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Using Saccadometry with Deep Brain Stimulation to Study Normal and Pathological Brain Function
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Modelling 3D saccade generation by feedforward optimal control.

Akhil John1, Carlos Aleluia1, A John Van Opstal2

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Summary
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The human eye

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Area of Science:

  • Computational Neuroscience
  • Ocular Motor Control
  • Robotics

Background:

  • The human saccadic eye-movement system faces the degrees-of-freedom problem, requiring three rotations but having only two degrees of freedom for gaze control.
  • Eye movements during saccades typically adhere to Listing's law (LL), where cyclotorsion is zero, and follow nonlinear main-sequence dynamics.

Purpose of the Study:

  • To explain the emergence of Listing's law and saccade main-sequence dynamics using a computational model.
  • To investigate how optimal control principles applied to a simplified eye model can reproduce observed saccadic eye movement properties.

Main Methods:

  • Developed a 3-degree-of-freedom robotic eye model with antagonistic muscle pairs and independent motor commands.
  • Employed numerical linearization and system identification for controller development.
  • Applied optimal control principles to the simulated model to reproduce saccadic behavior.

Main Results:

  • The optimal control model successfully reproduced Listing's law and the saccade main-sequence dynamics.
  • Identified four key terms in the cost functional: energy expenditure, movement duration, gaze accuracy, and static muscle force.

Conclusions:

  • Listing's law and saccade dynamics may emerge from a common mechanism optimizing speed-accuracy trade-offs.
  • Minimizing total muscle force during fixation is also a crucial factor in generating realistic saccadic eye movements.