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1. #HAMSTER MAZE WITH MULTIPLE WAYS OUT MANUAL#
2. #HAMSTER MAZE WITH MULTIPLE WAYS OUT SOFTWARE#
3. #HAMSTER MAZE WITH MULTIPLE WAYS OUT FREE#

Here, we have developed video tracking software to quantify locomotor behaviors, based on the widely available programming language Python and the OpenCV set of open source computer vision tools. For example, Pittman and Ishikawa (2013) employed smartphone applications originally designed for slow-motion analysis of golf club swings and other sports movements to quantify locomotor behaviors in zebrafish. Similarly, technologies already in widespread use have been creatively repurposed to quantify animal locomotion. To overcome these limitations, video tracking tools have been developed that use freely available software and inexpensive video equipment ( Togasaki et al., 2005 Ramazani et al., 2007 Aguiar et al., 2007). In addition, customization of these systems often requires hardware modifications and additional software that can increase costs further. Though commercial systems exist for video tracking of locomotor behaviors, the significant costs of the hardware and software used in these systems limit their broad applicability.

#HAMSTER MAZE WITH MULTIPLE WAYS OUT MANUAL#

These characteristics eliminate potential observer biases and enable quantification of behaviors that might be missed via manual observation, such as rapid movements, rare behaviors, or patterns of locomotion that emerge over extended periods of time ( Noldus et al., 2001). Furthermore, video-based analyses enable high sampling frequency, high spatial resolution, and long periods of data collection. Automated assays of animal movement enable simultaneous tracking of multiple animals and objective quantification of locomotor behaviors. Locomotion can be quantified via direct observation, but these assays are time- and labor-intensive, require extensive training, and can be confounded by subjective differences between observers, and drift due to observer fatigue. For example, locomotor output is a robustly quantifiable behavior that is sensitive to genetic and pharmacological perturbations of nervous system activity and to differences in sensory input ( Rothenfluh and Heberlein, 2002 Lebestky et al., 2009 Rihel et al., 2010 Swierczek et al., 2011 Woods et al., 2014). The quantification of behavior is a powerful, non-invasive method for studying nervous system function, as differences in behavior arise from differences in neural activity.

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Moreover, our system presents a free alternative to commercial video-tracking systems and is thus broadly applicable to a wide variety of educational settings and research programs.

The open source nature of our software enables flexibility, customization, and scalability in behavioral analyses. We applied the software to an analysis of the effects of ethanol on thigmotaxis (wall-hugging) behavior on adult zebrafish, and found that acute ethanol treatment decreased thigmotaxis behaviors without affecting overall amounts of motion. These tests highlight the capacity of our software for long-term data acquisition, parallel analysis of multiple animals, and application to animal species of different sizes and movement patterns. We validated this software through four tests on a variety of animal species, including larval and adult zebrafish ( Danio rerio), Siberian dwarf hamsters ( Phodopus sungorus), and wild birds. Here, we present a new video-tracking system built on Python-based software that is free, open source, and cross-platform, and that can analyze video input from widely available video capture devices such as smartphone cameras and webcams. Automated tracking of animal movement is less labor-intensive and bias-prone than direct observation, and allows for simultaneous analysis of multiple animals, high spatial and temporal resolution, and data collection over extended periods of time.

Measurements of animal locomotion enable the quantification of these differences. Differences in nervous system function can result in differences in behavioral output.