Using computational tools for revealing the neural basis of behavior in Drosophila
Understanding the neural basis of behavior is a major challenge in modern neuroscience. Drosophila melanogaster has long been served as an excellent model system for revealing the neural basis of behavior, due to the relative simplicity of this system, and the wide range of available genetic tools in this model. Recent advances in the field of machine learning are accelerating our understanding of the neural basis of social behavior in flies in two major directions. First, pose estimation is used for fine tracking of body parts of single or interacting flies, followed up by supervised and unsupervised algorithms for detailed and automated quantification of fly behaviors. Second, neural networks are being used for automatic detection of neurons in sub-micron resolution scans of the entire fly brain, therefore accelerating the process of building a fly ‘connectome’ – a description of all the connections between cells in adult or developing fly brains.
In my talk, I will first give some background on the use of machine learning based tools for studying the neural basis of social behaviors in flies. I will then describe two projects I completed during my post-doc, focusing on two aspects of social behaviors in flies: (1) sexual dimorphism in the circuits controlling social behaviors, and (2) the role of ‘internal’ brain states in controlling social decisions.
Last, I will describe some future directions of my recently established lab at the Neurobiology department, University of Haifa. Among them are deciphering the neural basis of social communication in complex, natural-like environments, and using the fly connectome for revealing principals regarding how the structure and connectivity of cells is related to their function.
David (Dudi) Deutsch received his B.Sc in electrical engineering from Tel-Aviv University in 2005. His M.Sc (direct path for outstanding students) was joint between Tel-Aviv university and the Weizmann Institute, focusing on electrostatic properties of adsorbed polar molecules. Following one year of travel in South America, he decided to move his scientific focus to understanding the mysteries of the brain. He joined the Neurobiology department at the Weizmann Institute of Science, where he did his Ph.D. under the supervision of Prof. Ehud Ahissar and Prof. Elad Schneidman. He studied how brains are actively controlling the flow of information that they collect from their environment (‘active sensing’), using the rat whisker system as a model.
Transitioning to his post-doctoral studies, he switched to studying the fruit fly Drosophila melanogaster, taking advantage of the tractability of the system, and the available genetic tools. In 2014 he joined the Murthy lab at the Princeton Neuroscience Institute. He studied social communication in males and females, focusing on two major questions: (1) What are the shared and sexually dimorphic circuits for the processing of courtship song in the male and female brains, and (2) How do internal motivational states modulate social behavior. Dudi opens his lab in the Neurobiology department at the University of Haifa in 2022, where he will study the neural basis of social communication.
In this talk I will present the problem of facial landmarks detection on animals, a topic that has not received much attention in the field of computer vision, despite the mathematical interest of the problem itself and its numerous applications in the field of animal emotions recognition and well-being. I will provide an overview of the challenges in detecting landmarks on animals, and contrast the use of computer vision for humans and animals. Then, I will delve into the process of cascade detection of landmarks, using the example of cats to demonstrate how this technique can be applied to animals. Additionally, I will discuss the potential applications of this technology in fields such as animal behavior research and veterinary medicine.
I am a PhD candidate studying computer vision and animal-affective computing. The focus of my current research is to develop solutions for the detection of animal faces and facial landmarks for the classification of their internal state and emotions. I obtained my BSc and MSc in applied mathematics and physics from Moscow Institute of Physics and Technology and last year I started working on a PhD thesis at the University of Haifa.