Nguyen Dieu My T, Roper Mark, Mircic Stanislav, Olberg Robert M, Gage Gregory J
Backyard Brains, Ann Arbor, MI 48104.
Neuroscience & Cognitive Science, University of Arizona, Tucson, AZ 85721.
J Undergrad Neurosci Educ. 2017 Jun 15;15(2):A162-A173. eCollection 2017 Spring.
Avoiding capture from a fast-approaching predator is an important survival skill shared by many animals. Investigating the neural circuits that give rise to this escape behavior can provide a tractable demonstration of systems-level neuroscience research for undergraduate laboratories. In this paper, we describe three related hands-on exercises using the grasshopper and affordable technology to bring neurophysiology, neuroethology, and neural computation to life and enhance student understanding and interest. We simplified a looming stimuli procedure using the Backyard Brains SpikerBox bioamplifier, an open-source and low-cost electrophysiology rig, to extracellularly record activity of the descending contralateral movement detector (DCMD) neuron from the grasshopper's neck. The DCMD activity underlies the grasshopper's motor responses to looming monocular visual cues and can easily be recorded and analyzed on an open-source iOS oscilloscope app, Spike Recorder. Visual stimuli are presented to the grasshopper by this same mobile application allowing for synchronized recording of stimuli and neural activity. An in-app spike-sorting algorithm is described that allows a quick way for students to record, sort, and analyze their data at the bench. We also describe a way for students to export these data to other analysis tools. With the protocol described, students will be able to prepare the grasshopper, find and record from the DCMD neuron, and visualize the DCMD responses to quantitatively investigate the escape system by adjusting the speed and size of simulated approaching objects. We describe the results from 22 grasshoppers, where 50 of the 57 recording sessions (87.7%) had a reliable DCMD response. Finally, we field-tested our experiment in an undergraduate neuroscience laboratory and found that a majority of students (67%) could perform this exercise in one two-hour lab setting, and had an increase in interest for studying the neural systems that drive behavior.
躲避快速逼近的捕食者是许多动物共有的一项重要生存技能。研究引发这种逃避行为的神经回路可为本科实验室的系统级神经科学研究提供一个易于操作的示范。在本文中，我们描述了三个相关的实践练习，利用蚱蜢和经济实惠的技术，将神经生理学、神经行为学和神经计算生动呈现出来，增强学生的理解和兴趣。我们使用Backyard Brains SpikerBox生物放大器简化了一种逼近刺激程序，该生物放大器是一种开源且低成本的电生理设备，用于从蚱蜢颈部细胞外记录下行对侧运动探测器（DCMD）神经元的活动。DCMD活动是蚱蜢对逼近的单眼视觉线索产生运动反应的基础，并且可以很容易地在开源iOS示波器应用程序Spike Recorder上进行记录和分析。通过同一个移动应用程序向蚱蜢呈现视觉刺激，从而实现刺激和神经活动的同步记录。文中描述了一种应用内尖峰分类算法，使学生能够在实验台上快速记录、分类和分析他们的数据。我们还描述了一种让学生将这些数据导出到其他分析工具的方法。通过所描述的方案，学生将能够准备蚱蜢，找到并记录DCMD神经元，以及通过调整模拟逼近物体的速度和大小来可视化DCMD反应，从而定量研究逃避系统。我们描述了22只蚱蜢的实验结果，在57次记录中，有50次（87.7%）获得了可靠的DCMD反应。最后，我们在本科神经科学实验室对我们的实验进行了实地测试，发现大多数学生（67%）能够在一个两小时的实验课上完成这个练习，并且对研究驱动行为的神经系统的兴趣有所增加。
