Nashaat Mostafa A, Oraby Hatem, Sachdev Robert N S, Winter York, Larkum Matthew E
Neurocure Cluster of Excellence, Humboldt-Universität zu Berlin, Berlin, Germany; and Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany.
Neurocure Cluster of Excellence, Humboldt-Universität zu Berlin, Berlin, Germany; and.
J Neurophysiol. 2016 Oct 1;116(4):1542-1553. doi: 10.1152/jn.00088.2016. Epub 2016 Jul 13.
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.
自然行为通过多种感觉和运动方式发生,尤其依赖于不断调整行为的感觉反馈。为了研究自然行为背后的神经元关联,能够使用最先进的记录设备(例如双光子成像、膜片钳记录等)是很有用的,而这些设备常常需要头部固定。这一限制已经通过各种方法得以解决,比如虚拟现实/气悬浮球或跑步机系统。然而,在这些系统中实现多模态的真实行为可能具有挑战性。这些系统通常也很复杂且实施成本高昂。在此,我们展示“空气轨道”,这是一种易于构建的头部固定行为环境,只需要最少的计算处理。空气轨道是一个漂浮在气垫桌上的轻质物理迷宫,它具备“真实”世界的所有特性,包括与运动动作紧密耦合的多种感觉模态。为了测试这个系统,我们在十字迷宫中对小鼠进行了“是/否”和二选一强制选择任务的训练。小鼠通过前后移动空气轨道并绕自身旋转来选择通道、辨别小孔或纹理。小鼠迅速适应了移动轨道,并利用视觉、听觉和触觉线索来指导它们完成任务。一个定制控制的摄像系统监测动物的位置,并生成可用于计算视觉和体感辨别任务中反应时间的数据。我们得出结论,空气轨道系统与几乎任何用于监测或操纵大脑活动的系统协同使用时,对于引发自然行为而言是理想的。