Mazzoni Alberto, Garcia-Perez Elizabeth, Zoccolan Davide, Graziosi Sergio, Torre Vincent
SISSA, Via Beirut 2, 34014 Trieste, Italy.
J Neurophysiol. 2005 Jan;93(1):580-93. doi: 10.1152/jn.00608.2004. Epub 2004 Aug 18.
This paper describes an automatic system for the analysis and classification of leech behavior. Three colored beads were attached to the dorsal side of a free moving or pinned leech, and color CCD camera images were taken of the animal. The leech was restrained to moving in a small tank or petri dish, where the water level can be varied. An automatic system based on color processing tracked the colored beads over time, allowing real-time monitoring of the leech motion for several hours. At the end of each experimental session, six time series (2 for each bead) describing the leech body motion were obtained. A statistical analysis based on the speed and frequency content of bead motion indicated the existence of several stereotypical patterns of motion, corresponding to different leech behaviors. The identified patterns corresponded to swimming, pseudo-swimming, crawling, exploratory behavior, stationary states, abrupt movements, and combinations of these behaviors. The automatic characterization of leech behavior demonstrated here represents an important step toward understanding leech behavior and its properties. This method can be used to characterize the behavior of other invertebrates and also for some small vertebrates.
本文描述了一种用于分析和分类水蛭行为的自动系统。将三颗彩色珠子附着在自由移动或固定的水蛭背侧，并用彩色CCD相机拍摄该动物的图像。水蛭被限制在一个小水箱或培养皿中移动，水箱或培养皿中的水位可以变化。基于颜色处理的自动系统会随时间跟踪彩色珠子，从而能够对水蛭的运动进行数小时的实时监测。在每个实验环节结束时，会获得六个描述水蛭身体运动的时间序列（每个珠子两个）。基于珠子运动的速度和频率成分的统计分析表明，存在几种典型的运动模式，分别对应不同的水蛭行为。识别出的模式包括游泳、伪游泳、爬行、探索行为、静止状态、突然运动以及这些行为的组合。此处展示的水蛭行为自动表征是朝着理解水蛭行为及其特性迈出的重要一步。该方法可用于表征其他无脊椎动物以及一些小型脊椎动物的行为。
