.

Keywords:

Saccade burst firing circuit model neural network oculomotor plant time-optimal controller

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Area of Science:

Neuroscience
Computational Neuroscience
Biophysics

Background:

Human saccades are rapid eye movements crucial for visual perception.
Existing models often simplify neural circuitry or focus on specific saccade stages.

Purpose of the Study:

To develop a comprehensive neural network model of the midbrain circuitry controlling horizontal saccades.
To investigate saccade dynamics by incorporating premotor and motor neural stages.

Main Methods:

A neural network model of biophysical neurons in the midbrain was created.
The model includes key neural components: omnipause neurons, burst neurons, tonic neurons, interneurons, and relevant nuclei.
A time-optimal controller and a linear homeomorphic model of the oculomotor plant were integrated.

Main Results:

The model successfully simulates neural activity across premotor and motor stages of saccade generation.
It provides insights into the dynamics of horizontal saccades.
This represents a novel, integrated approach to modeling saccadic eye movements.

Conclusions:

This study presents the first comprehensive model of neural circuits for saccadic eye movements, integrating premotor and motor stages.
The model offers a valuable tool for understanding saccade control and dynamics.
It lays the groundwork for future research into oculomotor disorders.