Air-Track: a real-world floating environment for active sensing in head-fixed mice.

Abstract

Natural behavior occurs in multiple sensory and motor modalities and in particular is dependent on sensory feedback that constantly adjusts behavior. To investigate the underlying neuronal correlates of natural behavior, it is useful to have access to state-of-the-art recording equipment (e.g., 2-photon imaging, patch recordings, etc.) that frequently requires head fixation. This limitation has been addressed with various approaches such as virtual reality/air ball or treadmill systems. However, achieving multimodal realistic behavior in these systems can be challenging. These systems are often also complex and expensive to implement. Here we present "Air-Track," an easy-to-build head-fixed behavioral environment that requires only minimal computational processing. The Air-Track is a lightweight physical maze floating on an air table that has all the properties of the "real" world, including multiple sensory modalities tightly coupled to motor actions. To test this system, we trained mice in Go/No-Go and two-alternative forced choice tasks in a plus maze. Mice chose lanes and discriminated apertures or textures by moving the Air-Track back and forth and rotating it around themselves. Mice rapidly adapted to moving the track and used visual, auditory, and tactile cues to guide them in performing the tasks. A custom-controlled camera system monitored animal location and generated data that could be used to calculate reaction times in the visual and somatosensory discrimination tasks. We conclude that the Air-Track system is ideal for eliciting natural behavior in concert with virtually any system for monitoring or manipulating brain activity.

DOI: 10.1152/jn.00088.2016

Cite this paper

@article{Nashaat2016AirTrackAR, title={Air-Track: a real-world floating environment for active sensing in head-fixed mice.}, author={Mostafa A Nashaat and Hatem Oraby and Robert N S Sachdev and York Winter and Matthew E. Larkum}, journal={Journal of neurophysiology}, year={2016}, volume={116 4}, pages={1542-1553} }