Spatial cognition in a virtual reality home-cage extension for freely moving rodents.

Virtual reality (VR) environments are a powerful tool to investigate brain mechanisms involved in the behavior of animals. With this technique, animals are usually head fixed or secured in a harness, and training for cognitively more complex VR paradigms is time consuming. A VR apparatus allowing free animal movement and the constant operator-independent training of tasks would enable many new applications. Key prospective usages include brain imaging of animal behavior when carrying a miniaturized mobile device such as a fluorescence microscope or an optetrode. Here, we introduce the Servoball, a spherical VR treadmill based on the closed-loop tracking of a freely moving animal and feedback counterrotation of the ball. Furthermore, we present the complete integration of this experimental system with the animals' group home cage, from which single individuals can voluntarily enter through a tunnel with radio-frequency identification (RFID)-automated access control and commence experiments. This automated animal sorter functions as a mechanical replacement of the experimenter. We automatically trained rats using visual or acoustic cues to solve spatial cognitive tasks and recorded spatially modulated entorhinal cells. When electrophysiological extracellular recordings from awake behaving rats were performed, head fixation can dramatically alter results, so that any complex behavior that requires head movement is impossible to achieve. We circumvented this problem with the use of the Servoball in open-field scenarios, as it allows the combination of open-field behavior with the recording of nerve cells, along with all the flexibility that a virtual environment brings. This integrated home cage with a VR arena experimental system permits highly efficient experimentation for complex cognitive experiments.NEW & NOTEWORTHY Virtual reality (VR) environments are a powerful tool for the investigation of brain mechanisms. We introduce the Servoball, a VR treadmill for freely moving rodents. The Servoball is integrated with the animals' group home cage. Single individuals voluntarily enter using automated access control. Training is highly time-efficient, even for cognitively complex VR paradigms.

KineWheel-DeepLabCut Automated Paw Annotation Using Alternating Stroboscopic UV and White Light Illumination.

Uncovering the relationships between neural circuits, behavior, and neural dysfunction may require rodent pose tracking. While open-source toolkits such as DeepLabCut have revolutionized markerless pose estimation using deep neural networks, the training process still requires human intervention for annotating key points of interest in video data. To further reduce human labor for neural network training, we developed a method that automatically generates annotated image datasets of rodent paw placement in a laboratory setting. It uses invisible but fluorescent markers that become temporarily visible under UV light. Through stroboscopic alternating illumination, adjacent video frames taken at 720 Hz are either UV or white light illuminated. After color filtering the UV-exposed video frames, the UV markings are identified and the paw locations are deterministically mapped. This paw information is then transferred to automatically annotate paw positions in the next white light-exposed frame that is later used for training the neural network. We demonstrate the effectiveness of our method using a KineWheel-DeepLabCut setup for the markerless tracking of the four paws of a harness-fixed mouse running on top of the transparent wheel with mirror. Our automated approach, made available open-source, achieves high-quality position annotations and significantly reduces the need for human involvement in the neural network training process, paving the way for more efficient and streamlined rodent pose tracking in neuroscience research.