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  • Associate Professor
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  • Technician
  • Undergraduate Student
  • Veterinary
  • Visiting Scientist
  • Deputy Director of Center
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  • Deputy Head of Facility
  • Director of Center
  • Director of Department
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About

Many behaviors are physically mutually exclusive and therefore cannot occur simultaneously. Competitive interactions must exist that ensure that when one behavior is selected other, competing behavior are being suppressed. The outcome of these competitive interactions can be influenced by different factors so different behaviors can be selected depending on the situation or animal’s state. This allows the animal to do right thing given the circumstances, which is essential to ensure survival. The neural circuit mechanisms that ensure this flexible selection of behaviors are known with cellular and synaptic resolution. This is due to the difficulty in establishing causal relationships between single neurons and behaviors and mapping synaptic connectivity across the brain in big and complex brains with many neurons and many connections. To overcome these difficulties, we take advantage of Drosophila larva. It has a compact brain and a volume of electron microscopy (EM) images of the entire nervous system where circuits can be mapped with synaptic resolution while the powerful genetic tools and rapid reproductive cycle make it possible to manipulate neuronal activity in a cell-type specific manner and quickly detect behavioral changes. Using these approaches combined with a machine learning behavioral detection method that allows automated detection of larval actions, we mapped the neural circuit pathways underlying the five different actions that occur in response to a mechanical stimulus. We identify neurons and circuit pathways that could underlie the competitive interaction between the five behaviors. Our findings suggest that the selection of behaviors occurs through a distributed process involving different regions of the nervous system.

This project has received funding from the European Union’s Horizon 2020 research and innovationprogramme under the Marie Sklodowska-Curie grant agreement No 798050

 

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