EyeLoop

EyeLoop performs real-time eye-tracking and closed-loop experimental control using Python 3 on consumer-grade hardware to enable dynamic visual-stimulus feedback and optogenetic stimulation experiments.

Key Features:

• Open-source: EyeLoop is distributed as open-source software implemented in Python 3.
• Real-time closed-loop control: The system supports dynamic experiments where eye position can influence stimulus feedback in real time, enabling closed-loop and open-loop paradigms.
• Modular logic framework: EyeLoop provides a modular design that allows integration of custom functions and experimental modules.
• Multi-species tracking: The software can track eyes from multiple species, including rodents, humans, and non-human primates.
• Integration with consumer-grade hardware: EyeLoop operates on consumer-grade hardware to perform eye tracking and experimental control.
• Support for optogenetic experiments: The platform can be used to implement rapid neuronal optogenetic stimulation driven by real-time eye measurements.
• Low hardware cost estimate: The minimal hardware setup is reported at approximately 29 USD.

Scientific Applications:

• Neuroscience experiments: EyeLoop facilitates studies of brain dynamics and neural circuit function using open-loop and closed-loop stimulus control and optogenetic stimulation.
• Clinical research: The system can be applied in clinical settings to investigate neuropathology and disease models via quantitative eye-movement analysis.
• Comparative and behavioral studies: Multi-species tracking enables comparative investigations of eye movements and behavior across rodents, humans, and non-human primates.

Methodology:

Implemented in Python 3 with a modular logic framework, EyeLoop integrates with consumer-grade hardware to perform real-time open-loop and closed-loop eye-tracking and to drive rapid neuronal optogenetic stimulation.