BEGIN:VCALENDAR VERSION:2.0 PRODID:-//linuxsoftware.nz//NONSGML Joyous v1.4//EN BEGIN:VEVENT SUMMARY:How does the brain control the eye movements ? An analysis-by-synt hesis approach DTSTART:20241119T150000Z DTEND:20241119T170000Z DTSTAMP:20260810T234624Z UID:f631adba-1f05-4cb6-a4db-6e64792ad8a1 SEQUENCE:1 CREATED:20241118T110459Z DESCRIPTION:Despite extensive research on eye movements\, the underlying m echanisms by which the brain controls these motions are not yet well under stood. Biomimetic models of biological systems have provided valuable insi ghts into their function and control. This work aims to develop an artific ial model of the human eye system to explore how the brain controls saccad ic movements. It is hypothesised that the control of saccadic movement ari ses from an optimisation process to the control inputs\, where metrics suc h as accuracy\, duration\, energy\, and tension are used to evaluate the p erformance of the resulting motion. To this end\, a model-free algorithm b ased on reinforcement learning is employed to learn\, through trial-and-er ror\, how to control the system in an open-loop manner\, using biologicall y inspired input signals. A biologically accurate\, six-degree-of-freedom computational model of the human eye is used to analyse how different inpu ts affect the resulting ocular motion. The results derived from this formu lation show that the learnt controls successfully replicate human-like sac cadic characteristics\, including the main sequence relationships\, the co mpliance with Listing'\;s law\, and the antagonist pairing of extraocu lar muscles - without directly enforcing these behaviours. Additionally\, this formulation was used to analyse the impact of the reward function fac tors\, as well as the introduction of signal-dependent and additive noise\ , on the resulting saccadic control strategies and resulting motions. LAST-MODIFIED:20241118T110459Z LOCATION:Room V0.08\, Floor 0 - Civil Pavilion URL:http://df.vps.tecnico.ulisboa.pt/en/events/how-does-the-brain-control- the-eye-movements-an-analysis-by-synthesis-approach/ X-ALT-DESC;FMTTYPE=text/html:

Despite extensive r esearch on eye movements\, the underlying mechanisms by which the brain co ntrols these motions are not yet well understood. Biomimetic models of bio logical systems have provided valuable insights into their function and co ntrol. This work aims to develop an artificial model of the human eye syst em to explore how the brain controls saccadic movements.

It is h ypothesised that the control of saccadic movement arises from an optimisat ion process to the control inputs\, where metrics such as accuracy\, durat ion\, energy\, and tension are used to evaluate the performance of the res ulting motion. To this end\, a model-free algorithm based on reinforcement learning is employed to learn\, through trial-and-error\, how to control the system in an open-loop manner\, using biologically inspired input sign als.

A biologically accurate\, six-degree-of-freedom computation al model of the human eye is used to analyse how different inputs affect t he resulting ocular motion. The results derived from this formulation show that the learnt controls successfully replicate human-like saccadic chara cteristics\, including the main sequence relationships\, the compliance wi th Listing'\;s law\, and the antagonist pairing of extraocular muscles - without directly enforcing these behaviours.

Additionally\, t his formulation was used to analyse the impact of the reward function fact ors\, as well as the introduction of signal-dependent and additive noise\, on the resulting saccadic control strategies and resulting motions.

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