Abstract
Our goal was to develop an automated system to determine whether animals have learned and changed their behavior in real-time using a low calculation-power central processing unit (CPU). The bottleneck of real-time analysis is the speed of image recognition. For fast image recognition, 99.5% of the image was excluded from image recognition by distinguishing between the subject and thebackground. We achieved this by applying a binarization and connected-component labeling technique. This task is important for developing a fully automated learning apparatus. The use of such an automated system can improve the efficiency and accuracy of biological studies. The pond snail Lymnaea stagnails can be classically conditioned to avoid food that naturally elicits feeding behavior, and to consolidate this aversion into long-term memory. Determining memory status in the snail requires real-time analysis of the number of bites the snail makes in response to food presentation. The main algorithm for counting bites comprises two parts: extracting the mouth images from the recorded video and measuring the bite rate corresponding to the memory status. Reinforcement-supervised learning and image recognition were used to extract the mouth images. A change in the size of the mouth area was used as the cue for counting the number of bites. The accuracy of the final judgment of whether or not the snail had learned was the same as that determined by human observation. This method to improve the processing speed of image recognition has the potential for broad application beyond biological fields.
Highlights
I N BIOLOGICAL research, observation and evaluation of animal behaviors are fundamental experimental techniques.Manuscript received July 2, 2019; accepted July 31, 2019
An automated learning apparatus for snail conditioned taste aversion (CTA) was recently developed, the analysis itself still requires manual judgements made by the experimenter [16]
We present a fully automated learning apparatus that covers all stages of learning from execution of the conditioning to assessment of the animal memory status on the basis of feeding behavior
Summary
I N BIOLOGICAL research, observation and evaluation of animal behaviors are fundamental experimental techniques. Several automated devices using image recognition technology are commercially available for studying learning and memory in rodents Many of these devices, require expensive high-precision computers with 3D video tracking systems, which is a limiting factor for researchers in institutes and laboratories with a restricted budget [1], [2]. Manual conditioning of a large number of snails is laborious and time-consuming for the experimenter It is difficult, if not impossible, to analyze multiple parameters simultaneously by human observation. An automated learning apparatus for snail CTA was recently developed, the analysis itself still requires manual judgements made by the experimenter [16]. We present a fully automated learning apparatus that covers all stages of learning from execution of the conditioning to assessment of the animal memory status on the basis of feeding behavior.
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