Guillaume Doucet1, Roberto A Gulli2, Julio C Martinez-Trujillo3. 1. Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, QC H3G 1Y6, Canada; Robarts Research Institute, Brain and Mind Institute, Department of Physiology and Pharmacology, Department of Psychiatry, Western University, 1151 Richmond St. N., Room 7239, London, ON N6A 5B7, Canada. Electronic address: guillaume.doucet2@mail.mcgill.ca. 2. Integrated Program in Neuroscience, McGill University, 3801 University Street, Room 141, Montreal, QC H3A 2B4, Canada; Robarts Research Institute, Brain and Mind Institute, Department of Physiology and Pharmacology, Department of Psychiatry, Western University, 1151 Richmond St. N., Room 7239, London, ON N6A 5B7, Canada. Electronic address: roberto.gulli@mail.mcgill.ca. 3. Robarts Research Institute, Brain and Mind Institute, Department of Physiology and Pharmacology, Department of Psychiatry, Western University, 1151 Richmond St. N., Room 7239, London, ON N6A 5B7, Canada. Electronic address: julio.martinez@robarts.ca.
Abstract
BACKGROUND: Although simplified visual stimuli, such as dots or gratings presented on homogeneous backgrounds, provide strict control over the stimulus parameters during visual experiments, they fail to approximate visual stimulation in natural conditions. Adoption of virtual reality (VR) in neuroscience research has been proposed to circumvent this problem, by combining strict control of experimental variables and behavioral monitoring within complex and realistic environments. NEW METHOD: We have created a VR toolbox that maximizes experimental flexibility while minimizing implementation costs. A free VR engine (Unreal 3) has been customized to interface with any control software via text commands, allowing seamless introduction into pre-existing laboratory data acquisition frameworks. Furthermore, control functions are provided for the two most common programming languages used in visual neuroscience: Matlab and Python. RESULTS: The toolbox offers milliseconds time resolution necessary for electrophysiological recordings and is flexible enough to support cross-species usage across a wide range of paradigms. COMPARISON WITH EXISTING METHODS: Unlike previously proposed VR solutions whose implementation is complex and time-consuming, our toolbox requires minimal customization or technical expertise to interface with pre-existing data acquisition frameworks as it relies on already familiar programming environments. Moreover, as it is compatible with a variety of display and input devices, identical VR testing paradigms can be used across species, from rodents to humans. CONCLUSIONS: This toolbox facilitates the addition of VR capabilities to any laboratory without perturbing pre-existing data acquisition frameworks, or requiring any major hardware changes.
BACKGROUND: Although simplified visual stimuli, such as dots or gratings presented on homogeneous backgrounds, provide strict control over the stimulus parameters during visual experiments, they fail to approximate visual stimulation in natural conditions. Adoption of virtual reality (VR) in neuroscience research has been proposed to circumvent this problem, by combining strict control of experimental variables and behavioral monitoring within complex and realistic environments. NEW METHOD: We have created a VR toolbox that maximizes experimental flexibility while minimizing implementation costs. A free VR engine (Unreal 3) has been customized to interface with any control software via text commands, allowing seamless introduction into pre-existing laboratory data acquisition frameworks. Furthermore, control functions are provided for the two most common programming languages used in visual neuroscience: Matlab and Python. RESULTS: The toolbox offers milliseconds time resolution necessary for electrophysiological recordings and is flexible enough to support cross-species usage across a wide range of paradigms. COMPARISON WITH EXISTING METHODS: Unlike previously proposed VR solutions whose implementation is complex and time-consuming, our toolbox requires minimal customization or technical expertise to interface with pre-existing data acquisition frameworks as it relies on already familiar programming environments. Moreover, as it is compatible with a variety of display and input devices, identical VR testing paradigms can be used across species, from rodents to humans. CONCLUSIONS: This toolbox facilitates the addition of VR capabilities to any laboratory without perturbing pre-existing data acquisition frameworks, or requiring any major hardware changes.
Authors: Roberto A Gulli; Lyndon R Duong; Benjamin W Corrigan; Guillaume Doucet; Sylvain Williams; Stefano Fusi; Julio C Martinez-Trujillo Journal: Nat Neurosci Date: 2019-12-23 Impact factor: 24.884