Insights and lessons learnt
Organisers: Lars Lewejohann (German Center for the Protection of Laboratory Animals, FU-Berlin, Germany) and Lior Bikovski (Tel-Aviv University, Israel)
Automated home cage monitoring (HCM) systems are promising tools to improve reproducible behavioral phenotyping. Collecting data from the animals’ activity in their familiar environment without disturbance by an experimenter may greatly improve animal welfare while also allowing the simultaneous collection of long-term data from many animals, thus promoting in depth analyses of behavioral patterns. The variety of HCM systems that are available today may leave us spoiled for choice, nevertheless, each of these systems comes with a different set of challenges.
Within the COST Action 20135, “TEATIME”, framework, we have been discussing the effects of HCM on bio-behavioral science and animal welfare and curating the different systems available today (Link). In this session, we will share our experiences with different HCM, commercial and non-commercial. In addition, we will discuss these systems’ advantages and disadvantages from a user perspective while discussing future directions that that may include the application of machine learning in both data acquisition and novel analyses methods.