Representation learning of animal behavior

Over the past decade, several methods have been developed that allow high-throughput automated quantification of animal behavior. Advances in computer vision make it possible to automatically track multiple body points. And continuous movements can be decomposed into a sequence of meaningful elementary units. In this project, we aim to build a latent variable model of a large dataset of zebrafish larva behavior. The behavior of each larva consists of a sequence of stereotypical tail movements. The model will be trained to perform prediction of future action. Once the model is trained we will explore transfer learning by using the representation from the model to detect the effect of drug treatment. For this, we will use a dataset of the larva behavior in response to 10 pharmacological compounds at different concentrations. Our goal is to learn the internal state of the animal using this approach, which could be useful for studying the brain and improving the detection of drug-induced behavioral changes. Our approach holds promise for neuroscience and preclinical research, as careful measurements of animal behavior have proven to be an important complement to modern techniques for recording and manipulating neural circuits. Marques, J.C., Lackner, S., Félix, R. and Orger, M.B., 2018. Structure of the zebrafish locomotor repertoire revealed with unsupervised behavioral clustering. Current Biology, 28(2), pp.181-195. // Wiltschko, A.B., Tsukahara, T., Zeine, A., Anyoha, R., Gillis, W.F., Markowitz, J.E., Peterson, R.E., Katon, J., Johnson, M.J. and Datta, S.R., 2020. Revealing the structure of pharmacobehavioral space through motion sequencing. Nature neuroscience, 23(11), pp.1433-1443. // Oord, A.V.D., Li, Y. and Vinyals, O., 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748.